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class="nn">transformers.utils.versions</span> <span class="kn">import</span> <span class="n">require_version</span> + +<span class="kn">from</span> <span class="nn">lmflow</span> <span class="kn">import</span> <span class="n">args</span><span class="p">,</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">pipeline</span><span class="p">,</span> <span class="n">utils</span> + +<span class="c1"># Will error if the minimal version of Transformers is not installed. Remove at your own risks.</span> +<span class="n">check_min_version</span><span class="p">(</span><span class="s2">&quot;4.27.0.dev0&quot;</span><span class="p">)</span> + +<span class="n">require_version</span><span class="p">(</span><span class="s2">&quot;datasets&gt;=1.8.0&quot;</span><span class="p">,</span> <span class="s2">&quot;To fix: pip install -r examples/pytorch/language-modeling/requirements.txt&quot;</span><span class="p">)</span> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script 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+</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.args</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;This script defines dataclasses: ModelArguments and DatasetArguments,</span> +<span class="sd">that contain the arguments for the model and dataset used in training.</span> + +<span class="sd">It imports several modules, including dataclasses, field from typing, Optional from typing,</span> +<span class="sd">require_version from transformers.utils.versions, MODEL_FOR_CAUSAL_LM_MAPPING,</span> +<span class="sd">and TrainingArguments from transformers.</span> + +<span class="sd">MODEL_CONFIG_CLASSES is assigned a list of the model config classes from</span> +<span class="sd">MODEL_FOR_CAUSAL_LM_MAPPING. MODEL_TYPES is assigned a tuple of the model types</span> +<span class="sd">extracted from the MODEL_CONFIG_CLASSES.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span><span class="p">,</span> <span class="n">field</span> +<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">List</span> + +<span class="kn">from</span> <span class="nn">transformers.utils.versions</span> <span class="kn">import</span> <span class="n">require_version</span> + +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">MODEL_FOR_CAUSAL_LM_MAPPING</span><span class="p">,</span> + <span class="n">TrainingArguments</span><span class="p">,</span> +<span class="p">)</span> + +<div class="viewcode-block" id="MODEL_CONFIG_CLASSES"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.MODEL_CONFIG_CLASSES">[docs]</a><span class="n">MODEL_CONFIG_CLASSES</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">MODEL_FOR_CAUSAL_LM_MAPPING</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span></div> +<div class="viewcode-block" id="MODEL_TYPES"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.MODEL_TYPES">[docs]</a><span class="n">MODEL_TYPES</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">conf</span><span class="o">.</span><span class="n">model_type</span> <span class="k">for</span> <span class="n">conf</span> <span class="ow">in</span> <span class="n">MODEL_CONFIG_CLASSES</span><span class="p">)</span></div> + + +<span class="nd">@dataclass</span> +<div class="viewcode-block" id="ModelArguments"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments">[docs]</a><span class="k">class</span> <span class="nc">ModelArguments</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Define a class ModelArguments using the dataclass decorator. </span> +<span class="sd"> The class contains several optional parameters that can be used to configure a model. </span> +<span class="sd"> </span> +<span class="sd"> model_name_or_path : str</span> +<span class="sd"> a string representing the path or name of a pretrained</span> +<span class="sd"> model checkpoint for weights initialization. If None, a model will be trained from scratch.</span> + +<span class="sd"> model_type : str</span> +<span class="sd"> a string representing the type of model to use if training from</span> +<span class="sd"> scratch. If not provided, a pretrained model will be used.</span> +<span class="sd"> </span> +<span class="sd"> config_overrides : str</span> +<span class="sd"> a string representing the default config settings to override</span> +<span class="sd"> when training a model from scratch.</span> +<span class="sd"> </span> +<span class="sd"> config_name : str</span> +<span class="sd"> a string representing the name or path of the pretrained config to</span> +<span class="sd"> use, if different from the model_name_or_path.</span> +<span class="sd"> </span> +<span class="sd"> tokenizer_name : str</span> +<span class="sd"> a string representing the name or path of the pretrained tokenizer</span> +<span class="sd"> to use, if different from the model_name_or_path.</span> + +<span class="sd"> cache_dir : str</span> +<span class="sd"> a string representing the path to the directory where pretrained models</span> +<span class="sd"> downloaded from huggingface.co will be stored.</span> + +<span class="sd"> use_fast_tokenizer : bool</span> +<span class="sd"> a boolean indicating whether to use a fast tokenizer (backed by the</span> +<span class="sd"> tokenizers library) or not.</span> + +<span class="sd"> model_revision : str</span> +<span class="sd"> a string representing the specific model version to use (can be a</span> +<span class="sd"> branch name, tag name, or commit id).</span> + +<span class="sd"> use_auth_token : bool</span> +<span class="sd"> a boolean indicating whether to use the token generated when running</span> +<span class="sd"> huggingface-cli login (necessary to use this script with private models).</span> + +<span class="sd"> torch_dtype : str</span> +<span class="sd"> a string representing the dtype to load the model under. If auto is</span> +<span class="sd"> passed, the dtype will be automatically derived from the model&#39;s weights.</span> + +<span class="sd"> use_ram_optimized_load : bool</span> +<span class="sd"> a boolean indicating whether to use disk mapping when memory is not</span> +<span class="sd"> enough.</span> +<span class="sd"> use_int8 : bool</span> +<span class="sd"> a boolean indicating whether to load int8 quantization for inference.</span> +<span class="sd"> &quot;&quot;&quot;</span> + +<div class="viewcode-block" id="ModelArguments.model_name_or_path"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.model_name_or_path">[docs]</a> <span class="n">model_name_or_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;The model checkpoint for weights initialization.Don&#39;t set if you want to train a model from scratch.&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.lora_model_path"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_model_path">[docs]</a> <span class="n">lora_model_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;The incremental model diff introduced by LoRA finetuning.&quot;</span> + <span class="s2">&quot; Along with the original non-finetuned model forms the whole&quot;</span> + <span class="s2">&quot; finetuned model.&quot;</span> + <span class="p">)</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.model_type"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.model_type">[docs]</a> <span class="n">model_type</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;If training from scratch, pass a model type from the list: &quot;</span> <span class="o">+</span> <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">MODEL_TYPES</span><span class="p">)},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.arch_type"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.arch_type">[docs]</a> <span class="n">arch_type</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;decoder_only&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The architecture type of the model. Currently supported decoder_only or encoder_decoder&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.config_overrides"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.config_overrides">[docs]</a> <span class="n">config_overrides</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;Override some existing default config settings when a model is trained from scratch. Example: &quot;</span> + <span class="s2">&quot;n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> + <span class="n">arch_type</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;decoder_only&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;Model architecture type, e.g. </span><span class="se">\&quot;</span><span class="s2">decoder_only</span><span class="se">\&quot;</span><span class="s2">,&quot;</span> + <span class="s2">&quot; </span><span class="se">\&quot;</span><span class="s2">encoder_decoder</span><span class="se">\&quot;</span><span class="s2">&quot;</span> + <span class="p">),</span> + <span class="s2">&quot;choices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;decoder_only&quot;</span><span class="p">,</span> <span class="s2">&quot;encoder_decoder&quot;</span><span class="p">,</span> <span class="s2">&quot;text_regression&quot;</span><span class="p">,</span> <span class="s2">&quot;vision_encoder_decoder&quot;</span><span class="p">],</span> + <span class="p">},</span> + <span class="p">)</span> +<div class="viewcode-block" id="ModelArguments.config_name"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.config_name">[docs]</a> <span class="n">config_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Pretrained config name or path if not the same as model_name&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.tokenizer_name"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.tokenizer_name">[docs]</a> <span class="n">tokenizer_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Pretrained tokenizer name or path if not the same as model_name&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.cache_dir"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.cache_dir">[docs]</a> <span class="n">cache_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Where do you want to store the pretrained models downloaded from huggingface.co&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.use_fast_tokenizer"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_fast_tokenizer">[docs]</a> <span class="n">use_fast_tokenizer</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.model_revision"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.model_revision">[docs]</a> <span class="n">model_revision</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;main&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The specific model version to use (can be a branch name, tag name or commit id).&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.use_auth_token"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_auth_token">[docs]</a> <span class="n">use_auth_token</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;Will use the token generated when running `huggingface-cli login` (necessary to use this script &quot;</span> + <span class="s2">&quot;with private models).&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.torch_dtype"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.torch_dtype">[docs]</a> <span class="n">torch_dtype</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the &quot;</span> + <span class="s2">&quot;dtype will be automatically derived from the model&#39;s weights.&quot;</span> + <span class="p">),</span> + <span class="s2">&quot;choices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span> <span class="s2">&quot;bfloat16&quot;</span><span class="p">,</span> <span class="s2">&quot;float16&quot;</span><span class="p">,</span> <span class="s2">&quot;float32&quot;</span><span class="p">],</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.use_lora"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_lora">[docs]</a> <span class="n">use_lora</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Whether to lora.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.lora_r"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_r">[docs]</a> <span class="n">lora_r</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;the rank of the lora parameters. The smaller lora_r is , the fewer parameters lora has.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.lora_alpha"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_alpha">[docs]</a> <span class="n">lora_alpha</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Merging ratio between the fine-tuned model and the original. This is controlled by a parameter called alpha in the paper.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.lora_target_modules"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_target_modules">[docs]</a> <span class="n">lora_target_modules</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Pretrained config name or path if not the same as model_name&quot;</span><span class="p">,</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.lora_dropout"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_dropout">[docs]</a> <span class="n">lora_dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The dropout rate in lora.linear.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.save_aggregated_lora"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.save_aggregated_lora">[docs]</a> <span class="n">save_aggregated_lora</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Whether to save aggregated lora.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.use_ram_optimized_load"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_ram_optimized_load">[docs]</a> <span class="n">use_ram_optimized_load</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Whether use disk mapping when memory is not enough.&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.use_flash_attention"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_flash_attention">[docs]</a> <span class="n">use_flash_attention</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;whether use flash attention layer to reduce GPU memory with&quot;</span> + <span class="s2">&quot; higher time cost.&quot;</span> + <span class="p">)</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.truncate_to_model_max_length"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.truncate_to_model_max_length">[docs]</a> <span class="n">truncate_to_model_max_length</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;whether truncate the dataset to model max length.&quot;</span> + <span class="p">)</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.do_rope_scaling"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.do_rope_scaling">[docs]</a> <span class="n">do_rope_scaling</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;whether do ROPE scaling for llama model.&quot;</span> + <span class="s2">&quot;Linear_scaling credits to the Reddit user /u/kaiokendev.&quot;</span> + <span class="s2">&quot;https://arxiv.org/abs/2306.15595&quot;</span> + <span class="s2">&quot;NTK_scaling credits to the Reddit users /u/bloc97 and /u/emozilla.&quot;</span> + <span class="s2">&quot;https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/&quot;</span> + <span class="p">)</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.rope_pi_ratio"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.rope_pi_ratio">[docs]</a> <span class="n">rope_pi_ratio</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;the ratio of pi in RoPE scaling.&quot;</span> + <span class="p">)</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.rope_ntk_ratio"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.rope_ntk_ratio">[docs]</a> <span class="n">rope_ntk_ratio</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;the ratio of NTK in RoPE scaling.&quot;</span> + <span class="p">)</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="ModelArguments.use_int8"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_int8">[docs]</a> <span class="n">use_int8</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;whether to load int8 quantization for inference&quot;</span><span class="p">}</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="ModelArguments.__post_init__"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.__post_init__">[docs]</a> <span class="k">def</span> <span class="nf">__post_init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">config_overrides</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">config_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_name_or_path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;--config_overrides can&#39;t be used in combination with --config_name or --model_name_or_path&quot;</span> + <span class="p">)</span></div></div> + + +<span class="nd">@dataclass</span> +<div class="viewcode-block" id="VisModelArguments"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments">[docs]</a><span class="k">class</span> <span class="nc">VisModelArguments</span><span class="p">(</span><span class="n">ModelArguments</span><span class="p">):</span> +<div class="viewcode-block" id="VisModelArguments.low_resource"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.low_resource">[docs]</a> <span class="n">low_resource</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Use 8 bit and float16 when loading llm&quot;</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="VisModelArguments.custom_model"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.custom_model">[docs]</a> <span class="n">custom_model</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;flag for the model from huggingface or not&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="VisModelArguments.checkpoint_path"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.checkpoint_path">[docs]</a> <span class="n">checkpoint_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;path for model checkpoint&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="VisModelArguments.llm_model_name_or_path"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.llm_model_name_or_path">[docs]</a> <span class="n">llm_model_name_or_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;llm model in multi-modality model&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="VisModelArguments.use_prompt_cache"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.use_prompt_cache">[docs]</a> <span class="n">use_prompt_cache</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Whether to use prompt cache.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="VisModelArguments.prompt_cache_path"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.prompt_cache_path">[docs]</a> <span class="n">prompt_cache_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Path to prompt cache.&quot;</span><span class="p">},</span> + <span class="p">)</span></div></div> + + + +<span class="nd">@dataclass</span> +<div class="viewcode-block" id="DatasetArguments"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments">[docs]</a><span class="k">class</span> <span class="nc">DatasetArguments</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Define a class DatasetArguments using the dataclass decorator. </span> +<span class="sd"> The class contains several optional parameters that can be used to configure a dataset for a language model. </span> +<span class="sd"> </span> + +<span class="sd"> dataset_path : str</span> +<span class="sd"> a string representing the path of the dataset to use.</span> + +<span class="sd"> dataset_name : str</span> +<span class="sd"> a string representing the name of the dataset to use. The default value is &quot;customized&quot;.</span> + +<span class="sd"> is_custom_dataset : bool</span> +<span class="sd"> a boolean indicating whether to use custom data. The default value is False.</span> + +<span class="sd"> customized_cache_dir : str</span> +<span class="sd"> a string representing the path to the directory where customized dataset caches will be stored.</span> + +<span class="sd"> dataset_config_name : str</span> +<span class="sd"> a string representing the configuration name of the dataset to use (via the datasets library).</span> + +<span class="sd"> train_file : str</span> +<span class="sd"> a string representing the path to the input training data file (a text file).</span> + +<span class="sd"> validation_file : str</span> +<span class="sd"> a string representing the path to the input evaluation data file to evaluate the perplexity on (a text file).</span> + +<span class="sd"> max_train_samples : int</span> +<span class="sd"> an integer indicating the maximum number of training examples to use for debugging or quicker training. </span> +<span class="sd"> If set, the training dataset will be truncated to this number.</span> + +<span class="sd"> max_eval_samples: int</span> +<span class="sd"> an integer indicating the maximum number of evaluation examples to use for debugging or quicker training. </span> +<span class="sd"> If set, the evaluation dataset will be truncated to this number.</span> + +<span class="sd"> streaming : bool</span> +<span class="sd"> a boolean indicating whether to enable streaming mode.</span> + +<span class="sd"> block_size: int</span> +<span class="sd"> an integer indicating the optional input sequence length after tokenization. The training dataset will be </span> +<span class="sd"> truncated in blocks of this size for training.</span> + +<span class="sd"> The class also includes some additional parameters that can be used to configure the dataset further, such as `overwrite_cache`,</span> +<span class="sd"> `validation_split_percentage`, `preprocessing_num_workers`, `disable_group_texts`, `demo_example_in_prompt`, `explanation_in_prompt`,</span> +<span class="sd"> `keep_linebreaks`, and `prompt_structure`.</span> + +<span class="sd"> The field function is used to set default values and provide help messages for each parameter. The Optional type hint is</span> +<span class="sd"> used to indicate that a parameter is optional. The metadata argument is used to provide additional information about </span> +<span class="sd"> each parameter, such as a help message.</span> +<span class="sd"> &quot;&quot;&quot;</span> + +<div class="viewcode-block" id="DatasetArguments.dataset_path"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.dataset_path">[docs]</a> <span class="n">dataset_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The path of the dataset to use.&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.dataset_name"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.dataset_name">[docs]</a> <span class="n">dataset_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;customized&quot;</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Should be </span><span class="se">\&quot;</span><span class="s2">customized</span><span class="se">\&quot;</span><span class="s2">&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.is_custom_dataset"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.is_custom_dataset">[docs]</a> <span class="n">is_custom_dataset</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;whether to use custom data&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.customized_cache_dir"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.customized_cache_dir">[docs]</a> <span class="n">customized_cache_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;.cache/llm-ft/datasets&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Where do you want to store the customized dataset caches&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.dataset_config_name"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.dataset_config_name">[docs]</a> <span class="n">dataset_config_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The configuration name of the dataset to use (via the datasets library).&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.train_file"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.train_file">[docs]</a> <span class="n">train_file</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span><span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The input training data file (a text file).&quot;</span><span class="p">})</span></div> +<div class="viewcode-block" id="DatasetArguments.validation_file"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.validation_file">[docs]</a> <span class="n">validation_file</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;An optional input evaluation data file to evaluate the perplexity on (a text file).&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.max_train_samples"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.max_train_samples">[docs]</a> <span class="n">max_train_samples</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;For debugging purposes or quicker training, truncate the number of training examples to this &quot;</span> + <span class="s2">&quot;value if set.&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.max_eval_samples"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.max_eval_samples">[docs]</a> <span class="n">max_eval_samples</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mf">1e10</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;For debugging purposes or quicker training, truncate the number of evaluation examples to this &quot;</span> + <span class="s2">&quot;value if set.&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.streaming"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.streaming">[docs]</a> <span class="n">streaming</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span><span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Enable streaming mode&quot;</span><span class="p">})</span></div> +<div class="viewcode-block" id="DatasetArguments.block_size"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.block_size">[docs]</a> <span class="n">block_size</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;Optional input sequence length after tokenization. &quot;</span> + <span class="s2">&quot;The training dataset will be truncated in block of this size for training. &quot;</span> + <span class="s2">&quot;Default to the model max input length for single sentence inputs (take into account special tokens).&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.overwrite_cache"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.overwrite_cache">[docs]</a> <span class="n">overwrite_cache</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Overwrite the cached training and evaluation sets&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.validation_split_percentage"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.validation_split_percentage">[docs]</a> <span class="n">validation_split_percentage</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The percentage of the train set used as validation set in case there&#39;s no validation split&quot;</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.preprocessing_num_workers"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.preprocessing_num_workers">[docs]</a> <span class="n">preprocessing_num_workers</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The number of processes to use for the preprocessing.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.group_texts_batch_size"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.group_texts_batch_size">[docs]</a> <span class="n">group_texts_batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;Number of samples that will be grouped together to go though&quot;</span> + <span class="s2">&quot; `group_texts` operation. See `--disable_group_texts` for&quot;</span> + <span class="s2">&quot; detailed explanation of this operation.&quot;</span> + <span class="p">)</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.disable_group_texts"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.disable_group_texts">[docs]</a> <span class="n">disable_group_texts</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;Whether we group original samples together to generate sample&quot;</span> + <span class="s2">&quot; sequences of length `block_size`. By default, we group every&quot;</span> + <span class="s2">&quot; 1000 tokenized sequences together, divide them into &quot;</span> + <span class="s2">&quot; [</span><span class="si">{total_num_tokens}</span><span class="s2"> / </span><span class="si">{block_size}</span><span class="s2">] sequences, each with&quot;</span> + <span class="s2">&quot; `block_size` tokens (the remaining tokens are ommited.&quot;</span> + <span class="s2">&quot; If this flag is set to True, we only group 1 tokenized&quot;</span> + <span class="s2">&quot; sequence, i.e. cutting long sequence into chunks.&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.keep_linebreaks"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.keep_linebreaks">[docs]</a> <span class="n">keep_linebreaks</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Whether to keep line breaks when using TXT files or not.&quot;</span><span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="DatasetArguments.test_file"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.test_file">[docs]</a> <span class="n">test_file</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Evaluation File Path&quot;</span><span class="p">},</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="DatasetArguments.__post_init__"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.__post_init__">[docs]</a> <span class="k">def</span> <span class="nf">__post_init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">streaming</span><span class="p">:</span> + <span class="n">require_version</span><span class="p">(</span><span class="s2">&quot;datasets&gt;=2.0.0&quot;</span><span class="p">,</span> <span class="s2">&quot;The streaming feature requires `datasets&gt;=2.0.0`&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dataset_name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_file</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_file</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Need either a dataset name or a training/validation file.&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">extension</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_file</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> + <span class="k">assert</span> <span class="n">extension</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;csv&quot;</span><span class="p">,</span> <span class="s2">&quot;json&quot;</span><span class="p">,</span> <span class="s2">&quot;txt&quot;</span><span class="p">],</span> <span class="s2">&quot;`train_file` should be a csv, a json or a txt file.&quot;</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">extension</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_file</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> + <span class="k">assert</span> <span class="n">extension</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;csv&quot;</span><span class="p">,</span> <span class="s2">&quot;json&quot;</span><span class="p">,</span> <span class="s2">&quot;txt&quot;</span><span class="p">],</span> <span class="s2">&quot;`validation_file` should be a csv, a json or a txt file.&quot;</span></div></div> + + +<span class="nd">@dataclass</span> +<div class="viewcode-block" id="FinetunerArguments"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.FinetunerArguments">[docs]</a><span class="k">class</span> <span class="nc">FinetunerArguments</span><span class="p">(</span><span class="n">TrainingArguments</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Adapt transformers.TrainingArguments</span> +<span class="sd"> &quot;&quot;&quot;</span> +<div class="viewcode-block" id="FinetunerArguments.eval_dataset_path"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.FinetunerArguments.eval_dataset_path">[docs]</a> <span class="n">eval_dataset_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The path of the eval dataset to use.&quot;</span><span class="p">}</span> + <span class="p">)</span></div></div> + + +<span class="nd">@dataclass</span> +<div class="viewcode-block" id="EvaluatorArguments"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments">[docs]</a><span class="k">class</span> <span class="nc">EvaluatorArguments</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Define a class EvaluatorArguments using the dataclass decorator. The class contains several optional</span> +<span class="sd"> parameters that can be used to configure a evaluator.</span> + +<span class="sd"> local_rank : str</span> +<span class="sd"> For distributed training: local_rank</span> + +<span class="sd"> random_shuffle : bool</span> + +<span class="sd"> use_wandb : bool</span> + +<span class="sd"> random_seed : int, default = 1</span> + +<span class="sd"> output_dir : str, default = &#39;./output_dir&#39;,</span> + +<span class="sd"> mixed_precision : str, choice from [&quot;bf16&quot;,&quot;fp16&quot;].</span> +<span class="sd"> mixed precision mode, whether to use bf16 or fp16</span> + +<span class="sd"> deepspeed : </span> +<span class="sd"> Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already</span> +<span class="sd"> loaded json file as a dict</span> +<span class="sd"> </span> +<span class="sd"> temperature : float</span> +<span class="sd"> An argument of model.generate in huggingface to control the diversity of generation.</span> +<span class="sd"> </span> +<span class="sd"> repetition_penalty : float</span> +<span class="sd"> An argument of model.generate in huggingface to penalize repetitions.</span> +<span class="sd"> &quot;&quot;&quot;</span> +<div class="viewcode-block" id="EvaluatorArguments.local_rank"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.local_rank">[docs]</a> <span class="n">local_rank</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;For distributed training: local_rank&quot;</span> + <span class="p">}</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="EvaluatorArguments.random_shuffle"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.random_shuffle">[docs]</a> <span class="n">random_shuffle</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span> + <span class="p">}</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="EvaluatorArguments.use_wandb"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.use_wandb">[docs]</a> <span class="n">use_wandb</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;When this flag is True, wandb will be enabled&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.random_seed"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.random_seed">[docs]</a> <span class="n">random_seed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;used to set random seed&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.output_dir"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.output_dir">[docs]</a> <span class="n">output_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;./output_dir&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Output path for the inferenced results&quot;</span><span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.mixed_precision"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.mixed_precision">[docs]</a> <span class="n">mixed_precision</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;bf16&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;mixed precision mode, whether to use bf16 or fp16&quot;</span> + <span class="p">),</span> + <span class="s2">&quot;choices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;bf16&quot;</span><span class="p">,</span><span class="s2">&quot;fp16&quot;</span><span class="p">],</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.deepspeed"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.deepspeed">[docs]</a> <span class="n">deepspeed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already&quot;</span> + <span class="s2">&quot; loaded json file as a dict&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.answer_type"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.answer_type">[docs]</a> <span class="n">answer_type</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;text&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s1">&#39;Question type for answer extraction from the decoder output.&#39;</span> + <span class="s1">&#39; Supported types: </span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; 1) &quot;multiple_choice&quot;, e.g. A, B, C, D, ...</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; 2) &quot;binary_choice&quot;, e.g. yes, no, maybe</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; 3) &quot;math&quot;, e.g. 1.0, -3.52</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; 4) &quot;text&quot;, e.g. &quot;I think that it is okay&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; 5) Special treatment for several datasets</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;gsm8k&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;svamp&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;asdiv&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;addsub&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;singleeq&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;multiarith&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;aqua&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;csqa&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;strategyqa&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;pubmedqa&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;medmcqa&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; - &quot;usmle&quot;</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.prompt_structure"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.prompt_structure">[docs]</a> <span class="n">prompt_structure</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">{input}</span><span class="s2">&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s1">&#39;Prompt structure to facilitate prompt engineering during&#39;</span> + <span class="s1">&#39; inference. The model will receive&#39;</span> + <span class="s1">&#39; `prompt_structure.format(input=input)` as its input.&#39;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.evaluate_block_size"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.evaluate_block_size">[docs]</a> <span class="n">evaluate_block_size</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;the model will have at least block_size tokens for context when calculating the conditional likelihood of any one token&quot;</span> + <span class="s2">&quot; (provided there are block_size preceding tokens available to condition on)&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.metric"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.metric">[docs]</a> <span class="n">metric</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;the metric the model will be evaluated on&quot;</span><span class="p">,</span> + <span class="s2">&quot;choices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;ppl&quot;</span><span class="p">,</span> <span class="s2">&quot;perplexity&quot;</span><span class="p">,</span> <span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> <span class="s2">&quot;nll&quot;</span><span class="p">,</span> <span class="s2">&quot;neg_log_likelihood&quot;</span><span class="p">],</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.inference_batch_size_per_device"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.inference_batch_size_per_device">[docs]</a> <span class="n">inference_batch_size_per_device</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;every device will infer </span><span class="si">{inference_batch_size_per_device}</span><span class="s2">&quot;</span> + <span class="s2">&quot; samples in parallel. The inferred results will be concatenaed&quot;</span> + <span class="s2">&quot; with inputs and attach a reward.&quot;</span> + <span class="p">),</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="EvaluatorArguments.use_accelerator_for_evaluator"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.use_accelerator_for_evaluator">[docs]</a> <span class="n">use_accelerator_for_evaluator</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Whether to use Huggingface Accelerator instead of Deepspeed&quot;</span><span class="p">},</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="EvaluatorArguments.temperature"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.temperature">[docs]</a> <span class="n">temperature</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Temperature during inference.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="EvaluatorArguments.repetition_penalty"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.repetition_penalty">[docs]</a> <span class="n">repetition_penalty</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Repetition_penalty during inference.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="EvaluatorArguments.max_new_tokens"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.max_new_tokens">[docs]</a> <span class="n">max_new_tokens</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Maximum length during inference.&quot;</span><span class="p">},</span> + <span class="p">)</span></div></div> + +<span class="nd">@dataclass</span> +<div class="viewcode-block" id="InferencerArguments"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments">[docs]</a><span class="k">class</span> <span class="nc">InferencerArguments</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Define a class InferencerArguments using the dataclass decorator. The class contains several optional</span> +<span class="sd"> parameters that can be used to configure a inferencer.</span> + +<span class="sd"> local_rank : str</span> +<span class="sd"> For distributed training: local_rank</span> + +<span class="sd"> random_seed : int, default = 1</span> + +<span class="sd"> deepspeed :</span> +<span class="sd"> Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already</span> +<span class="sd"> loaded json file as a dict</span> +<span class="sd"> mixed_precision : str, choice from [&quot;bf16&quot;,&quot;fp16&quot;].</span> +<span class="sd"> mixed precision mode, whether to use bf16 or fp16</span> +<span class="sd"> </span> +<span class="sd"> temperature : float</span> +<span class="sd"> An argument of model.generate in huggingface to control the diversity of generation.</span> +<span class="sd"> </span> +<span class="sd"> repetition_penalty : float</span> +<span class="sd"> An argument of model.generate in huggingface to penalize repetitions.</span> +<span class="sd"> &quot;&quot;&quot;</span> +<div class="viewcode-block" id="InferencerArguments.device"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.device">[docs]</a> <span class="n">device</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;gpu&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;device of chatbot&quot;</span><span class="p">,</span> + <span class="s2">&quot;choices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;gpu&quot;</span><span class="p">,</span> <span class="s2">&quot;cpu&quot;</span><span class="p">],</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="InferencerArguments.local_rank"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.local_rank">[docs]</a> <span class="n">local_rank</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;For distributed training: local_rank&quot;</span> + <span class="p">},</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="InferencerArguments.temperature"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.temperature">[docs]</a> <span class="n">temperature</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Temperature during inference.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="InferencerArguments.repetition_penalty"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.repetition_penalty">[docs]</a> <span class="n">repetition_penalty</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Repetition_penalty during inference.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="InferencerArguments.max_new_tokens"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.max_new_tokens">[docs]</a> <span class="n">max_new_tokens</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Maximum length during inference.&quot;</span><span class="p">},</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="InferencerArguments.random_seed"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.random_seed">[docs]</a> <span class="n">random_seed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;used to set random seed&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="InferencerArguments.deepspeed"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.deepspeed">[docs]</a> <span class="n">deepspeed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already&quot;</span> + <span class="s2">&quot; loaded json file as a dict&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="InferencerArguments.mixed_precision"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.mixed_precision">[docs]</a> <span class="n">mixed_precision</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;bf16&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;mixed precision mode, whether to use bf16 or fp16&quot;</span> + <span class="p">),</span> + <span class="s2">&quot;choices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;bf16&quot;</span><span class="p">,</span><span class="s2">&quot;fp16&quot;</span><span class="p">],</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="InferencerArguments.do_sample"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.do_sample">[docs]</a> <span class="n">do_sample</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;whether turn on true random sampling during inference.&quot;</span> + <span class="p">},</span> + <span class="p">)</span></div></div> + +<span class="nd">@dataclass</span> +<div class="viewcode-block" id="RaftAlignerArguments"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments">[docs]</a><span class="k">class</span> <span class="nc">RaftAlignerArguments</span><span class="p">(</span><span class="n">TrainingArguments</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Define a class RaftAlignerArguments to configure raft aligner.</span> +<span class="sd"> &quot;&quot;&quot;</span> +<div class="viewcode-block" id="RaftAlignerArguments.output_reward_path"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.output_reward_path">[docs]</a> <span class="n">output_reward_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;tmp/raft_aligner/&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The path of output rewards.&quot;</span> + <span class="p">}</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="RaftAlignerArguments.output_min_length"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.output_min_length">[docs]</a> <span class="n">output_min_length</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;minimum length of the output token sequence generated from&quot;</span> + <span class="s2">&quot; model given an input.&quot;</span> + <span class="p">),</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="RaftAlignerArguments.output_max_length"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.output_max_length">[docs]</a> <span class="n">output_max_length</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;maximum length of the output token sequence generated from&quot;</span> + <span class="s2">&quot; model given an output.&quot;</span> + <span class="p">),</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="RaftAlignerArguments.num_raft_iteration"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.num_raft_iteration">[docs]</a> <span class="n">num_raft_iteration</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;number of iterations of the raft aligner.&quot;</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="RaftAlignerArguments.raft_batch_size"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.raft_batch_size">[docs]</a> <span class="n">raft_batch_size</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1024</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;only select </span><span class="si">{raft_batch_size}</span><span class="s2"> samples each time for STF training.&quot;</span> + <span class="p">)</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="RaftAlignerArguments.top_reward_percentage"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.top_reward_percentage">[docs]</a> <span class="n">top_reward_percentage</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;only top </span><span class="si">{top_reward_percentage}</span><span class="s2"> samples in the raft batch,&quot;</span> + <span class="s2">&quot; (in terms of rewards), will be used for SFT the model.&quot;</span> + <span class="p">),</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="RaftAlignerArguments.inference_batch_size_per_device"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.inference_batch_size_per_device">[docs]</a> <span class="n">inference_batch_size_per_device</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;every device will infer </span><span class="si">{inference_batch_size_per_device}</span><span class="s2">&quot;</span> + <span class="s2">&quot; samples in parallel. The inferred results will be concatenaed&quot;</span> + <span class="s2">&quot; with inputs and attach a reward.&quot;</span> + <span class="p">),</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="RaftAlignerArguments.collection_strategy"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.collection_strategy">[docs]</a> <span class="n">collection_strategy</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;top&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;</span><span class="si">{collection_strategy}</span><span class="s2"> is either top or local&quot;</span> + <span class="s2">&quot; top means that we rank the samples globally regardless of the prompts&quot;</span> + <span class="s2">&quot; local means that we only rank the samples with the same prompt&quot;</span> + <span class="p">),</span> + <span class="p">},</span> + <span class="p">)</span></div></div> + +<span class="nd">@dataclass</span> +<div class="viewcode-block" id="BenchmarkingArguments"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.BenchmarkingArguments">[docs]</a><span class="k">class</span> <span class="nc">BenchmarkingArguments</span><span class="p">:</span> +<div class="viewcode-block" id="BenchmarkingArguments.dataset_name"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.BenchmarkingArguments.dataset_name">[docs]</a> <span class="n">dataset_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;benchmark dataset name provided by lmflow&quot;</span> + <span class="p">},</span> + <span class="p">)</span></div> +<div class="viewcode-block" id="BenchmarkingArguments.lm_evaluation_metric"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.BenchmarkingArguments.lm_evaluation_metric">[docs]</a> <span class="n">lm_evaluation_metric</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;the metric the model will be evaluated on&quot;</span><span class="p">,</span> + <span class="s2">&quot;choices&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="s2">&quot;acc_norm&quot;</span><span class="p">,</span> <span class="s2">&quot;bleu&quot;</span><span class="p">,</span> <span class="s2">&quot;chrf&quot;</span><span class="p">,</span> <span class="s2">&quot;em&quot;</span><span class="p">,</span> <span class="s2">&quot;f1&quot;</span><span class="p">,</span> <span class="s2">&quot;ppl&quot;</span><span class="p">,</span> \ + <span class="s2">&quot;ter&quot;</span><span class="p">,</span> <span class="s2">&quot;r@1&quot;</span><span class="p">,</span> <span class="s2">&quot;r@2&quot;</span><span class="p">,</span> <span class="s2">&quot;mrr&quot;</span><span class="p">,</span> <span class="s2">&quot;mc1&quot;</span><span class="p">,</span> <span class="s2">&quot;mc2&quot;</span><span class="p">,</span> <span class="s2">&quot;word_perplexity&quot;</span><span class="p">,</span> \ + <span class="s2">&quot;byte_perplexity&quot;</span><span class="p">,</span> <span class="s2">&quot;bits_per_byte&quot;</span><span class="p">],</span> + <span class="p">},</span> + <span class="p">)</span></div></div> + + + +<div class="viewcode-block" id="PIPELINE_ARGUMENT_MAPPING"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.PIPELINE_ARGUMENT_MAPPING">[docs]</a><span class="n">PIPELINE_ARGUMENT_MAPPING</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;finetuner&quot;</span><span class="p">:</span> <span class="n">FinetunerArguments</span><span class="p">,</span> + <span class="s2">&quot;evaluator&quot;</span><span class="p">:</span> <span class="n">EvaluatorArguments</span><span class="p">,</span> + <span class="s2">&quot;inferencer&quot;</span><span class="p">:</span> <span class="n">InferencerArguments</span><span class="p">,</span> + <span class="s2">&quot;raft_aligner&quot;</span><span class="p">:</span> <span class="n">RaftAlignerArguments</span><span class="p">,</span> +<span class="p">}</span></div> + + +<div class="viewcode-block" id="AutoArguments"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.AutoArguments">[docs]</a><span class="k">class</span> <span class="nc">AutoArguments</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Automatically choose arguments from FinetunerArguments or EvaluatorArguments.</span> +<span class="sd"> &quot;&quot;&quot;</span> +<div class="viewcode-block" id="AutoArguments.get_pipeline_args_class"><a class="viewcode-back" href="../../autoapi/lmflow/args/index.html#lmflow.args.AutoArguments.get_pipeline_args_class">[docs]</a> <span class="k">def</span> <span class="nf">get_pipeline_args_class</span><span class="p">(</span><span class="n">pipeline_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span> + <span class="k">return</span> <span class="n">PIPELINE_ARGUMENT_MAPPING</span><span class="p">[</span><span class="n">pipeline_name</span><span class="p">]</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded 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class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.datasets.dataset</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.datasets.dataset</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;This Python code defines a class Dataset with methods for initializing, loading,</span> +<span class="sd">and manipulating datasets from different backends such as Hugging Face and JSON.</span> +<span class="sd"> </span> +<span class="sd">The `Dataset` class includes methods for loading datasets from a dictionary and a Hugging</span> +<span class="sd">Face dataset, mapping datasets, and retrieving the backend dataset and arguments.</span> +<span class="sd">&quot;&quot;&quot;</span> + + + +<span class="c1"># Importing necessary libraries and modules</span> +<span class="kn">import</span> <span class="nn">copy</span> +<span class="kn">import</span> <span class="nn">json</span> + +<span class="kn">from</span> <span class="nn">cmath</span> <span class="kn">import</span> <span class="n">e</span> +<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span> +<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span> + +<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">load_dataset</span> +<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="n">Dataset</span> <span class="k">as</span> <span class="n">HFDataset</span> + +<span class="kn">from</span> <span class="nn">lmflow.args</span> <span class="kn">import</span> <span class="n">DatasetArguments</span> +<span class="kn">from</span> <span class="nn">lmflow.utils.constants</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">DATASET_DESCRIPTION_MAP</span><span class="p">,</span> + <span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span><span class="p">,</span> + <span class="n">TEXT2TEXT_DATASET_DESCRIPTION</span><span class="p">,</span> + <span class="n">FLOAT_ONLY_DATASET_DESCRIPTION</span><span class="p">,</span> + <span class="n">INSTANCE_FIELDS_MAP</span><span class="p">,</span> +<span class="p">)</span> + +<div class="viewcode-block" id="DATASET_TYPES"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.DATASET_TYPES">[docs]</a><span class="n">DATASET_TYPES</span> <span class="o">=</span> <span class="p">[</span> + <span class="s2">&quot;text_only&quot;</span><span class="p">,</span> + <span class="s2">&quot;text2text&quot;</span><span class="p">,</span> + <span class="s2">&quot;float_only&quot;</span><span class="p">,</span> + <span class="s2">&quot;image_text&quot;</span><span class="p">,</span> +<span class="p">]</span></div> + +<div class="viewcode-block" id="KEY_TYPE"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.KEY_TYPE">[docs]</a><span class="n">KEY_TYPE</span> <span class="o">=</span> <span class="s2">&quot;type&quot;</span></div> +<div class="viewcode-block" id="KEY_INSTANCES"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.KEY_INSTANCES">[docs]</a><span class="n">KEY_INSTANCES</span> <span class="o">=</span> <span class="s2">&quot;instances&quot;</span></div> + +<div class="viewcode-block" id="Dataset"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/index.html#lmflow.datasets.dataset.Dataset">[docs]</a><span class="k">class</span> <span class="nc">Dataset</span><span class="p">:</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes the Dataset object with the given parameters.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> data_args : DatasetArguments object.</span> +<span class="sd"> Contains the arguments required to load the dataset.</span> + +<span class="sd"> backend : str, default=&quot;huggingface&quot;</span> +<span class="sd"> A string representing the dataset backend. Defaults to &quot;huggingface&quot;.</span> +<span class="sd"> </span> +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_args</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">backend</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s2">&quot;huggingface&quot;</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span> <span class="o">=</span> <span class="n">data_args</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend</span> <span class="o">=</span> <span class="n">backend</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span> <span class="o">=</span> <span class="kc">None</span> + <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># Original type of the dataset</span> + <span class="bp">self</span><span class="o">.</span><span class="n">dataset_path</span> <span class="o">=</span> <span class="n">data_args</span><span class="o">.</span><span class="n">dataset_path</span> + + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">dataset_path</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">return</span> + + <span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;huggingface&quot;</span><span class="p">:</span> + <span class="n">data_files</span> <span class="o">=</span> <span class="p">[</span> + <span class="n">x</span><span class="o">.</span><span class="n">absolute</span><span class="p">()</span><span class="o">.</span><span class="n">as_posix</span><span class="p">()</span> + <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset_path</span><span class="p">)</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s2">&quot;*.json&quot;</span><span class="p">)</span> + <span class="p">]</span> + + <span class="c1"># Iterate through all the files and ensure they have the same data type</span> + <span class="k">for</span> <span class="n">single_file</span> <span class="ow">in</span> <span class="n">data_files</span><span class="p">:</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">single_file</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span> + <span class="n">json_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fin</span><span class="p">)</span> + <span class="k">if</span> <span class="n">KEY_TYPE</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">json_data</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">KEY_TYPE</span><span class="si">}</span><span class="s1">&quot; field must be specified for data, e.g.&#39;</span> + <span class="s1">&#39;{</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="sa">f</span><span class="s1">&#39; &quot;</span><span class="si">{</span><span class="n">KEY_TYPE</span><span class="si">}</span><span class="s1">: &quot;text_only&quot;,</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="sa">f</span><span class="s1">&#39; &quot;</span><span class="si">{</span><span class="n">KEY_INSTANCES</span><span class="si">}</span><span class="s1">&quot;: [</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; { &quot;text&quot;: &quot;Sentence 1: This is a sentence.&quot; }</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39; { &quot;text&quot;: &quot;Sentence 2: This is another sentence.&quot; }</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="sa">f</span><span class="s1">&#39; ]</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="s1">&#39;}&#39;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="n">json_data</span><span class="p">[</span><span class="n">KEY_TYPE</span><span class="p">]</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="n">json_data</span><span class="p">[</span><span class="n">KEY_TYPE</span><span class="p">]:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s1">&#39;All task files must have same data types. Previous&#39;</span> + <span class="sa">f</span><span class="s1">&#39; files have type &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="si">}</span><span class="s1">&quot;, but in file&#39;</span> + <span class="sa">f</span><span class="s1">&#39; </span><span class="si">{</span><span class="n">single_file</span><span class="si">}</span><span class="s1">, it has type &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="si">}</span><span class="s1">&quot;.&#39;</span> + <span class="p">)</span> + + <span class="c1"># Load the dataset using the HuggingFace dataset library</span> + <span class="n">extensions</span> <span class="o">=</span> <span class="s2">&quot;json&quot;</span> + <span class="n">raw_dataset</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span> + <span class="n">extensions</span><span class="p">,</span> + <span class="n">data_files</span><span class="o">=</span><span class="n">data_files</span><span class="p">,</span> + <span class="n">field</span><span class="o">=</span><span class="n">KEY_INSTANCES</span><span class="p">,</span> + <span class="n">split</span><span class="o">=</span><span class="s2">&quot;train&quot;</span><span class="p">,</span> + <span class="n">use_auth_token</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span> <span class="o">=</span> <span class="n">raw_dataset</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_check_data_format</span><span class="p">()</span> + <span class="k">elif</span> <span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;json&quot;</span><span class="p">:</span> + <span class="c1"># TODO (@Jiachun)</span> + <span class="k">pass</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Unsupported dataset backend &quot;</span><span class="si">{</span><span class="n">backend</span><span class="si">}</span><span class="s1">&quot;&#39;</span><span class="p">)</span> + + +<div class="viewcode-block" id="Dataset._check_data_format"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset._check_data_format">[docs]</a> <span class="k">def</span> <span class="nf">_check_data_format</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;Checks if data type and data structure matches</span> + +<span class="sd"> Raise messages with hints if not matched.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">data_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span> + <span class="k">if</span> <span class="n">KEY_TYPE</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">KEY_TYPE</span><span class="si">}</span><span class="s1">&quot; must be provided to initialize a dataset,&#39;</span> + <span class="sa">f</span><span class="s1">&#39; e.g.</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="sa">f</span><span class="s1">&#39; </span><span class="si">{</span><span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span><span class="si">}</span><span class="s1">&#39;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">KEY_INSTANCES</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">KEY_INSTANCES</span><span class="si">}</span><span class="s1">&quot; must be provided to initialize a&#39;</span> + <span class="sa">f</span><span class="s1">&#39; dataset, e.g.</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="sa">f</span><span class="s1">&#39; </span><span class="si">{</span><span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span><span class="si">}</span><span class="s1">&#39;</span> + <span class="p">)</span> + + <span class="n">data_type</span> <span class="o">=</span> <span class="n">data_dict</span><span class="p">[</span><span class="n">KEY_TYPE</span><span class="p">]</span> + <span class="n">fields</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_backend_dataset</span><span class="p">()</span><span class="o">.</span><span class="n">features</span> + <span class="n">correct_fields</span> <span class="o">=</span> <span class="n">INSTANCE_FIELDS_MAP</span><span class="p">[</span><span class="n">data_type</span><span class="p">]</span> + <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">fields</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="n">correct_fields</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;Data instance fields incorrect&#39;</span> + <span class="sa">f</span><span class="s1">&#39; </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">fields</span><span class="p">)</span><span class="si">}</span><span class="s1">: should be </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">correct_fields</span><span class="p">)</span><span class="si">}</span><span class="s1">.&#39;</span> + <span class="p">)</span></div> + + +<div class="viewcode-block" id="Dataset.from_dict"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.from_dict">[docs]</a> <span class="k">def</span> <span class="nf">from_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dict_obj</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Create a Dataset object from a dictionary.</span> + +<span class="sd"> Return a Dataset given a dict with format:</span> +<span class="sd"> {</span> +<span class="sd"> &quot;type&quot;: TYPE,</span> +<span class="sd"> &quot;instances&quot;: [</span> +<span class="sd"> {</span> +<span class="sd"> &quot;key_1&quot;: VALUE_1.1,</span> +<span class="sd"> &quot;key_2&quot;: VALUE_1.2,</span> +<span class="sd"> ...</span> +<span class="sd"> },</span> +<span class="sd"> {</span> +<span class="sd"> &quot;key_1&quot;: VALUE_2.1,</span> +<span class="sd"> &quot;key_2&quot;: VALUE_2.2,</span> +<span class="sd"> ...</span> +<span class="sd"> },</span> +<span class="sd"> ...</span> +<span class="sd"> ]</span> +<span class="sd"> }</span> + +<span class="sd"> Parameters</span> +<span class="sd"> -----------</span> + +<span class="sd"> dict_obj : dict.</span> +<span class="sd"> A dictionary containing the dataset information.</span> +<span class="sd"> </span> +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> + +<span class="sd"> Returns</span> +<span class="sd"> ---------</span> + +<span class="sd"> self : Dataset object.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;huggingface&quot;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">KEY_TYPE</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dict_obj</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">KEY_TYPE</span><span class="si">}</span><span class="s1">&quot; must be provided to initialize a dataset,&#39;</span> + <span class="sa">f</span><span class="s1">&#39; e.g.</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="sa">f</span><span class="s1">&#39; </span><span class="si">{</span><span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span><span class="si">}</span><span class="s1">&#39;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">KEY_INSTANCES</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dict_obj</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;&quot;</span><span class="si">{</span><span class="n">KEY_INSTANCES</span><span class="si">}</span><span class="s1">&quot; must be provided to initialize a&#39;</span> + <span class="sa">f</span><span class="s1">&#39; dataset, e.g.</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="sa">f</span><span class="s1">&#39; </span><span class="si">{</span><span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span><span class="si">}</span><span class="s1">&#39;</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="n">dict_obj</span><span class="p">[</span><span class="n">KEY_TYPE</span><span class="p">]</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="ow">in</span> <span class="n">INSTANCE_FIELDS_MAP</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;type &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="si">}</span><span class="s1">&quot; is not supported&#39;</span><span class="p">)</span> + + <span class="n">correct_fields</span> <span class="o">=</span> <span class="n">INSTANCE_FIELDS_MAP</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">]</span> + + <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">instance</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dict_obj</span><span class="p">[</span><span class="n">KEY_INSTANCES</span><span class="p">]):</span> + <span class="n">fields</span> <span class="o">=</span> <span class="n">instance</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> + <span class="k">if</span> <span class="nb">set</span><span class="p">(</span><span class="n">fields</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">set</span><span class="p">(</span><span class="n">correct_fields</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;data instance fields incorrect&#39;</span> + <span class="sa">f</span><span class="s1">&#39; </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">fields</span><span class="p">)</span><span class="si">}</span><span class="s1">: should be </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">correct_fields</span><span class="p">)</span><span class="si">}</span><span class="s1">.</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="sa">f</span><span class="s1">&#39;The bad instance triggers the error, the </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">-th instance:</span><span class="se">\n</span><span class="s1">&#39;</span> + <span class="sa">f</span><span class="s1">&#39; </span><span class="si">{</span><span class="n">instance</span><span class="si">}</span><span class="s1">&#39;</span> + <span class="p">)</span> + + <span class="k">try</span><span class="p">:</span> + <span class="n">hf_dict</span> <span class="o">=</span> <span class="p">{}</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dict_obj</span><span class="p">[</span><span class="n">KEY_INSTANCES</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">dict_obj</span><span class="p">[</span><span class="n">KEY_INSTANCES</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span> + <span class="n">hf_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span> + <span class="n">instance</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">dict_obj</span><span class="p">[</span><span class="n">KEY_INSTANCES</span><span class="p">]</span> + <span class="p">]</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span> <span class="o">=</span> <span class="n">HFDataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">hf_dict</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">except</span> <span class="ne">AttributeError</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Error occurs: </span><span class="si">{</span><span class="n">ex</span><span class="si">}</span><span class="s2">. Failed to convert dict to&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="se">\&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2"> dataset,&quot;</span> <span class="sa">f</span><span class="s2">&quot; the standard format is as&quot;</span> + <span class="sa">f</span><span class="s2">&quot; follows:</span><span class="se">\n</span><span class="s2">&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">DATASET_DESCRIPTION_MAP</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_check_data_format</span><span class="p">()</span> + + <span class="k">return</span> <span class="bp">self</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;dict&quot;</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span> <span class="o">=</span> <span class="n">dict_obj</span> + <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="n">dict_obj</span><span class="p">[</span><span class="n">KEY_TYPE</span><span class="p">]</span> + <span class="k">return</span> <span class="bp">self</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;Currently .from_dict is not supported for backend &quot;</span><span class="si">{</span><span class="n">backend</span><span class="si">}</span><span class="s1">&quot;&#39;</span> + <span class="p">)</span></div> + + + <span class="nd">@classmethod</span> +<div class="viewcode-block" id="Dataset.create_from_dict"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.create_from_dict">[docs]</a> <span class="k">def</span> <span class="nf">create_from_dict</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dict_obj</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns</span> +<span class="sd"> --------</span> + +<span class="sd"> Returns a Dataset object given a dict.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">empty_data_args</span> <span class="o">=</span> <span class="n">DatasetArguments</span><span class="p">(</span><span class="n">dataset_path</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> + <span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">empty_data_args</span><span class="p">)</span> + <span class="k">return</span> <span class="n">dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">dict_obj</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="Dataset.to_dict"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.to_dict">[docs]</a> <span class="k">def</span> <span class="nf">to_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns</span> +<span class="sd"> ---------</span> + +<span class="sd"> Return a dict represents the dataset:</span> +<span class="sd"> {</span> +<span class="sd"> &quot;type&quot;: TYPE,</span> +<span class="sd"> &quot;instances&quot;: [</span> +<span class="sd"> {</span> +<span class="sd"> &quot;key_1&quot;: VALUE_1.1,</span> +<span class="sd"> &quot;key_2&quot;: VALUE_1.2,</span> +<span class="sd"> ...</span> +<span class="sd"> },</span> +<span class="sd"> {</span> +<span class="sd"> &quot;key_1&quot;: VALUE_2.1,</span> +<span class="sd"> &quot;key_2&quot;: VALUE_2.2,</span> +<span class="sd"> ...</span> +<span class="sd"> },</span> +<span class="sd"> ...</span> +<span class="sd"> ]</span> +<span class="sd"> }</span> + +<span class="sd"> A python dict object represents the content of this dataset.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;huggingface&quot;</span><span class="p">:</span> + <span class="n">dict_obj</span> <span class="o">=</span> <span class="p">{}</span> + <span class="n">dict_obj</span><span class="p">[</span><span class="n">KEY_TYPE</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_type</span><span class="p">()</span> + <span class="n">hf_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span> + <span class="n">dict_obj</span><span class="p">[</span><span class="n">KEY_INSTANCES</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="n">first_key</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">hf_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span> + <span class="n">first_key</span> <span class="o">=</span> <span class="n">key</span> + <span class="k">break</span> + + <span class="k">if</span> <span class="n">first_key</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">num_instances</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">hf_dict</span><span class="p">[</span><span class="n">first_key</span><span class="p">])</span> + <span class="n">dict_obj</span><span class="p">[</span><span class="n">KEY_INSTANCES</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span> + <span class="p">{</span> + <span class="n">key</span><span class="p">:</span> <span class="n">hf_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">hf_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> + <span class="p">}</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_instances</span><span class="p">)</span> + <span class="p">]</span> + + <span class="k">return</span> <span class="n">dict_obj</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;dict&quot;</span><span class="p">:</span> + <span class="n">dict_obj</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span> + <span class="k">return</span> <span class="n">dict_obj</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;Current .to_dict is not supported for backend &quot;</span><span class="si">{</span><span class="n">backend</span><span class="si">}</span><span class="s1">&quot;&#39;</span> + <span class="p">)</span></div> + + +<div class="viewcode-block" id="Dataset.to_list"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.to_list">[docs]</a> <span class="k">def</span> <span class="nf">to_list</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;Returns a list of instances.&quot;&quot;&quot;</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;huggingface&quot;</span><span class="p">:</span> + <span class="n">instance_list</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span> + <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span><span class="p">))]</span> + <span class="k">return</span> <span class="n">instance_list</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;dict&quot;</span><span class="p">:</span> + <span class="n">instance_list</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span><span class="p">[</span><span class="n">KEY_INSTANCES</span><span class="p">])</span> + <span class="c1"># TODO: should be a list of instances, instance should be huggingface datasets row format</span> + <span class="k">return</span> <span class="n">instance_list</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;Current .to_list is not supported for backend &quot;</span><span class="si">{</span><span class="n">backend</span><span class="si">}</span><span class="s1">&quot;&#39;</span> + <span class="p">)</span></div> + + +<div class="viewcode-block" id="Dataset.map"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.map">[docs]</a> <span class="k">def</span> <span class="nf">map</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> + +<span class="sd"> Returns</span> +<span class="sd"> ---------</span> + +<span class="sd"> self : Dataset object.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># If the dataset uses Hugging Face as the backend, </span> + <span class="c1"># call the `map()` function of the Hugging Face backend dataset</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend</span> <span class="o">==</span> <span class="s2">&quot;huggingface&quot;</span><span class="p">:</span> + <span class="c1"># Set the mapped dataset as the backend dataset of the current dataset</span> + <span class="n">mapped_backend_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span> <span class="o">=</span> <span class="n">mapped_backend_dataset</span> + <span class="k">return</span> <span class="bp">self</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># If the backend is not Hugging Face, raise a NotImplementedError</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;Currently .map is not supported for backend &quot;</span><span class="si">{</span><span class="n">backend</span><span class="si">}</span><span class="s1">&quot;&#39;</span> + <span class="p">)</span></div> + + +<div class="viewcode-block" id="Dataset.get_backend"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_backend">[docs]</a> <span class="k">def</span> <span class="nf">get_backend</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns</span> +<span class="sd"> ---------</span> + +<span class="sd"> self.backend</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend</span></div> + + +<div class="viewcode-block" id="Dataset.get_backend_dataset"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_backend_dataset">[docs]</a> <span class="k">def</span> <span class="nf">get_backend_dataset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns</span> +<span class="sd"> ---------</span> + +<span class="sd"> self.backend_dataset</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span></div> + + +<div class="viewcode-block" id="Dataset.get_fingerprint"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_fingerprint">[docs]</a> <span class="k">def</span> <span class="nf">get_fingerprint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns</span> +<span class="sd"> ---------</span> + +<span class="sd"> Fingerprint of the backend_dataset which controls the cache</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_dataset</span><span class="o">.</span><span class="n">_fingerprint</span></div> + + +<div class="viewcode-block" id="Dataset.get_data_args"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_data_args">[docs]</a> <span class="k">def</span> <span class="nf">get_data_args</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns</span> +<span class="sd"> ---------</span> + +<span class="sd"> self.data_args</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span></div> + + +<div class="viewcode-block" id="Dataset.get_type"><a class="viewcode-back" href="../../../autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_type">[docs]</a> <span class="k">def</span> <span class="nf">get_type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns</span> +<span class="sd"> ---------</span> + +<span class="sd"> self.type</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer 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aria-current="page">lmflow.models.auto_model</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.models.auto_model</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;Automatically get correct model type.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">lmflow.models.hf_decoder_model</span> <span class="kn">import</span> <span class="n">HFDecoderModel</span> +<span class="kn">from</span> <span class="nn">lmflow.models.text_regression_model</span> <span class="kn">import</span> <span class="n">TextRegressionModel</span> +<span class="kn">from</span> <span class="nn">lmflow.models.hf_encoder_decoder_model</span> <span class="kn">import</span> <span class="n">HFEncoderDecoderModel</span> + +<div class="viewcode-block" id="AutoModel"><a class="viewcode-back" href="../../../autoapi/lmflow/models/auto_model/index.html#lmflow.models.auto_model.AutoModel">[docs]</a><span class="k">class</span> <span class="nc">AutoModel</span><span class="p">:</span> + + <span class="nd">@classmethod</span> +<div class="viewcode-block" id="AutoModel.get_model"><a class="viewcode-back" href="../../../autoapi/lmflow/models/auto_model/index.html#lmflow.models.auto_model.AutoModel.get_model">[docs]</a> <span class="k">def</span> <span class="nf">get_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_args</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="n">arch_type</span> <span class="o">=</span> <span class="n">model_args</span><span class="o">.</span><span class="n">arch_type</span> + <span class="k">if</span> <span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;decoder_only&quot;</span><span class="p">:</span> + <span class="k">return</span> <span class="n">HFDecoderModel</span><span class="p">(</span><span class="n">model_args</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;text_regression&quot;</span><span class="p">:</span> + <span class="k">return</span> <span class="n">TextRegressionModel</span><span class="p">(</span><span class="n">model_args</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;encoder_decoder&quot;</span> <span class="ow">or</span> \ + <span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;vision_encoder_decoder&quot;</span><span class="p">:</span> + <span class="k">return</span> <span class="n">HFEncoderDecoderModel</span><span class="p">(</span><span class="n">model_args</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;model architecture type </span><span class="se">\&quot;</span><span class="si">{</span><span class="n">arch_type</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2"> is not supported&quot;</span> + <span class="p">)</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div class="footer-item"> + <p class="copyright"> + + © Copyright LMFlow 2023. + <br/> + + </p> +</div> + + <div class="footer-item"> + <p class="sphinx-version"> + Created using <a href="https://sphinx-doc.org/">Sphinx</a> 5.3.0. + <br/> + </p> +</div> + + </div> + + + <div 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coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;Base model class.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span> + + +<div class="viewcode-block" id="BaseModel"><a class="viewcode-back" href="../../../autoapi/lmflow/models/base_model/index.html#lmflow.models.base_model.BaseModel">[docs]</a><span class="k">class</span> <span class="nc">BaseModel</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span> + + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="k">pass</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> 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by a period.</span> + +<span class="sd">Leave one blank line. The rest of this docstring should contain an</span> +<span class="sd">overall description of the module or program. Optionally, it may also</span> +<span class="sd">contain a brief description of exported classes and functions and/or usage</span> +<span class="sd">examples.</span> + +<span class="sd">Typical usage example:</span> + +<span class="sd"> foo = ClassFoo()</span> +<span class="sd"> bar = foo.FunctionBar()</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">lmflow.models.base_model</span> <span class="kn">import</span> <span class="n">BaseModel</span> + + +<div class="viewcode-block" id="DecoderModel"><a class="viewcode-back" href="../../../autoapi/lmflow/models/decoder_model/index.html#lmflow.models.decoder_model.DecoderModel">[docs]</a><span class="k">class</span> <span class="nc">DecoderModel</span><span class="p">(</span><span class="n">BaseModel</span><span class="p">):</span> + + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="k">pass</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div class="footer-item"> + <p class="copyright"> + + © Copyright LMFlow 2023. + <br/> + + </p> +</div> + + <div 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id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.models.encoder_decoder_model</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;A one-line summary of the module or program, terminated by a period.</span> + +<span class="sd">Leave one blank line. The rest of this docstring should contain an</span> +<span class="sd">overall description of the module or program. Optionally, it may also</span> +<span class="sd">contain a brief description of exported classes and functions and/or usage</span> +<span class="sd">examples.</span> + +<span class="sd">Typical usage example:</span> + +<span class="sd"> foo = ClassFoo()</span> +<span class="sd"> bar = foo.FunctionBar()</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">lmflow.models.base_model</span> <span class="kn">import</span> <span class="n">BaseModel</span> + + +<div class="viewcode-block" id="EncoderDecoderModel"><a class="viewcode-back" href="../../../autoapi/lmflow/models/encoder_decoder_model/index.html#lmflow.models.encoder_decoder_model.EncoderDecoderModel">[docs]</a><span class="k">class</span> <span class="nc">EncoderDecoderModel</span><span class="p">(</span><span class="n">BaseModel</span><span class="p">):</span> + + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="k">pass</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div class="footer-item"> + <p class="copyright"> + + © Copyright LMFlow 2023. + <br/> + + </p> 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<article class="bd-article" role="main"> + + <h1>Source code for lmflow.models.hf_decoder_model</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;This is a class called HFDecoderModel which is a wrapper around transformers model and</span> +<span class="sd">tokenizer classes. It has several methods such as __init__, tokenize, and train that are </span> +<span class="sd">used for training and fine-tuning the model. The __init__ method takes in several arguments</span> +<span class="sd">such as model_args, tune_strategy, and ds_config, which are used to load the pretrained </span> +<span class="sd">model and tokenizer, and initialize the training settings.</span> + +<span class="sd">The tokenize method is used to tokenize the input text and return the input IDs and attention</span> +<span class="sd">masks that can be fed to the model for training or inference.</span> + +<span class="sd">This class supports different tune_strategy options such as &#39;normal&#39;, &#39;none&#39;, &#39;lora&#39;, and</span> +<span class="sd">&#39;adapter&#39;, which allow for different fine-tuning settings of the model. However, the &#39;lora&#39;</span> +<span class="sd">and &#39;adapter&#39; strategies are not yet implemented.</span> + +<span class="sd">Overall, this class provides a convenient interface for loading and fine-tuning transformer</span> +<span class="sd">models and can be used for various NLP tasks such as language modeling, text classification,</span> +<span class="sd">and question answering.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">import</span> <span class="nn">hashlib</span> +<span class="kn">import</span> <span class="nn">logging</span> +<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span> + +<span class="kn">import</span> <span class="nn">deepspeed</span> + +<span class="kn">from</span> <span class="nn">peft</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">LoraConfig</span><span class="p">,</span> + <span class="n">PeftModel</span><span class="p">,</span> + <span class="n">TaskType</span><span class="p">,</span> + <span class="n">get_peft_config</span><span class="p">,</span> + <span class="n">get_peft_model</span><span class="p">,</span> +<span class="p">)</span> + +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">transformers</span> +<span class="kn">from</span> <span class="nn">transformers.deepspeed</span> <span class="kn">import</span> <span class="n">HfDeepSpeedConfig</span> + +<span class="kn">from</span> <span class="nn">transformers.testing_utils</span> <span class="kn">import</span> <span class="n">CaptureLogger</span> + +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">CONFIG_MAPPING</span><span class="p">,</span> + <span class="n">AutoConfig</span><span class="p">,</span> + <span class="n">AutoTokenizer</span><span class="p">,</span> + <span class="n">AutoModelForCausalLM</span><span class="p">,</span> +<span class="p">)</span> + +<span class="kn">from</span> <span class="nn">lmflow.datasets.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span> +<span class="kn">from</span> <span class="nn">lmflow.models.decoder_model</span> <span class="kn">import</span> <span class="n">DecoderModel</span> +<span class="kn">from</span> <span class="nn">lmflow.models.interfaces.tunable</span> <span class="kn">import</span> <span class="n">Tunable</span> +<span class="kn">from</span> <span class="nn">lmflow.utils.constants</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span><span class="p">,</span> + <span class="n">TEXT2TEXT_DATASET_DESCRIPTION</span><span class="p">,</span> +<span class="p">)</span> + + +<div class="viewcode-block" id="logger"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.logger">[docs]</a><span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span></div> + +<div class="viewcode-block" id="MODELS_SUPPORT_FLASH_ATTENTION"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.MODELS_SUPPORT_FLASH_ATTENTION">[docs]</a><span class="n">MODELS_SUPPORT_FLASH_ATTENTION</span> <span class="o">=</span> <span class="p">[</span> + <span class="s2">&quot;LlamaForCausalLM&quot;</span><span class="p">,</span> + <span class="s2">&quot;GPTNeoForCausalLM&quot;</span><span class="p">,</span> + <span class="s2">&quot;GPT2ForCausalLM&quot;</span><span class="p">,</span> + <span class="s2">&quot;BloomForCausalLM&quot;</span> +<span class="p">]</span></div> + +<span class="n">GPU_SUPPORT_FLASH_ATTENTION</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;A100&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;LlamaForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;GPTNeoForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;GPT2ForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;BloomForCausalLM&quot;</span><span class="p">],</span> + <span class="s2">&quot;A40&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;GPTNeoForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;GPT2ForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;BloomForCausalLM&quot;</span><span class="p">]</span> +<span class="p">}</span> + +<span class="k">try</span><span class="p">:</span> + <span class="kn">import</span> <span class="nn">flash_attn</span> + <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">flash_attn</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> +<div class="viewcode-block" id="GPU_SUPPORT_FLASH_ATTENTION"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.GPU_SUPPORT_FLASH_ATTENTION">[docs]</a> <span class="n">GPU_SUPPORT_FLASH_ATTENTION</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;A100&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;LlamaForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;GPTNeoForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;GPT2ForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;BloomForCausalLM&quot;</span><span class="p">],</span> + <span class="s2">&quot;A40&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;LlamaForCausalLM&quot;</span><span class="p">,</span><span class="s2">&quot;GPTNeoForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;GPT2ForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;BloomForCausalLM&quot;</span><span class="p">]</span> + <span class="p">}</span></div> + <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">flash_attn</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">GPU_SUPPORT_FLASH_ATTENTION</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;A100&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;LlamaForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;GPTNeoForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;GPT2ForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;BloomForCausalLM&quot;</span><span class="p">],</span> + <span class="s2">&quot;A40&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;GPTNeoForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;GPT2ForCausalLM&quot;</span><span class="p">,</span> <span class="s2">&quot;BloomForCausalLM&quot;</span><span class="p">]</span> + <span class="p">}</span> +<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> + <span class="k">pass</span> + +<div class="viewcode-block" id="HFDecoderModel"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel">[docs]</a><span class="k">class</span> <span class="nc">HFDecoderModel</span><span class="p">(</span><span class="n">DecoderModel</span><span class="p">,</span> <span class="n">Tunable</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes a HFDecoderModel instance.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> + +<span class="sd"> model_args : </span> +<span class="sd"> Model arguments such as model name, path, revision, etc.</span> + +<span class="sd"> tune_strategy : str or none, default=&quot;normal&quot;.</span> +<span class="sd"> A string representing the dataset backend. Defaults to &quot;huggingface&quot;.</span> +<span class="sd"> </span> +<span class="sd"> ds_config : </span> +<span class="sd"> Deepspeed configuations.</span> +<span class="sd"> </span> +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments. </span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model_args</span><span class="p">,</span> + <span class="n">tune_strategy</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">,</span> + <span class="n">ds_config</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">device</span><span class="o">=</span><span class="s2">&quot;gpu&quot;</span><span class="p">,</span> + <span class="n">use_accelerator</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes a HFDecoderModel instance.</span> +<span class="sd"> :param model_args: dictionary with model arguments such as model name, path, revision, etc.</span> +<span class="sd"> :param tune_strategy: tuning strategy: normal, none, lora or adapter</span> +<span class="sd"> :param ds_config: deepspeed configuration for distributed training</span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="c1"># See more about loading any type of standard or custom dataset (from</span> + <span class="c1"># files, python dict, pandas DataFrame, etc) at</span> + <span class="c1"># https://huggingface.co/docs/datasets/loading_datasets.html.</span> + + <span class="c1"># Load pretrained model and tokenizer</span> + <span class="c1">#</span> + <span class="c1"># Distributed training: The .from_pretrained methods guarantee that</span> + <span class="c1"># only one local process can concurrently download model &amp; vocab.</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span> <span class="o">=</span> <span class="n">model_args</span> + <span class="n">tokenizer_kwargs</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;cache_dir&quot;</span><span class="p">:</span> <span class="n">model_args</span><span class="o">.</span><span class="n">cache_dir</span><span class="p">,</span> + <span class="s2">&quot;use_fast&quot;</span><span class="p">:</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_fast_tokenizer</span><span class="p">,</span> + <span class="s2">&quot;revision&quot;</span><span class="p">:</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_revision</span><span class="p">,</span> + <span class="s2">&quot;use_auth_token&quot;</span><span class="p">:</span> <span class="kc">True</span> <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_auth_token</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="p">}</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">tokenizer_name</span><span class="p">:</span> + <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">tokenizer_name</span><span class="p">,</span> <span class="o">**</span><span class="n">tokenizer_kwargs</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">:</span> + <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> <span class="o">**</span><span class="n">tokenizer_kwargs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;You are instantiating a new tokenizer from scratch. This is&quot;</span> + <span class="s2">&quot; not supported by this script. You can do it from another&quot;</span> + <span class="s2">&quot; script, save it, and load it from here, using&quot;</span> + <span class="s2">&quot; --tokenizer_name.&quot;</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">tokenizer</span> + + <span class="n">torch_dtype</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">torch_dtype</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">torch_dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span> + <span class="k">else</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">torch</span><span class="p">,</span> <span class="n">model_args</span><span class="o">.</span><span class="n">torch_dtype</span><span class="p">)</span> + <span class="p">)</span> + + <span class="n">config_kwargs</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;cache_dir&quot;</span><span class="p">:</span> <span class="n">model_args</span><span class="o">.</span><span class="n">cache_dir</span><span class="p">,</span> + <span class="s2">&quot;revision&quot;</span><span class="p">:</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_revision</span><span class="p">,</span> + <span class="s2">&quot;use_auth_token&quot;</span><span class="p">:</span> <span class="kc">True</span> <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_auth_token</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="p">}</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">config_name</span><span class="p">:</span> + <span class="n">config</span> <span class="o">=</span> <span class="n">AutoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">config_name</span><span class="p">,</span> <span class="o">**</span><span class="n">config_kwargs</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">:</span> + <span class="n">config</span> <span class="o">=</span> <span class="n">AutoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> <span class="o">**</span><span class="n">config_kwargs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">config</span> <span class="o">=</span> <span class="n">CONFIG_MAPPING</span><span class="p">[</span><span class="n">model_args</span><span class="o">.</span><span class="n">model_type</span><span class="p">]()</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;You are instantiating a new config instance from scratch.&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">config_overrides</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Overriding config: </span><span class="si">{</span><span class="n">model_args</span><span class="o">.</span><span class="n">config_overrides</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">config</span><span class="o">.</span><span class="n">update_from_string</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">config_overrides</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;New config: </span><span class="si">{</span><span class="n">config</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="c1">#position interpolation</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">do_rope_scaling</span><span class="p">:</span> + <span class="k">if</span> <span class="s2">&quot;LlamaForCausalLM&quot;</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">architectures</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">replace_llama_with_condense</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">replace_llama_with_condense</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">rope_pi_ratio</span><span class="p">,</span> <span class="n">model_args</span><span class="o">.</span><span class="n">rope_ntk_ratio</span><span class="p">)</span> + + <span class="c1"># Whether use flash attention</span> + <span class="n">supported_gpu_device</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">for</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">GPU_SUPPORT_FLASH_ATTENTION</span><span class="p">:</span> + <span class="k">if</span> <span class="n">gpu</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_name</span><span class="p">():</span> + <span class="n">supported_gpu_device</span> <span class="o">=</span> <span class="n">gpu</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_flash_attention</span><span class="p">:</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span><span class="n">model_supported</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">architectures</span> + <span class="k">for</span> <span class="n">model_supported</span> <span class="ow">in</span> <span class="n">MODELS_SUPPORT_FLASH_ATTENTION</span><span class="p">):</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Model </span><span class="se">\&quot;</span><span class="si">{</span><span class="n">config</span><span class="o">.</span><span class="n">architectures</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2"> does not support&quot;</span> + <span class="s2">&quot; flash attention, use normal attention layer instead&quot;</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="n">supported_gpu_device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Your decice </span><span class="se">\&quot;</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_name</span><span class="p">()</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2">&quot;</span> + <span class="s2">&quot; does not support flash attention, it will&quot;</span> + <span class="s2">&quot; automatically use normal attention layer&quot;</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + + <span class="n">supported_models</span> <span class="o">=</span> <span class="n">GPU_SUPPORT_FLASH_ATTENTION</span><span class="p">[</span><span class="n">supported_gpu_device</span><span class="p">]</span> + + <span class="n">config</span><span class="o">.</span><span class="n">use_cache</span> <span class="o">=</span> <span class="kc">False</span> + <span class="k">if</span> <span class="s2">&quot;LlamaForCausalLM&quot;</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">architectures</span> <span class="ow">and</span> <span class="s2">&quot;LlamaForCausalLM&quot;</span> <span class="ow">in</span> <span class="n">supported_models</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">lmflow.utils.flash_attention.llama_flash_attention</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">replace_llama_attn_with_flash_attn</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">replace_llama_attn_with_flash_attn</span><span class="p">()</span> + <span class="k">elif</span> <span class="s2">&quot;GPTNeoForCausalLM&quot;</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">architectures</span> <span class="ow">and</span> <span class="s2">&quot;GPTNeoForCausalLM&quot;</span> <span class="ow">in</span> <span class="n">supported_models</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">lmflow.utils.flash_attention.gpt_neo_flash_attention</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">replace_gpt_neo_attn_with_flash_attn</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">replace_gpt_neo_attn_with_flash_attn</span><span class="p">()</span> + <span class="k">elif</span> <span class="s2">&quot;GPT2ForCausalLM&quot;</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">architectures</span> <span class="ow">and</span> <span class="s2">&quot;GPT2ForCausalLM&quot;</span> <span class="ow">in</span> <span class="n">supported_models</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">lmflow.utils.flash_attention.gpt2_flash_attention</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">replace_gpt2_attn_with_flash_attn</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">replace_gpt2_attn_with_flash_attn</span><span class="p">()</span> + <span class="k">elif</span> <span class="s2">&quot;BloomForCausalLM&quot;</span> <span class="ow">in</span> <span class="n">config</span><span class="o">.</span><span class="n">architectures</span> <span class="ow">and</span> <span class="s2">&quot;BloomForCausalLM&quot;</span> <span class="ow">in</span> <span class="n">supported_models</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">lmflow.utils.flash_attention.bloom_flash_attention</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">replace_bloom_attn_with_flash_attn</span> + <span class="p">)</span> + <span class="n">replace_bloom_attn_with_flash_attn</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Model </span><span class="se">\&quot;</span><span class="si">{</span><span class="n">config</span><span class="o">.</span><span class="n">architectures</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2"> with GPU </span><span class="si">{</span><span class="n">supported_gpu_device</span><span class="si">}</span><span class="s2"> does not support&quot;</span> + <span class="s2">&quot; flash attention, use normal attention layer instead&quot;</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">tune_strategy</span> <span class="o">==</span> <span class="s1">&#39;normal&#39;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> + <span class="n">from_tf</span><span class="o">=</span><span class="nb">bool</span><span class="p">(</span><span class="s2">&quot;.ckpt&quot;</span> <span class="ow">in</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">),</span> + <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> + <span class="n">cache_dir</span><span class="o">=</span><span class="n">model_args</span><span class="o">.</span><span class="n">cache_dir</span><span class="p">,</span> + <span class="n">revision</span><span class="o">=</span><span class="n">model_args</span><span class="o">.</span><span class="n">model_revision</span><span class="p">,</span> + <span class="n">use_auth_token</span><span class="o">=</span><span class="kc">True</span> <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_auth_token</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch_dtype</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_config</span><span class="p">(</span><span class="n">config</span><span class="p">)</span> + <span class="n">n_params</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="nb">dict</span><span class="p">((</span><span class="n">p</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">(),</span> <span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">())</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Training new model from scratch - Total size=</span><span class="si">{</span><span class="n">n_params</span><span class="o">/</span><span class="mi">2</span><span class="o">**</span><span class="mi">20</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">M params&quot;</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model_full</span> <span class="o">=</span> <span class="n">model</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_lora</span><span class="p">:</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">lora_target_modules</span><span class="p">:</span> + <span class="n">lora_target_modules</span> <span class="o">=</span> <span class="n">model_args</span><span class="o">.</span><span class="n">lora_target_modules</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">lora_target_modules</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">peft_config</span> <span class="o">=</span> <span class="n">LoraConfig</span><span class="p">(</span> + <span class="n">task_type</span><span class="o">=</span><span class="n">TaskType</span><span class="o">.</span><span class="n">CAUSAL_LM</span><span class="p">,</span> + <span class="n">inference_mode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">r</span><span class="o">=</span><span class="n">model_args</span><span class="o">.</span><span class="n">lora_r</span><span class="p">,</span> + <span class="n">lora_alpha</span><span class="o">=</span><span class="n">model_args</span><span class="o">.</span><span class="n">lora_alpha</span><span class="p">,</span> + <span class="n">lora_dropout</span><span class="o">=</span><span class="n">model_args</span><span class="o">.</span><span class="n">lora_dropout</span><span class="p">,</span> + <span class="n">target_modules</span><span class="o">=</span><span class="n">lora_target_modules</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">get_peft_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">peft_config</span><span class="p">)</span> + <span class="n">model</span><span class="o">.</span><span class="n">print_trainable_parameters</span><span class="p">()</span> + + <span class="c1"># We resize the embeddings only when necessary to avoid index errors.</span> + <span class="c1"># If you are creating a model from scratch on a small vocab and want a</span> + <span class="c1"># smaller embedding size, remove this test.</span> + <span class="k">with</span> <span class="n">deepspeed</span><span class="o">.</span><span class="n">zero</span><span class="o">.</span><span class="n">GatheredParameters</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">get_input_embeddings</span><span class="p">()</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">modifier_rank</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="n">weights</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_input_embeddings</span><span class="p">()</span><span class="o">.</span><span class="n">weight</span> + <span class="n">embedding_size</span> <span class="o">=</span> <span class="n">weights</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">embedding_size</span><span class="p">:</span> + <span class="n">model</span><span class="o">.</span><span class="n">resize_token_embeddings</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">))</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">config</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">model</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tune_strategy</span> <span class="o">=</span> <span class="n">tune_strategy</span> + + <span class="k">elif</span> <span class="n">tune_strategy</span> <span class="o">==</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">use_accelerator</span><span class="p">:</span> + <span class="n">peft_model_id</span> <span class="o">=</span> <span class="n">model_args</span><span class="o">.</span><span class="n">lora_model_path</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> + <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> + <span class="n">device_map</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span> + <span class="n">offload_folder</span><span class="o">=</span><span class="s2">&quot;offload&quot;</span><span class="p">,</span> + <span class="n">offload_state_dict</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch_dtype</span><span class="p">,</span> + <span class="n">load_in_8bit</span> <span class="o">=</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_int8</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">peft_model_id</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">PeftModel</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="p">,</span> + <span class="n">peft_model_id</span><span class="p">,</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">padding_side</span> <span class="o">=</span> <span class="s2">&quot;left&quot;</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">dschf</span> <span class="o">=</span> <span class="n">HfDeepSpeedConfig</span><span class="p">(</span><span class="n">ds_config</span><span class="p">)</span> + <span class="n">peft_model_id</span> <span class="o">=</span> <span class="n">model_args</span><span class="o">.</span><span class="n">lora_model_path</span> + <span class="c1"># NOTE: Currently offload is not supported by llama</span> + <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;llama&quot;</span> <span class="ow">and</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_ram_optimized_load</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;llama does not support RAM optimized load. Automatically&quot;</span> + <span class="s2">&quot; use original load instead.&quot;</span> + <span class="p">)</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">use_ram_optimized_load</span> <span class="o">=</span> <span class="kc">False</span> + + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_ram_optimized_load</span> <span class="ow">and</span> <span class="n">peft_model_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">try</span><span class="p">:</span> + <span class="c1"># RAM-optimized load</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> + <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> + <span class="n">device_map</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span> + <span class="n">offload_folder</span><span class="o">=</span><span class="s2">&quot;offload&quot;</span><span class="p">,</span> + <span class="n">offload_state_dict</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch_dtype</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">except</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;Failed to use RAM optimized load. Automatically&quot;</span> + <span class="s2">&quot; use original load instead.&quot;</span> + <span class="p">)</span> + <span class="c1"># Normal load</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> + <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> + <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch_dtype</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">peft_model_id</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;LoRA does not support RAM optimized load currently.&quot;</span> + <span class="s2">&quot; Automatically use original load instead.&quot;</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> + <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span> + <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch_dtype</span><span class="p">,</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model_full</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> + <span class="k">if</span> <span class="n">peft_model_id</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">PeftModel</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="p">,</span> <span class="n">peft_model_id</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">padding_side</span> <span class="o">=</span> <span class="s2">&quot;left&quot;</span> <span class="c1">#necessary for llama, gpt2 and other decoder models</span> + + <span class="k">if</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span><span class="p">:</span> + <span class="n">deepspeed</span><span class="o">.</span><span class="n">init_distributed</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">ds_engine</span> <span class="o">=</span> <span class="n">deepspeed</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="p">,</span> <span class="n">config_params</span><span class="o">=</span><span class="n">ds_config</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> + <span class="bp">self</span><span class="o">.</span><span class="n">ds_engine</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> + + <span class="k">elif</span> <span class="n">tune_strategy</span> <span class="o">==</span> <span class="s1">&#39;adapter&#39;</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;adapter tune strategy not implemented&#39;</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">eos_token_id</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span> + + +<div class="viewcode-block" id="HFDecoderModel.tokenize"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.tokenize">[docs]</a> <span class="k">def</span> <span class="nf">tokenize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">add_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Tokenize the full dataset.</span> +<span class="sd"> </span> +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> dataset : lmflow.datasets.Dataset.</span> + +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments. </span> +<span class="sd"> </span> +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> tokenized_datasets :</span> +<span class="sd"> The tokenized dataset, without any leading or trailing special</span> +<span class="sd"> tokens (normally they are Begin-Of-Sentence or End-Of-Sentence</span> +<span class="sd"> tokens).</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># Preprocessing the datasets.</span> + <span class="c1"># First we tokenize all the texts.</span> + <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_backend</span><span class="p">()</span> <span class="o">!=</span> <span class="s2">&quot;huggingface&quot;</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="s2">&quot;tokenization of datasets with non-huggingface backend are&quot;</span> + <span class="s2">&quot;not supported yet&quot;</span> + <span class="p">)</span> + + <span class="n">dataset_type</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_type</span><span class="p">()</span> + + <span class="c1"># Requires three types of information for tokenizing different datasets</span> + <span class="c1"># 1) Which fields require tokenization, e.g.</span> + <span class="c1"># &quot;text2float&quot;: &quot;text&quot;, but not &quot;float&quot;</span> + <span class="c1"># &quot;text2text&quot;: both &quot;input&quot; and &quot;output&quot;</span> + <span class="c1"># 2) How will there tokenized sequence concatenated together, e.g.</span> + <span class="c1"># &quot;text_only&quot;: &quot;text&quot; -&gt; &quot;text&quot;</span> + <span class="c1"># &quot;text2text&quot;: &quot;input&quot;, &quot;output&quot; -&gt; &quot;input&quot; + &quot;output&quot;</span> + <span class="c1"># 3) Which fields require loss in final computation, e.g.</span> + <span class="c1"># &quot;text_only&quot;: &quot;text&quot;</span> + <span class="c1"># &quot;text2text&quot;: &quot;output&quot; only</span> + <span class="n">tokenized_column_order</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># Handles 1) and 2)</span> + <span class="n">label_columns</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># Handles 3)</span> + <span class="k">if</span> <span class="n">dataset_type</span> <span class="o">==</span> <span class="s2">&quot;text_only&quot;</span><span class="p">:</span> + <span class="n">tokenized_column_order</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;text&quot;</span><span class="p">]</span> + <span class="n">label_columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;text&quot;</span><span class="p">]</span> + <span class="k">elif</span> <span class="n">dataset_type</span> <span class="o">==</span> <span class="s2">&quot;text2text&quot;</span><span class="p">:</span> + <span class="n">tokenized_column_order</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">,</span> <span class="s2">&quot;output&quot;</span><span class="p">]</span> + <span class="n">label_columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">]</span> + <span class="n">add_special_tokens</span> <span class="o">=</span> <span class="kc">False</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;dataset type </span><span class="se">\&quot;</span><span class="si">{</span><span class="n">dataset_type</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2"> is not supported, currently&quot;</span> + <span class="s2">&quot; only support following data types:</span><span class="se">\n</span><span class="s2">&quot;</span> + <span class="sa">f</span><span class="s2">&quot; 1) </span><span class="si">{</span><span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span> + <span class="sa">f</span><span class="s2">&quot; 2) </span><span class="si">{</span><span class="n">TEXT2TEXT_DATASET_DESCRIPTION</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span> + <span class="p">)</span> + + <span class="n">model_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span> + <span class="n">raw_datasets</span> <span class="o">=</span> <span class="n">dataset</span> + <span class="n">hf_raw_datasets</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_backend_dataset</span><span class="p">()</span> + <span class="n">column_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">hf_raw_datasets</span><span class="o">.</span><span class="n">features</span><span class="p">)</span> + + <span class="c1"># since this will be pickled to avoid _LazyModule error in Hasher force</span> + <span class="c1"># logger loading before tokenize_function</span> + <span class="n">tok_logger</span> <span class="o">=</span> <span class="n">transformers</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">logging</span><span class="o">.</span><span class="n">get_logger</span><span class="p">(</span><span class="s2">&quot;transformers.tokenization_utils_base&quot;</span><span class="p">)</span> + + <span class="k">def</span> <span class="nf">tokenize_function</span><span class="p">(</span><span class="n">examples</span><span class="p">):</span> + <span class="n">num_example</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">examples</span><span class="p">[</span><span class="n">column_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span> + <span class="n">token_dict</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_example</span><span class="p">)],</span> + <span class="s2">&quot;attention_mask&quot;</span><span class="p">:</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_example</span><span class="p">)],</span> + <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_example</span><span class="p">)],</span> + <span class="p">}</span> + <span class="k">with</span> <span class="n">CaptureLogger</span><span class="p">(</span><span class="n">tok_logger</span><span class="p">)</span> <span class="k">as</span> <span class="n">cl</span><span class="p">:</span> + <span class="k">for</span> <span class="n">column_name</span> <span class="ow">in</span> <span class="n">tokenized_column_order</span><span class="p">:</span> + <span class="n">encoding</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">(</span> + <span class="n">examples</span><span class="p">[</span><span class="n">column_name</span><span class="p">],</span> + <span class="n">add_special_tokens</span><span class="o">=</span><span class="n">add_special_tokens</span><span class="p">,</span> + <span class="n">truncation</span><span class="o">=</span><span class="kc">True</span> <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_lora</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">column_name</span> <span class="ow">in</span> <span class="n">label_columns</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span> + <span class="p">[</span><span class="o">-</span><span class="mi">100</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_example</span><span class="p">)</span> + <span class="p">]</span> + + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_example</span><span class="p">):</span> + <span class="n">token_dict</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> + <span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> + <span class="p">)</span> + <span class="n">token_dict</span><span class="p">[</span><span class="s2">&quot;attention_mask&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span> + <span class="n">encoding</span><span class="p">[</span><span class="s2">&quot;attention_mask&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> + <span class="p">)</span> + <span class="n">token_dict</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> + + <span class="c1"># clm input could be much much longer than block_size</span> + <span class="k">if</span> <span class="s2">&quot;Token indices sequence length is longer than the&quot;</span> <span class="ow">in</span> <span class="n">cl</span><span class="o">.</span><span class="n">out</span><span class="p">:</span> + <span class="n">tok_logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits&quot;</span> + <span class="s2">&quot; before being passed to the model.&quot;</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">token_dict</span> + + <span class="n">data_args</span> <span class="o">=</span> <span class="n">raw_datasets</span><span class="o">.</span><span class="n">get_data_args</span><span class="p">()</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">data_args</span><span class="o">.</span><span class="n">streaming</span><span class="p">:</span> + <span class="n">fingerprint</span> <span class="o">=</span> <span class="n">raw_datasets</span><span class="o">.</span><span class="n">get_fingerprint</span><span class="p">()</span> + <span class="n">new_fingerprint</span> <span class="o">=</span> <span class="n">hashlib</span><span class="o">.</span><span class="n">md5</span><span class="p">(</span> + <span class="p">(</span><span class="n">fingerprint</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">))</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span> + <span class="p">)</span><span class="o">.</span><span class="n">hexdigest</span><span class="p">()</span> + + <span class="n">tokenized_datasets</span> <span class="o">=</span> <span class="n">raw_datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> + <span class="n">tokenize_function</span><span class="p">,</span> + <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">num_proc</span><span class="o">=</span><span class="n">data_args</span><span class="o">.</span><span class="n">preprocessing_num_workers</span><span class="p">,</span> + <span class="n">remove_columns</span><span class="o">=</span><span class="n">column_names</span><span class="p">,</span> + <span class="n">load_from_cache_file</span><span class="o">=</span><span class="ow">not</span> <span class="n">data_args</span><span class="o">.</span><span class="n">overwrite_cache</span><span class="p">,</span> + <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Running tokenizer on dataset&quot;</span><span class="p">,</span> + <span class="n">new_fingerprint</span><span class="o">=</span><span class="n">new_fingerprint</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">tokenized_datasets</span> <span class="o">=</span> <span class="n">raw_datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> + <span class="n">tokenize_function</span><span class="p">,</span> + <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">remove_columns</span><span class="o">=</span><span class="n">column_names</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">tokenized_datasets</span></div> + + +<div class="viewcode-block" id="HFDecoderModel.encode"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.encode">[docs]</a> <span class="k">def</span> <span class="nf">encode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]],</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform encoding process of the tokenizer.</span> +<span class="sd"> </span> +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> inputs : str or list.</span> +<span class="sd"> The text sequence.</span> +<span class="sd"> </span> +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments. </span> +<span class="sd"> </span> +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> outputs :</span> +<span class="sd"> if string input,return the tokenized inputs.</span> +<span class="sd"> &quot;Hello,world!&quot;-&gt; [101, 7592, 1010, 2088, 102]</span> +<span class="sd"> if batch input,return {input_ids,attention_mask,token_type_ids}</span> +<span class="sd"> [&quot;Hello,world!&quot;,&quot;Hello!&quot;]-&gt; {&#39;input_ids&#39;: tensor([[ 101, 7592, 1010, 2088, 102],...),&#39;attention_mask&#39;: tensor([[1, 1, 1, 1, 1],[0,0,1,1,1]])}</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">(</span><span class="n">text</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="c1">#batch encode,will automatically do left padding</span> + <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">text</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;type &quot;</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span><span class="si">}</span><span class="s1">&quot; cannot be encoded&#39;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="HFDecoderModel.decode"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.decode">[docs]</a> <span class="k">def</span> <span class="nf">decode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform decoding process of the tokenizer.</span> +<span class="sd"> </span> +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> inputs : list or tensor.</span> +<span class="sd"> The token sequence.</span> +<span class="sd"> </span> +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments. </span> +<span class="sd"> </span> +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> outputs :</span> +<span class="sd"> The text decoded from the token inputs.</span> +<span class="sd"> if batch input,return the list of text</span> +<span class="sd"> [[101, 7592, 1010, 2088, 102],[101, 7592, 1010, 2088, 102]]-&gt; [&quot;Hello,world!&quot;,&quot;Hello,world!&quot;</span> +<span class="sd"> if single input,return the text</span> +<span class="sd"> [101, 7592, 1010, 2088, 102]-&gt; &quot;Hello,world!&quot;</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">List</span><span class="p">):</span> + <span class="nb">input</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">batch_decode</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="c1">#batch_decode</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># Can be list of ints or a Tensor</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="HFDecoderModel.inference"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.inference">[docs]</a> <span class="k">def</span> <span class="nf">inference</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">use_accelerator</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform generation process of the model.</span> +<span class="sd"> </span> +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> inputs :</span> +<span class="sd"> The sequence used as a prompt for the generation or as model inputs to the model.</span> +<span class="sd"> </span> +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments. </span> +<span class="sd"> </span> +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> outputs :</span> +<span class="sd"> The generated sequence output </span> +<span class="sd"> &quot;&quot;&quot;</span> + + + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> + <span class="k">if</span> <span class="n">use_accelerator</span><span class="p">:</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> + <span class="n">input_ids</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> + <span class="n">pad_token_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span><span class="p">:</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ds_engine</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> + <span class="n">input_ids</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> + <span class="n">synced_gpus</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">pad_token_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;cpu&quot;</span><span class="p">:</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> + <span class="n">input_ids</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> + <span class="n">synced_gpus</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">pad_token_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;device </span><span class="se">\&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2"> is not supported&quot;</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">outputs</span></div> + + +<div class="viewcode-block" id="HFDecoderModel.merge_lora_weights"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.merge_lora_weights">[docs]</a> <span class="k">def</span> <span class="nf">merge_lora_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span><span class="o">.</span><span class="n">use_lora</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()</span><span class="o">.</span><span class="n">merge_and_unload</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;LoRA training is NOT enabled. Merging LoRA weights is not applicable.&quot;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="HFDecoderModel.save"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.save">[docs]</a> <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">dir</span><span class="p">,</span> <span class="n">save_full_model</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform generation process of the model.</span> +<span class="sd"> </span> +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> dir :</span> +<span class="sd"> The directory to save model and tokenizer</span> +<span class="sd"> </span> +<span class="sd"> save_full_model : Optional.</span> +<span class="sd"> Whether to save full model.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments. </span> +<span class="sd"> </span> +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> outputs :</span> +<span class="sd"> The generated sequence output </span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">get_tokenizer</span><span class="p">()</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="nb">dir</span><span class="p">)</span> + <span class="k">if</span> <span class="n">save_full_model</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span><span class="o">.</span><span class="n">use_lora</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model_full</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="nb">dir</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="nb">dir</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="HFDecoderModel.get_max_length"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.get_max_length">[docs]</a> <span class="k">def</span> <span class="nf">get_max_length</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Return max acceptable input length in terms of tokens.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">model_max_length</span></div> + + +<div class="viewcode-block" id="HFDecoderModel.get_tokenizer"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.get_tokenizer">[docs]</a> <span class="k">def</span> <span class="nf">get_tokenizer</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Return the tokenizer of the model.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span></div> + + +<div class="viewcode-block" id="HFDecoderModel.get_backend_model"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.get_backend_model">[docs]</a> <span class="k">def</span> <span class="nf">get_backend_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Return the backend model.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer 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HFDecoderModel which is a wrapper around transformers model and</span> +<span class="sd">tokenizer classes. It has several methods such as __init__, tokenize, and train that are</span> +<span class="sd">used for training and fine-tuning the model. The __init__ method takes in several arguments</span> +<span class="sd">such as model_args, tune_strategy, and ds_config, which are used to load the pretrained</span> +<span class="sd">model and tokenizer, and initialize the training settings.</span> + +<span class="sd">The tokenize method is used to tokenize the input text and return the input IDs and attention</span> +<span class="sd">masks that can be fed to the model for training or inference.</span> + +<span class="sd">This class supports different tune_strategy options such as &#39;normal&#39;, &#39;none&#39;, &#39;lora&#39;, and</span> +<span class="sd">&#39;adapter&#39;, which allow for different fine-tuning settings of the model. However, the &#39;lora&#39;</span> +<span class="sd">and &#39;adapter&#39; strategies are not yet implemented.</span> + +<span class="sd">Overall, this class provides a convenient interface for loading and fine-tuning transformer</span> +<span class="sd">models and can be used for various NLP tasks such as language modeling, text classification,</span> +<span class="sd">and question answering.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">import</span> <span class="nn">copy</span> +<span class="kn">import</span> <span class="nn">logging</span> +<span class="kn">import</span> <span class="nn">time</span> +<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span> + +<span class="kn">import</span> <span class="nn">deepspeed</span> + +<span class="kn">from</span> <span class="nn">peft</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">LoraConfig</span><span class="p">,</span> + <span class="n">PeftModel</span><span class="p">,</span> + <span class="n">TaskType</span><span class="p">,</span> + <span class="n">get_peft_config</span><span class="p">,</span> + <span class="n">get_peft_model</span><span class="p">,</span> +<span class="p">)</span> + +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">transformers</span> +<span class="kn">from</span> <span class="nn">transformers.deepspeed</span> <span class="kn">import</span> <span class="n">HfDeepSpeedConfig</span> + +<span class="kn">from</span> <span class="nn">transformers.testing_utils</span> <span class="kn">import</span> <span class="n">CaptureLogger</span> + +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">CONFIG_MAPPING</span><span class="p">,</span> + <span class="n">AutoConfig</span><span class="p">,</span> + <span class="n">AutoTokenizer</span><span class="p">,</span> + <span class="n">AutoModelForSeq2SeqLM</span><span class="p">,</span> + <span class="n">AutoModelForVision2Seq</span><span class="p">,</span> + <span class="n">AutoModel</span><span class="p">,</span> + <span class="n">AutoProcessor</span><span class="p">,</span> + <span class="n">LlamaTokenizer</span> +<span class="p">)</span> + +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="p">(</span><span class="n">Blip2VisionConfig</span><span class="p">,</span> + <span class="n">Blip2QFormerConfig</span><span class="p">,</span> + <span class="n">Blip2Config</span><span class="p">,</span> + <span class="n">LlamaConfig</span><span class="p">)</span> + +<span class="kn">from</span> <span class="nn">lmflow.datasets.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span> +<span class="kn">from</span> <span class="nn">lmflow.models.encoder_decoder_model</span> <span class="kn">import</span> <span class="n">EncoderDecoderModel</span> +<span class="kn">from</span> <span class="nn">lmflow.models.interfaces.tunable</span> <span class="kn">import</span> <span class="n">Tunable</span> +<span class="kn">from</span> <span class="nn">lmflow.models.vision2seq_model</span> <span class="kn">import</span> <span class="n">CustomAutoVision2SeqModel</span> + +<div class="viewcode-block" id="logger"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.logger">[docs]</a><span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="HFEncoderDecoderModel"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel">[docs]</a><span class="k">class</span> <span class="nc">HFEncoderDecoderModel</span><span class="p">(</span><span class="n">EncoderDecoderModel</span><span class="p">,</span> <span class="n">Tunable</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes a HFEncoderDecoderModel instance.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> + +<span class="sd"> model_args :</span> +<span class="sd"> Model arguments such as model name, path, revision, etc.</span> + +<span class="sd"> tune_strategy : str or none, default=&quot;normal&quot;.</span> +<span class="sd"> A string representing the dataset backend. Defaults to &quot;huggingface&quot;.</span> + +<span class="sd"> ds_config :</span> +<span class="sd"> Deepspeed configuations.</span> + +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> + +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model_args</span><span class="p">,</span> + <span class="n">tune_strategy</span><span class="o">=</span><span class="s1">&#39;normal&#39;</span><span class="p">,</span> + <span class="n">ds_config</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">device</span><span class="o">=</span><span class="s2">&quot;gpu&quot;</span><span class="p">,</span> + <span class="n">use_accelerator</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">custom_model</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes a HFDecoderModel instance.</span> +<span class="sd"> :param model_args: dictionary with model arguments such as model name, path, revision, etc.</span> +<span class="sd"> :param tune_strategy: tuning strategy: normal, none, lora or adapter</span> +<span class="sd"> :param ds_config: deepspeed configuration for distributed training</span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="c1"># See more about loading any type of standard or custom dataset (from</span> + <span class="c1"># files, python dict, pandas DataFrame, etc) at</span> + <span class="c1"># https://huggingface.co/docs/datasets/loading_datasets.html.</span> + + <span class="c1"># Load pretrained model and tokenizer</span> + <span class="c1">#</span> + <span class="c1"># Distributed training: The .from_pretrained methods guarantee that</span> + <span class="c1"># only one local process can concurrently download model &amp; vocab.</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span> + + <span class="k">if</span> <span class="n">tune_strategy</span> <span class="o">==</span> <span class="s1">&#39;normal&#39;</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;tune_strategy </span><span class="se">\&quot;</span><span class="si">{</span><span class="n">tune_strategy</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2"> is not supported&quot;</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="n">tune_strategy</span> <span class="o">==</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">use_accelerator</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Currently encoder2decoder model is not supported with accelerator&quot;</span> + <span class="p">)</span> + <span class="n">dschf</span> <span class="o">=</span> <span class="n">HfDeepSpeedConfig</span><span class="p">(</span><span class="n">ds_config</span><span class="p">)</span> + <span class="n">peft_model_id</span> <span class="o">=</span> <span class="n">model_args</span><span class="o">.</span><span class="n">lora_model_path</span> + <span class="c1"># NOTE: Currently offload is not supported by llama</span> + <span class="k">if</span> <span class="s2">&quot;llama&quot;</span> <span class="ow">in</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span> <span class="ow">and</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_ram_optimized_load</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;llama does not support RAM optimized load. Automatically&quot;</span> + <span class="s2">&quot; use original load instead.&quot;</span> + <span class="p">)</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">use_ram_optimized_load</span> <span class="o">=</span> <span class="kc">False</span> + + <span class="c1"># get model register</span> + <span class="bp">self</span><span class="o">.</span><span class="n">arch_type</span> <span class="o">=</span> <span class="n">model_args</span><span class="o">.</span><span class="n">arch_type</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;encoder_decoder&quot;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span> <span class="o">==</span> <span class="s1">&#39;THUDM/chatglm-6b&#39;</span><span class="p">:</span> + <span class="n">model_register</span> <span class="o">=</span> <span class="n">AutoModel</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">model_register</span> <span class="o">=</span> <span class="n">AutoModelForSeq2SeqLM</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;vision_encoder_decoder&quot;</span><span class="p">:</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">custom_model</span><span class="p">:</span> + <span class="n">model_register</span> <span class="o">=</span> <span class="n">AutoModelForVision2Seq</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">model_register</span> <span class="o">=</span> <span class="n">CustomAutoVision2SeqModel</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">custom_model</span><span class="p">:</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span> <span class="o">==</span> <span class="s1">&#39;THUDM/chatglm-6b&#39;</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">model_register</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> <span class="n">trust_remote_code</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + + <span class="k">elif</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_ram_optimized_load</span> <span class="ow">and</span> <span class="n">peft_model_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">try</span><span class="p">:</span> + <span class="c1"># RAM-optimized load</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">model_register</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> + <span class="n">device_map</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span><span class="p">,</span> + <span class="n">offload_folder</span><span class="o">=</span><span class="s2">&quot;offload&quot;</span><span class="p">,</span> + <span class="n">offload_state_dict</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">except</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;Failed to use RAM optimized load. Automatically&quot;</span> + <span class="s2">&quot; use original load instead.&quot;</span> + <span class="p">)</span> + <span class="c1"># Normal load</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">model_register</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">peft_model_id</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;LoRA does not support RAM optimized load currently.&quot;</span> + <span class="s2">&quot; Automatically use original load instead.&quot;</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">model_register</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> + <span class="p">)</span> + <span class="c1"># else:</span> + <span class="c1"># self.backend_model = model_register.from_pretrained(</span> + <span class="c1"># model_args.model_name_or_path)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">CustomAutoVision2SeqModel</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">)</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">llm_model_name_or_path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">text_config</span> <span class="o">=</span> <span class="n">LlamaConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">llm_model_name_or_path</span><span class="p">)</span> + <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">text_config</span> <span class="o">=</span> <span class="n">text_config</span> + <span class="n">model</span><span class="o">.</span><span class="n">language_model_from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">llm_model_name_or_path</span><span class="p">,</span> + <span class="n">low_resource</span><span class="o">=</span><span class="n">model_args</span><span class="o">.</span><span class="n">low_resource</span><span class="p">)</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">checkpoint_path</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span> + <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="c1"># model = CustomAutoVision2SeqModel.from_pretrained(</span> + <span class="c1"># &quot;/home/qlianab/checkpoints/pretrained_weights/minigpt4-lmflow-vicuna-7b-low_resource/&quot;）</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">model</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;encoder_decoder&quot;</span><span class="p">:</span> + <span class="n">tokenizer_register</span> <span class="o">=</span> <span class="n">AutoTokenizer</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;vision_encoder_decoder&quot;</span><span class="p">:</span> + <span class="n">tokenizer_register</span> <span class="o">=</span> <span class="n">AutoProcessor</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">tokenizer_register</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> <span class="n">trust_remote_code</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">llm_model_name_or_path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">LlamaTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">llm_model_name_or_path</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model_full</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> + <span class="k">if</span> <span class="n">peft_model_id</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span> <span class="o">=</span> <span class="n">PeftModel</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="p">,</span> <span class="n">peft_model_id</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span><span class="p">:</span> + <span class="n">deepspeed</span><span class="o">.</span><span class="n">init_distributed</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">ds_engine</span> <span class="o">=</span> <span class="n">deepspeed</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="p">,</span> <span class="n">config_params</span><span class="o">=</span><span class="n">ds_config</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> + <span class="bp">self</span><span class="o">.</span><span class="n">ds_engine</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">padding_side</span> <span class="o">=</span> <span class="s2">&quot;left&quot;</span> <span class="c1">#necessary for auto-gressive inference</span> + + <span class="k">elif</span> <span class="n">tune_strategy</span> <span class="o">==</span> <span class="s1">&#39;adapter&#39;</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;adapter tune strategy not implemented&#39;</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;encoder_decoder&quot;</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">eos_token_id</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span> + +<div class="viewcode-block" id="HFEncoderDecoderModel.tokenize"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.tokenize">[docs]</a> <span class="k">def</span> <span class="nf">tokenize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Tokenize the full dataset.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> dataset :</span> +<span class="sd"> Text dataset.</span> + +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> + +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> + +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> tokenized_datasets :</span> +<span class="sd"> The tokenized dataset.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;tokenize not implemented&#39;</span><span class="p">)</span></div> + +<div class="viewcode-block" id="HFEncoderDecoderModel.encode"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.encode">[docs]</a> <span class="k">def</span> <span class="nf">encode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]],</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform encoding process of the tokenizer.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> inputs : str or list.</span> +<span class="sd"> The text sequence.</span> + +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> + +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> + +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> outputs :</span> +<span class="sd"> The tokenized inputs.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="c1"># TODO refactor the input type to make it elegant.</span> + <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> + <span class="n">tokens</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">return</span> <span class="n">tokens</span> + <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">(</span><span class="n">text</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="c1">#batch encode,will automatically do left padding</span> + <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">text</span><span class="o">=</span><span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;type &quot;</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span><span class="si">}</span><span class="s1">&quot; cannot be encoded&#39;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="HFEncoderDecoderModel.decode"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.decode">[docs]</a> <span class="k">def</span> <span class="nf">decode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span> <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform decoding process of the tokenizer.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> inputs : list.</span> +<span class="sd"> The token sequence.</span> + +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> + +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> + +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> outputs :</span> +<span class="sd"> The text decoded from the token inputs.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">List</span><span class="p">):</span> + <span class="nb">input</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">input</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">batch_decode</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span><span class="c1">#batch_decode</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># Can be list of ints or a Tensor</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="HFEncoderDecoderModel.inference"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.inference">[docs]</a> <span class="k">def</span> <span class="nf">inference</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform generation process of the model.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> inputs :</span> +<span class="sd"> The sequence used as a prompt for the generation or as model inputs to the model.</span> + +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> + +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> + +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> outputs :</span> +<span class="sd"> The generated sequence output</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># current_time = time.strftime(&quot;%H:%M:%S&quot;, time.localtime())</span> + <span class="c1"># print(f&quot;{current_time}: model.inference: start&quot;, flush=True)</span> + + <span class="c1"># TODO need to discuss how to handle pad_token_id</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;encoder_decoder&quot;</span><span class="p">:</span> + <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">pad_token_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span><span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">arch_type</span> <span class="o">==</span> <span class="s2">&quot;vision_encoder_decoder&quot;</span><span class="p">:</span> + <span class="c1"># TODO disucss how to modify the interface to remove this part.</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> + <span class="n">input_ids</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;input_ids&#39;</span><span class="p">)</span> + <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">)</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">input_ids</span> + + <span class="c1"># current_time = time.strftime(&quot;%H:%M:%S&quot;, time.localtime())</span> + <span class="c1"># print(f&quot;{current_time}: model.inference: kwargs update end&quot;, flush=True)</span> + + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span><span class="p">:</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ds_engine</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> + <span class="n">input_ids</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> + <span class="n">synced_gpus</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;cpu&quot;</span><span class="p">:</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> + <span class="n">input_ids</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> + <span class="n">synced_gpus</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;device </span><span class="se">\&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2"> is not supported&quot;</span> + <span class="p">)</span> + + <span class="c1"># current_time = time.strftime(&quot;%H:%M:%S&quot;, time.localtime())</span> + <span class="c1"># print(f&quot;{current_time}: model.inference: end&quot;, flush=True)</span> + + <span class="k">return</span> <span class="n">outputs</span></div> + + +<div class="viewcode-block" id="HFEncoderDecoderModel.merge_lora_weights"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.merge_lora_weights">[docs]</a> <span class="k">def</span> <span class="nf">merge_lora_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span><span class="o">.</span><span class="n">use_lora</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()</span><span class="o">.</span><span class="n">merge_and_unload</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;LoRA training is NOT enabled. Merging LoRA weights is not applicable.&quot;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="HFEncoderDecoderModel.save"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.save">[docs]</a> <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">dir</span><span class="p">,</span> <span class="n">save_full_model</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform generation process of the model.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> dir :</span> +<span class="sd"> The directory to save model and tokenizer</span> + +<span class="sd"> save_full_model : Optional.</span> +<span class="sd"> Whether to save full model.</span> + +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> + +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> outputs :</span> +<span class="sd"> The generated sequence output</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">get_tokenizer</span><span class="p">()</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="nb">dir</span><span class="p">)</span> + <span class="k">if</span> <span class="n">save_full_model</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span><span class="o">.</span><span class="n">use_lora</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backend_model_full</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="nb">dir</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="nb">dir</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="HFEncoderDecoderModel.get_max_length"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_max_length">[docs]</a> <span class="k">def</span> <span class="nf">get_max_length</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Return max acceptable input length in terms of tokens.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="s2">&quot;tokenizer&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">model_max_length</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># for the multi-modality processor,</span> + <span class="c1"># the max length is stored in the inner text tokenizer</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">model_max_length</span></div> + + +<div class="viewcode-block" id="HFEncoderDecoderModel.get_tokenizer"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_tokenizer">[docs]</a> <span class="k">def</span> <span class="nf">get_tokenizer</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Return the tokenizer of the model.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span></div> + + +<div class="viewcode-block" id="HFEncoderDecoderModel.get_backend_model"><a class="viewcode-back" href="../../../autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_backend_model">[docs]</a> <span class="k">def</span> <span class="nf">get_backend_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Return the backend model.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">backend_model</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script 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+ + +<div class="viewcode-block" id="Tunable"><a class="viewcode-back" href="../../../../autoapi/lmflow/models/interfaces/tunable/index.html#lmflow.models.interfaces.tunable.Tunable">[docs]</a><span class="k">class</span> <span class="nc">Tunable</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span> + <span class="k">pass</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div 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aria-current="page">lmflow.models.regression_model</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.models.regression_model</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;General regression model.&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">lmflow.models.base_model</span> <span class="kn">import</span> <span class="n">BaseModel</span> + + +<div class="viewcode-block" id="RegressionModel"><a class="viewcode-back" href="../../../autoapi/lmflow/models/regression_model/index.html#lmflow.models.regression_model.RegressionModel">[docs]</a><span class="k">class</span> <span class="nc">RegressionModel</span><span class="p">(</span><span class="n">BaseModel</span><span class="p">):</span> + + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="k">pass</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div 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class="breadcrumb-item active" aria-current="page">lmflow.models.text_regression_model</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.models.text_regression_model</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="sd">A model maps &quot;text_only&quot; data to float.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">lmflow.models.regression_model</span> <span class="kn">import</span> <span class="n">RegressionModel</span> +<span class="kn">from</span> <span class="nn">lmflow.datasets.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span> + + +<div class="viewcode-block" id="TextRegressionModel"><a class="viewcode-back" href="../../../autoapi/lmflow/models/text_regression_model/index.html#lmflow.models.text_regression_model.TextRegressionModel">[docs]</a><span class="k">class</span> <span class="nc">TextRegressionModel</span><span class="p">(</span><span class="n">RegressionModel</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes a TextRegressionModel instance.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> + +<span class="sd"> model_args : </span> +<span class="sd"> Model arguments such as model name, path, revision, etc.</span> + +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> + +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments. </span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model_args</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes a TextRegressionModel instance.</span> +<span class="sd"> :param model_args: dictionary with model arguments such as model name, path, revision, etc.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">inference_func</span> <span class="o">=</span> <span class="kc">None</span> + + +<div class="viewcode-block" id="TextRegressionModel.register_inference_function"><a class="viewcode-back" href="../../../autoapi/lmflow/models/text_regression_model/index.html#lmflow.models.text_regression_model.TextRegressionModel.register_inference_function">[docs]</a> <span class="k">def</span> <span class="nf">register_inference_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inference_func</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Registers a regression function.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">inference_func</span> <span class="o">=</span> <span class="n">inference_func</span></div> + + +<div class="viewcode-block" id="TextRegressionModel.inference"><a class="viewcode-back" href="../../../autoapi/lmflow/models/text_regression_model/index.html#lmflow.models.text_regression_model.TextRegressionModel.inference">[docs]</a> <span class="k">def</span> <span class="nf">inference</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Gets regression results of a given dataset.</span> + +<span class="sd"> :inputs: Dataset object, only accept type &quot;text_only&quot;.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference_func</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference_func</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">pass</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + 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<span class="nn">copy</span> +<span class="kn">import</span> <span class="nn">logging</span> +<span class="kn">import</span> <span class="nn">time</span> +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span> +<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Union</span> + +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">Blip2ForConditionalGeneration</span><span class="p">,</span> + <span class="n">Blip2Config</span><span class="p">,</span> + <span class="n">AutoModelForCausalLM</span> +<span class="p">)</span> + +<span class="kn">from</span> <span class="nn">.base_model</span> <span class="kn">import</span> <span class="n">BaseModel</span> + +<div class="viewcode-block" id="CustomAutoVision2SeqModel"><a class="viewcode-back" href="../../../autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel">[docs]</a><span class="k">class</span> <span class="nc">CustomAutoVision2SeqModel</span><span class="p">(</span><span class="n">Blip2ForConditionalGeneration</span><span class="p">,</span> <span class="n">BaseModel</span><span class="p">):</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">:</span> <span class="n">Blip2Config</span><span class="p">):</span> + <span class="n">Blip2ForConditionalGeneration</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">with_prompt_cache</span> <span class="o">=</span> <span class="kc">False</span> + <span class="bp">self</span><span class="o">.</span><span class="n">cache_dict</span> <span class="o">=</span> <span class="p">{}</span> + +<div class="viewcode-block" id="CustomAutoVision2SeqModel.vision_model_from_pretrained"><a class="viewcode-back" href="../../../autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.vision_model_from_pretrained">[docs]</a> <span class="k">def</span> <span class="nf">vision_model_from_pretrained</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pretrained_path</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">vision_model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vision_model</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">pretrained_path</span><span class="p">,</span> + <span class="n">config</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">vision_config</span><span class="p">)</span></div> +<div class="viewcode-block" id="CustomAutoVision2SeqModel.qformer_from_pretrained"><a class="viewcode-back" href="../../../autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.qformer_from_pretrained">[docs]</a> <span class="k">def</span> <span class="nf">qformer_from_pretrained</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pretrained_path</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">qformer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">qformer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">pretrained_path</span><span class="p">,</span> + <span class="n">config</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">qformer_config</span><span class="p">)</span></div> + <span class="c1"># print(self.qformer.encoder.layer[11].output_query.dense.weight.mean())</span> + +<div class="viewcode-block" id="CustomAutoVision2SeqModel.language_model_from_pretrained"><a class="viewcode-back" href="../../../autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.language_model_from_pretrained">[docs]</a> <span class="k">def</span> <span class="nf">language_model_from_pretrained</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> + <span class="n">pretrained_path</span><span class="p">,</span> + <span class="n">low_resource</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">use_prompt_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> + <span class="c1"># TODO remove the low resource related loading in the future</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_prompt_cache</span> <span class="o">=</span> <span class="n">use_prompt_cache</span> + <span class="k">if</span> <span class="n">low_resource</span><span class="p">:</span> + <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span> + <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> + <span class="n">load_in_8bit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">device_map</span><span class="o">=</span><span class="s2">&quot;auto&quot;</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{}</span> + <span class="n">past_model_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">language_model</span><span class="o">.</span><span class="n">model_dim</span> + <span class="bp">self</span><span class="o">.</span><span class="n">language_model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span> + <span class="n">pretrained_path</span><span class="p">,</span> + <span class="n">config</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">text_config</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">text_config</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">!=</span> <span class="n">past_model_dim</span><span class="p">:</span> + <span class="c1"># should update the language projection layer</span> + <span class="n">in_channels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">language_projection</span><span class="o">.</span><span class="n">in_features</span> + <span class="bp">self</span><span class="o">.</span><span class="n">language_projection</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">in_channels</span><span class="p">,</span> + <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">text_config</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">,</span> + <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div> + +<div class="viewcode-block" id="CustomAutoVision2SeqModel.register_prompt_cache"><a class="viewcode-back" href="../../../autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.register_prompt_cache">[docs]</a> <span class="k">def</span> <span class="nf">register_prompt_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">prompt_ids</span><span class="p">,</span> <span class="n">prompt_keys_values</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Udpate the prompt id and embedding for reuse in the future</span> + +<span class="sd"> Args:</span> +<span class="sd"> prompt_ids (torch.LongTensor): The id of the prompt.</span> +<span class="sd"> prompt_keys_values (torch.FloatTensor): The embedding of the prompt.</span> + +<span class="sd"> Returns:</span> +<span class="sd"> None</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">prompt_ids</span> <span class="o">=</span> <span class="n">prompt_ids</span> + <span class="bp">self</span><span class="o">.</span><span class="n">prompt_keys_values</span> <span class="o">=</span> <span class="n">prompt_keys_values</span> + <span class="bp">self</span><span class="o">.</span><span class="n">with_prompt_cache</span> <span class="o">=</span> <span class="kc">True</span></div> + +<div class="viewcode-block" id="CustomAutoVision2SeqModel.save_prompt_cache"><a class="viewcode-back" href="../../../autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.save_prompt_cache">[docs]</a> <span class="k">def</span> <span class="nf">save_prompt_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Save prompt embedding and id.</span> + +<span class="sd"> Args:</span> +<span class="sd"> path: The path to save the prompt embedding and id.</span> +<span class="sd"> </span> +<span class="sd"> Returns:</span> +<span class="sd"> None</span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span> + <span class="nb">dict</span><span class="p">(</span> + <span class="n">prompt_ids</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prompt_ids</span><span class="p">,</span> + <span class="n">prompt_keys_values</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">prompt_keys_values</span> + <span class="p">),</span> + <span class="n">path</span><span class="p">)</span></div> + +<div class="viewcode-block" id="CustomAutoVision2SeqModel.load_prompt_cache"><a class="viewcode-back" href="../../../autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.load_prompt_cache">[docs]</a> <span class="k">def</span> <span class="nf">load_prompt_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Load prompt embedding and id.</span> +<span class="sd"> Args:</span> +<span class="sd"> path: The path to load the prompt embedding and id.</span> +<span class="sd"> </span> +<span class="sd"> Returns:</span> +<span class="sd"> None</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">prompt_cache</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">register_prompt_cache</span><span class="p">(</span><span class="n">prompt_cache</span><span class="p">[</span><span class="s2">&quot;prompt_ids&quot;</span><span class="p">],</span> + <span class="n">prompt_cache</span><span class="p">[</span><span class="s2">&quot;prompt_keys_values&quot;</span><span class="p">])</span></div> + + + <span class="nd">@torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()</span> +<div class="viewcode-block" id="CustomAutoVision2SeqModel.generate"><a class="viewcode-back" href="../../../autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.generate">[docs]</a> <span class="k">def</span> <span class="nf">generate</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">pixel_values</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">,</span> + <span class="n">input_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">attention_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">image_token_indexes</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> + <span class="n">one_sample_multiple_images</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> + <span class="o">**</span><span class="n">generate_kwargs</span><span class="p">,</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Overrides `generate` function to be able to use the model as a conditional generator.</span> + +<span class="sd"> Args:</span> +<span class="sd"> pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)):</span> +<span class="sd"> Input images to be processed.</span> +<span class="sd"> input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):</span> +<span class="sd"> The sequence used as a prompt for the generation.</span> +<span class="sd"> attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*):</span> +<span class="sd"> Mask to avoid performing attention on padding token indices</span> +<span class="sd"> image_token_indexes (bool, *optional*):</span> +<span class="sd"> The index for inserting the image tokens.</span> +<span class="sd"> one_sample_multiple_images: (bool, *optional*):</span> +<span class="sd"> The flag for inference that the input batch size is 1 and contain multiple images.</span> + +<span class="sd"> Returns:</span> +<span class="sd"> captions (list): A list of strings of length batch_size * num_captions.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;hf_device_map&quot;</span><span class="p">):</span> + <span class="c1"># preprocess for `accelerate`</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_preprocess_accelerate</span><span class="p">()</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">one_sample_multiple_images</span><span class="p">:</span> + <span class="n">batch_size</span> <span class="o">=</span> <span class="n">pixel_values</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">1</span> + + <span class="n">image_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vision_model</span><span class="p">(</span><span class="n">pixel_values</span><span class="p">,</span> <span class="n">return_dict</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">last_hidden_state</span> + <span class="n">image_attention_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">image_embeds</span><span class="o">.</span><span class="n">size</span><span class="p">()[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">image_embeds</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="n">query_tokens</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">query_tokens</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">image_embeds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> + <span class="n">query_outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">qformer</span><span class="p">(</span> + <span class="n">query_embeds</span><span class="o">=</span><span class="n">query_tokens</span><span class="p">,</span> + <span class="n">encoder_hidden_states</span><span class="o">=</span><span class="n">image_embeds</span><span class="p">,</span> + <span class="n">encoder_attention_mask</span><span class="o">=</span><span class="n">image_attention_mask</span><span class="p">,</span> + <span class="n">return_dict</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">query_output</span> <span class="o">=</span> <span class="n">query_outputs</span><span class="o">.</span><span class="n">last_hidden_state</span> + <span class="n">language_model_inputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">language_projection</span><span class="p">(</span><span class="n">query_output</span><span class="p">)</span> + + <span class="n">language_attention_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span> + <span class="n">language_model_inputs</span><span class="o">.</span><span class="n">size</span><span class="p">()[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">language_model_inputs</span><span class="o">.</span><span class="n">device</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">input_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">input_ids</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">([[</span><span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">text_config</span><span class="o">.</span><span class="n">bos_token_id</span><span class="p">]])</span> + <span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> + <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">image_embeds</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">attention_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">attention_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">input_ids</span><span class="p">)</span> + <span class="n">attention_mask</span> <span class="o">=</span> <span class="n">attention_mask</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">language_attention_mask</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="c1"># concatenate query embeddings with prompt embeddings</span> + <span class="n">inputs_embeds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input_embeddings</span><span class="p">()(</span><span class="n">input_ids</span><span class="p">)</span> + <span class="n">inputs_embeds</span> <span class="o">=</span> <span class="n">inputs_embeds</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">language_model_inputs</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="c1"># concatenate the text embeddings with image embeddings</span> + <span class="n">inputs_embeds_with_images</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">attention_mask_with_images</span> <span class="o">=</span> <span class="p">[]</span> + <span class="c1"># currently we only support with one image</span> + <span class="n">start_index</span><span class="p">,</span> <span class="n">end_index</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span> + <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">image_token_indexes</span><span class="p">)</span> <span class="o">==</span> <span class="n">pixel_values</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + <span class="c1"># token format: (# text, # image)xN, # text</span> + + <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">image_token_index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">image_token_indexes</span><span class="p">):</span> + <span class="n">end_index</span> <span class="o">+=</span> <span class="n">image_token_index</span> + <span class="n">inputs_embeds_with_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> + <span class="n">inputs_embeds</span><span class="p">[:,</span> <span class="n">start_index</span><span class="p">:</span><span class="n">end_index</span><span class="p">])</span> + <span class="n">inputs_embeds_with_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">language_model_inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="kc">None</span><span class="p">])</span> + <span class="n">attention_mask_with_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> + <span class="n">attention_mask</span><span class="p">[:,</span> <span class="n">start_index</span><span class="p">:</span><span class="n">end_index</span><span class="p">])</span> + <span class="n">attention_mask_with_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">language_attention_mask</span><span class="p">[</span><span class="n">idx</span><span class="p">][</span><span class="kc">None</span><span class="p">])</span> + <span class="n">start_index</span> <span class="o">=</span> <span class="n">end_index</span> + + <span class="n">inputs_embeds_with_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">inputs_embeds</span><span class="p">[:,</span> <span class="n">image_token_indexes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:])</span> + <span class="n">inputs_embeds</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">inputs_embeds_with_images</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> + <span class="n">attention_mask_with_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attention_mask</span><span class="p">[:,</span> <span class="n">image_token_indexes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:])</span> + <span class="n">attention_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">attention_mask_with_images</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> + <span class="c1"># comebine the embeds</span> + <span class="n">inputs_embeds</span> <span class="o">=</span> <span class="n">inputs_embeds</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">language_model</span><span class="o">.</span><span class="n">lm_head</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> + <span class="n">attention_mask</span> <span class="o">=</span> <span class="n">attention_mask</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">language_model</span><span class="o">.</span><span class="n">lm_head</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_prompt_cache</span> <span class="ow">or</span> <span class="n">batch_size</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">language_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> + <span class="n">inputs_embeds</span><span class="o">=</span><span class="n">inputs_embeds</span><span class="p">,</span> + <span class="n">attention_mask</span><span class="o">=</span><span class="n">attention_mask</span><span class="p">,</span> + <span class="o">**</span><span class="n">generate_kwargs</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># current resuse prompt embeddings is not supported when batch size is 1;</span> + <span class="n">past_key_values</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">prompt_length</span> <span class="o">=</span> <span class="n">image_token_indexes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">with_prompt_cache</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span> + <span class="n">prompt_ids</span> <span class="o">=</span> <span class="n">input_ids</span><span class="p">[:,</span> <span class="p">:</span><span class="n">prompt_length</span><span class="p">]</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">language_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> + <span class="n">inputs_embeds</span><span class="o">=</span><span class="n">inputs_embeds</span><span class="p">[:,</span> <span class="p">:</span><span class="n">prompt_length</span><span class="p">],</span> + <span class="n">attention_mask</span><span class="o">=</span><span class="n">attention_mask</span><span class="p">[:,</span> <span class="p">:</span><span class="n">prompt_length</span><span class="p">],</span> + <span class="n">use_cache</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_prompt_cache</span><span class="p">,</span> + <span class="o">**</span><span class="n">generate_kwargs</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">past_key_values</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;past_key_values&quot;</span><span class="p">]</span> + <span class="bp">self</span><span class="o">.</span><span class="n">register_prompt_cache</span><span class="p">(</span><span class="n">prompt_ids</span><span class="p">,</span> <span class="n">past_key_values</span><span class="p">)</span> + + <span class="n">prompt_length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prompt_id</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> + <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">input_ids</span><span class="p">[:,</span> <span class="p">:</span><span class="n">prompt_length</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">prompt_id</span><span class="p">):</span> + <span class="n">past_key_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prompt_key_values</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">past_key_values</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">generate_kwargs</span><span class="p">[</span><span class="s2">&quot;past_key_values&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">past_key_values</span> + + <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">language_model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span> + <span class="n">inputs_embeds</span><span class="o">=</span><span class="n">inputs_embeds</span><span class="p">[:,</span> <span class="n">prompt_length</span><span class="p">:],</span> + <span class="n">attention_mask</span><span class="o">=</span><span class="n">attention_mask</span><span class="p">[:,</span> <span class="n">prompt_length</span><span class="p">:],</span> + <span class="n">use_cache</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_prompt_cache</span><span class="p">,</span> + <span class="o">**</span><span class="n">generate_kwargs</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">logits</span> + + <span class="k">return</span> <span class="n">outputs</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div class="footer-item"> + <p 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+ + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.pipeline.auto_pipeline</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;Return a pipeline automatically based on its name.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">lmflow.pipeline.evaluator</span> <span class="kn">import</span> <span class="n">Evaluator</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.finetuner</span> <span class="kn">import</span> <span class="n">Finetuner</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.inferencer</span> <span class="kn">import</span> <span class="n">Inferencer</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.raft_aligner</span> <span class="kn">import</span> <span class="n">RaftAligner</span> + + +<div class="viewcode-block" id="PIPELINE_MAPPING"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/auto_pipeline/index.html#lmflow.pipeline.auto_pipeline.PIPELINE_MAPPING">[docs]</a><span class="n">PIPELINE_MAPPING</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;evaluator&quot;</span><span class="p">:</span> <span class="n">Evaluator</span><span class="p">,</span> + <span class="s2">&quot;finetuner&quot;</span><span class="p">:</span> <span class="n">Finetuner</span><span class="p">,</span> + <span class="s2">&quot;inferencer&quot;</span><span class="p">:</span> <span class="n">Inferencer</span><span class="p">,</span> + <span class="s2">&quot;raft_aligner&quot;</span><span class="p">:</span> <span class="n">RaftAligner</span><span class="p">,</span> +<span class="p">}</span></div> + + +<div class="viewcode-block" id="AutoPipeline"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/auto_pipeline/index.html#lmflow.pipeline.auto_pipeline.AutoPipeline">[docs]</a><span class="k">class</span> <span class="nc">AutoPipeline</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot; </span> +<span class="sd"> The class designed to return a pipeline automatically based on its name.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="nd">@classmethod</span> +<div class="viewcode-block" id="AutoPipeline.get_pipeline"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/auto_pipeline/index.html#lmflow.pipeline.auto_pipeline.AutoPipeline.get_pipeline">[docs]</a> <span class="k">def</span> <span class="nf">get_pipeline</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> + <span class="n">pipeline_name</span><span class="p">,</span> + <span class="n">model_args</span><span class="p">,</span> + <span class="n">data_args</span><span class="p">,</span> + <span class="n">pipeline_args</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">):</span> + <span class="k">if</span> <span class="n">pipeline_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">PIPELINE_MAPPING</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s1">&#39;Pipeline &quot;</span><span class="si">{</span><span class="n">pipeline_name</span><span class="si">}</span><span class="s1">&quot; is not supported&#39;</span> + <span class="p">)</span> + + <span class="n">pipeline</span> <span class="o">=</span> <span class="n">PIPELINE_MAPPING</span><span class="p">[</span><span class="n">pipeline_name</span><span class="p">](</span> + <span class="n">model_args</span><span class="p">,</span> + <span class="n">data_args</span><span class="p">,</span> + <span class="n">pipeline_args</span><span class="p">,</span> + <span class="o">*</span><span class="n">args</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">pipeline</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> 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aria-current="page">lmflow.pipeline.base_aligner</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.pipeline.base_aligner</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot; BaseTuner: a subclass of BasePipeline.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">lmflow.pipeline.base_pipeline</span> <span class="kn">import</span> <span class="n">BasePipeline</span> + + +<div class="viewcode-block" id="BaseAligner"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/base_aligner/index.html#lmflow.pipeline.base_aligner.BaseAligner">[docs]</a><span class="k">class</span> <span class="nc">BaseAligner</span><span class="p">(</span><span class="n">BasePipeline</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot; A subclass of BasePipeline which is alignable.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="k">pass</span> + +<div class="viewcode-block" id="BaseAligner._check_if_alignable"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/base_aligner/index.html#lmflow.pipeline.base_aligner.BaseAligner._check_if_alignable">[docs]</a> <span class="k">def</span> <span class="nf">_check_if_alignable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">reward_model</span><span class="p">):</span> + <span class="c1"># TODO: check if the model is alignable and dataset is compatible</span> + <span class="c1"># TODO: add reward_model</span> + <span class="k">pass</span></div> + +<div class="viewcode-block" id="BaseAligner.align"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/base_aligner/index.html#lmflow.pipeline.base_aligner.BaseAligner.align">[docs]</a> <span class="k">def</span> <span class="nf">align</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">reward_model</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;.align is not implemented&quot;</span><span class="p">)</span></div></div> +</pre></div> + + </article> + + + + <footer 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coding=utf-8</span> +<span class="sd">&quot;&quot;&quot; BasePipeline.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span> <span class="c1"># abstract class</span> + +<div class="viewcode-block" id="BasePipeline"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/base_pipeline/index.html#lmflow.pipeline.base_pipeline.BasePipeline">[docs]</a><span class="k">class</span> <span class="nc">BasePipeline</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span> + <span class="k">pass</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so 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+<div class="viewcode-block" id="BaseTuner"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/base_tuner/index.html#lmflow.pipeline.base_tuner.BaseTuner">[docs]</a><span class="k">class</span> <span class="nc">BaseTuner</span><span class="p">(</span><span class="n">BasePipeline</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot; A subclass of BasePipeline which is tunable.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="k">pass</span> + +<div class="viewcode-block" id="BaseTuner._check_if_tunable"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/base_tuner/index.html#lmflow.pipeline.base_tuner.BaseTuner._check_if_tunable">[docs]</a> <span class="k">def</span> <span class="nf">_check_if_tunable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span> + <span class="c1"># TODO: check if the model is tunable and dataset is compatible</span> + <span class="k">pass</span></div> + +<div class="viewcode-block" id="BaseTuner.tune"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/base_tuner/index.html#lmflow.pipeline.base_tuner.BaseTuner.tune">[docs]</a> <span class="k">def</span> <span class="nf">tune</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;.tune is not implemented&quot;</span><span class="p">)</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div class="footer-item"> + <p class="copyright"> + + © Copyright LMFlow 2023. + <br/> + + </p> +</div> + + <div class="footer-item"> + <p 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class="bd-article" role="main"> + + <h1>Source code for lmflow.pipeline.evaluator</h1><div class="highlight"><pre> +<span></span><span class="sd">&quot;&quot;&quot;The Evaluator class simplifies the process of running evaluation on a language model provided by a HFDecoderModel instance imported from the lmflow package. The class constructor takes three dictionaries as arguments: model_args containing arguments related to the language model, data_args containing arguments related to the data used for evaluation, and evaluator_args containing other arguments for the evaluation process.</span> + +<span class="sd">The class has two methods: create_dataloader() that loads the data from the test file, creates a data loader, and returns it with the size of the data, and evaluate(model) that generates output text given input text. It uses the create_dataloader() method to load the data, iterates over the data in mini-batches, and encodes the input text with the encode() method of the HFDecoderModel class. Then, it generates output text using the evaluate() method of the HFDecoderModel class, decodes the generated output text using the decode() method of the HFDecoderModel class, and writes the output to a file in the output directory. The method also logs some information to the console and Weights and Biases if the use_wandb argument is True.</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="kn">import</span> <span class="nn">os</span> +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">wandb</span> +<span class="kn">import</span> <span class="nn">deepspeed</span> +<span class="kn">import</span> <span class="nn">sys</span> +<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> +<span class="kn">import</span> <span class="nn">datetime</span> +<span class="kn">import</span> <span class="nn">json</span> +<span class="c1"># TODO: remove later</span> +<span class="kn">from</span> <span class="nn">accelerate</span> <span class="kn">import</span> <span class="n">Accelerator</span> +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoConfig</span> +<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span> + +<span class="kn">from</span> <span class="nn">lmflow.datasets.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.base_pipeline</span> <span class="kn">import</span> <span class="n">BasePipeline</span> +<span class="kn">from</span> <span class="nn">lmflow.models.hf_decoder_model</span> <span class="kn">import</span> <span class="n">HFDecoderModel</span> +<span class="kn">from</span> <span class="nn">lmflow.utils.data_utils</span> <span class="kn">import</span> <span class="n">set_random_seed</span><span class="p">,</span> <span class="n">batchlize</span><span class="p">,</span> <span class="n">answer_extraction</span> +<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;TOKENIZERS_PARALLELISM&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;false&quot;</span> <span class="c1"># To avoid warnings about parallelism in tokenizers</span> + +<div class="viewcode-block" id="Evaluator"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator">[docs]</a><span class="k">class</span> <span class="nc">Evaluator</span><span class="p">(</span><span class="n">BasePipeline</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes the `Evaluator` class with given arguments.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> model_args : ModelArguments object.</span> +<span class="sd"> Contains the arguments required to load the model.</span> +<span class="sd"> </span> +<span class="sd"> data_args : DatasetArguments object.</span> +<span class="sd"> Contains the arguments required to load the dataset.</span> + +<span class="sd"> evaluator_args : EvaluatorArguments object.</span> +<span class="sd"> Contains the arguments required to perform evaluation.</span> + + +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_args</span><span class="p">,</span> <span class="n">data_args</span><span class="p">,</span> <span class="n">evaluator_args</span><span class="p">):</span> + <span class="c1"># our method</span> + <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span> <span class="o">=</span> <span class="n">data_args</span> + <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span> <span class="o">=</span> <span class="n">evaluator_args</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span> <span class="o">=</span> <span class="n">model_args</span> + + <span class="c1"># logger</span> + <span class="k">if</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">use_wandb</span> <span class="o">==</span> <span class="kc">True</span><span class="p">):</span> + <span class="n">wandb</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">project</span><span class="o">=</span><span class="s2">&quot;lmflow_evaluation&quot;</span><span class="p">)</span> + <span class="c1"># random seed</span> + <span class="n">set_random_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">random_seed</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;LOCAL_RANK&quot;</span><span class="p">,</span> <span class="s2">&quot;0&quot;</span><span class="p">))</span> + <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;WORLD_SIZE&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">))</span> + <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> <span class="c1"># NOTE: cpu-only machine will have error</span> + + <span class="k">if</span> <span class="n">evaluator_args</span><span class="o">.</span><span class="n">use_accelerator_for_evaluator</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">accelerator</span> <span class="o">=</span> <span class="n">Accelerator</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">accelerator</span><span class="o">.</span><span class="n">wait_for_everyone</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">deepspeed</span><span class="o">.</span><span class="n">init_distributed</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">AutoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">)</span> + <span class="k">try</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_hidden_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">hidden_size</span> + <span class="k">except</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error in setting hidden size, use the default size 1024&quot;</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_hidden_size</span> <span class="o">=</span> <span class="mi">1024</span> <span class="c1"># gpt2 seems do not have hidden_size in config</span> + + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;model_hidden_size = </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_hidden_size</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="c1"># batch size has to be divisible by world_size, but can be bigger than world_size</span> + <span class="n">train_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> + <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">minibatch_size</span> <span class="o">=</span> <span class="n">train_batch_size</span> + <span class="bp">self</span><span class="o">.</span><span class="n">block_size</span> <span class="o">=</span> <span class="n">evaluator_args</span><span class="o">.</span><span class="n">evaluate_block_size</span> + <span class="c1"># dataloader, data_size = create_dataloader(args) # load dataset</span> + + +<div class="viewcode-block" id="Evaluator.create_dataloader"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator.create_dataloader">[docs]</a> <span class="k">def</span> <span class="nf">create_dataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">):</span> + <span class="n">data_dict</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span> <span class="n">instance</span><span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="p">[</span><span class="s2">&quot;instances&quot;</span><span class="p">]</span> <span class="p">]</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span> <span class="n">instance</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="p">[</span><span class="s2">&quot;instances&quot;</span><span class="p">]</span> <span class="p">]</span> + <span class="n">dataset_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> + <span class="n">dataset_buf</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dataset_size</span><span class="p">):</span> + <span class="n">dataset_buf</span><span class="o">.</span><span class="n">append</span><span class="p">({</span> + <span class="s2">&quot;input&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> + <span class="s2">&quot;output&quot;</span><span class="p">:</span> <span class="n">outputs</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> + <span class="s2">&quot;input_idx&quot;</span><span class="p">:</span> <span class="n">idx</span> + <span class="p">})</span> + + <span class="n">dataloader</span> <span class="o">=</span> <span class="n">batchlize</span><span class="p">(</span> + <span class="n">dataset_buf</span><span class="p">,</span> + <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">minibatch_size</span><span class="p">,</span> + <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">random_shuffle</span> + <span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Successfully create dataloader with size </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span><span class="si">}</span><span class="s2">,batch_size </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">minibatch_size</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">dataloader</span><span class="p">,</span> <span class="n">dataset_size</span></div> + + + <span class="c1"># TODO: Split for better unittest</span> + +<div class="viewcode-block" id="Evaluator._match"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._match">[docs]</a> <span class="k">def</span> <span class="nf">_match</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predicted_answer</span><span class="p">,</span> <span class="n">groundtruth</span><span class="p">,</span> <span class="n">answer_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="n">case_insensitive_types</span> <span class="o">=</span> <span class="p">[</span> + <span class="s2">&quot;strategyqa&quot;</span><span class="p">,</span> + <span class="s2">&quot;coin_flip&quot;</span><span class="p">,</span> + <span class="s2">&quot;pubmedqa&quot;</span><span class="p">,</span> + <span class="s2">&quot;binary_choice&quot;</span><span class="p">,</span> + <span class="s2">&quot;medmcqa&quot;</span><span class="p">,</span> + <span class="s2">&quot;usmle&quot;</span><span class="p">,</span> + <span class="p">]</span> + <span class="k">if</span> <span class="n">answer_type</span> <span class="ow">in</span> <span class="n">case_insensitive_types</span><span class="p">:</span> + <span class="k">return</span> <span class="n">predicted_answer</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="n">groundtruth</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">return</span> <span class="n">predicted_answer</span> <span class="o">==</span> <span class="n">groundtruth</span> + <span class="k">return</span> <span class="kc">False</span></div> + + +<div class="viewcode-block" id="Evaluator.evaluate"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator.evaluate">[docs]</a> <span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> + <span class="n">metric</span> <span class="o">=</span> <span class="s2">&quot;accuracy&quot;</span><span class="p">,</span> + <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform Evaluation for a model</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> model : TunableModel object.</span> +<span class="sd"> TunableModel to perform inference</span> + +<span class="sd"> dataset : Dataset object.</span> +<span class="sd"> </span> + +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="n">metric</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;acc&quot;</span><span class="p">,</span> <span class="s2">&quot;accuracy&quot;</span><span class="p">]:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">use_accelerator_for_evaluator</span><span class="p">:</span> + <span class="n">acc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_evaluate_acc_with_accelerator</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">acc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_evaluate_acc_with_deepspeed</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Evaluating final accuracy: </span><span class="si">{</span><span class="n">acc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">return</span> <span class="n">acc</span> + <span class="k">elif</span> <span class="n">metric</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;ppl&quot;</span><span class="p">,</span> <span class="s2">&quot;perplexity&quot;</span><span class="p">]:</span> + <span class="n">ppl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_evaluate_ppl</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Evaluating final perplexity: </span><span class="si">{</span><span class="n">ppl</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">return</span> <span class="n">ppl</span> + <span class="k">elif</span> <span class="n">metric</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;nll&quot;</span><span class="p">,</span> <span class="s2">&quot;neg_log_likelihood&quot;</span><span class="p">]:</span> + <span class="n">nll</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_evaluate_nll</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Evaluating final negative log likelihood: </span><span class="si">{</span><span class="n">nll</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">return</span> <span class="n">nll</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;metric </span><span class="si">{</span><span class="n">metric</span><span class="si">}</span><span class="s2"> is not supported&quot;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="Evaluator._evaluate_acc_with_accelerator"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._evaluate_acc_with_accelerator">[docs]</a> <span class="k">def</span> <span class="nf">_evaluate_acc_with_accelerator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> + <span class="n">dataloader</span><span class="p">,</span> <span class="n">data_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_dataloader</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">accelerator</span><span class="o">.</span><span class="n">is_local_main_process</span><span class="p">:</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">):</span> + <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">)</span> + <span class="n">output_writer</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">output_dir</span><span class="si">}</span><span class="s2">/evaluation.json&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> + + <span class="n">correct_number_list</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">batch_index</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span> + <span class="k">if</span> <span class="n">batch_index</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span><span class="o">.</span><span class="n">max_eval_samples</span><span class="p">:</span> + <span class="k">break</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span> <span class="o">&gt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span> + <span class="n">current_batch</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span><span class="p">]</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">current_batch</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span><span class="p">:(</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span><span class="p">]</span> + <span class="n">prompt_structure</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">prompt_structure</span> + <span class="nb">input</span> <span class="o">=</span> <span class="p">[</span><span class="n">prompt_structure</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">i</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">current_batch</span><span class="p">]</span> + <span class="n">output</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="s1">&#39;output&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">current_batch</span><span class="p">]</span> + + <span class="n">batch_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">,</span><span class="n">padding</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">accelerator</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">batch_input</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> + <span class="n">mask</span> <span class="o">=</span> <span class="n">batch_input</span><span class="p">[</span><span class="s1">&#39;attention_mask&#39;</span><span class="p">]</span> + <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">accelerator</span><span class="o">.</span><span class="n">autocast</span><span class="p">():</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">max_new_tokens</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">max_new_tokens</span><span class="p">,</span><span class="n">attention_mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span><span class="n">temperature</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">temperature</span><span class="p">,</span> <span class="n">repetition_penalty</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">repetition_penalty</span><span class="p">,</span><span class="n">use_accelerator</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">use_accelerator_for_evaluator</span><span class="p">)</span> + <span class="n">text_out</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">decoded_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">,)</span> + <span class="n">prompt_length</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">decoded_input</span><span class="p">]</span> + <span class="n">text_out</span> <span class="o">=</span> <span class="p">[</span><span class="n">text_out</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">prompt_length</span><span class="p">[</span><span class="n">i</span><span class="p">]:]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">text_out</span><span class="p">))]</span> + <span class="n">answer_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">answer_type</span> + <span class="n">pred_answer</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">text_out</span><span class="p">:</span> + <span class="n">pred_answer</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">answer_extraction</span><span class="p">(</span> + <span class="n">i</span><span class="p">,</span> + <span class="n">answer_type</span><span class="o">=</span><span class="n">answer_type</span><span class="p">,</span> + <span class="p">))</span> + <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;batch_index</span><span class="si">{</span><span class="n">batch_index</span><span class="si">}</span><span class="s2"> rank</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="si">}</span><span class="s2">:</span><span class="se">\n</span><span class="s2"> question=</span><span class="si">{</span><span class="nb">input</span><span class="si">}</span><span class="se">\n</span><span class="s2"> prediction=</span><span class="si">{</span><span class="n">text_out</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;predicted answer: </span><span class="si">{</span><span class="n">pred_answer</span><span class="si">}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;groundtruth answer: </span><span class="si">{</span><span class="n">output</span><span class="si">}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span> <span class="o">&gt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span> + <span class="n">correct_</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">correct_</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pred_answer</span><span class="p">)):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match</span><span class="p">(</span><span class="n">pred_answer</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">answer_type</span><span class="p">):</span> + <span class="n">correct_</span> <span class="o">+=</span> <span class="mi">1</span> + + <span class="c1"># collect accuracy from all gpus</span> + <span class="n">all_process</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">correct_</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> + <span class="n">all_process</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">accelerator</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="n">all_process</span><span class="p">)</span> + <span class="n">correct_</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">all_process</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span> + <span class="n">correct_number_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">correct_</span><span class="p">)</span> + + <span class="c1"># collect predictions from all gpus</span> + <span class="n">output_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;question&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">,</span> + <span class="s2">&quot;prediction&quot;</span><span class="p">:</span> <span class="n">text_out</span><span class="p">,</span> + <span class="s2">&quot;pred_answer&quot;</span><span class="p">:</span> <span class="n">pred_answer</span><span class="p">,</span> + <span class="s2">&quot;answer&quot;</span><span class="p">:</span> <span class="n">output</span><span class="p">}</span> + <span class="k">if</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">):</span> + <span class="n">all_process_list</span> <span class="o">=</span> <span class="p">[{}]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> + <span class="n">dist</span><span class="o">.</span><span class="n">gather_object</span><span class="p">(</span><span class="n">output_dict</span><span class="p">,</span> <span class="n">all_process_list</span> <span class="k">if</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dst</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">all_process_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">output_dict</span><span class="p">]</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">accelerator</span><span class="o">.</span><span class="n">is_local_main_process</span><span class="p">:</span> + <span class="n">current_total</span> <span class="o">=</span> <span class="p">(</span><span class="n">batch_index</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span> + <span class="n">current_accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">current_total</span> <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">current_total</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">data_size</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">data_size</span> + <span class="nb">print</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2"> %H:%M:%S&quot;</span><span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">current_total</span><span class="p">)</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="nb">int</span><span class="p">(</span><span class="n">current_total</span><span class="p">)</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">data_size</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="n">data_size</span><span class="si">}</span><span class="s2"> / </span><span class="si">{</span><span class="n">data_size</span><span class="si">}</span><span class="s2"> has been finished, # correct = </span><span class="si">{</span><span class="w"> </span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span><span class="si">}</span><span class="s2">, current accuracy = </span><span class="si">{</span><span class="n">current_accuracy</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="k">if</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">use_wandb</span> <span class="o">==</span> <span class="kc">True</span><span class="p">):</span> + <span class="n">wandb</span><span class="o">.</span><span class="n">log</span><span class="p">({</span><span class="s2">&quot;Accuracy&quot;</span><span class="p">:</span> <span class="n">current_accuracy</span><span class="p">})</span> + + <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">output</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">all_process_list</span><span class="p">):</span> + <span class="n">output_json</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">output</span><span class="p">)</span> + <span class="n">output_writer</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">output_json</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">accelerator</span><span class="o">.</span><span class="n">is_local_main_process</span><span class="p">:</span> + <span class="n">current_accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">data_size</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;# Correct = </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span><span class="si">}</span><span class="s2">, # Total = </span><span class="si">{</span><span class="n">data_size</span><span class="si">}</span><span class="s2">, Final accuracy = &quot;</span><span class="p">,</span> <span class="n">current_accuracy</span><span class="p">)</span> + <span class="n">output_writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> + <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">data_size</span></div> + +<div class="viewcode-block" id="Evaluator._evaluate_acc_with_deepspeed"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._evaluate_acc_with_deepspeed">[docs]</a> <span class="k">def</span> <span class="nf">_evaluate_acc_with_deepspeed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> + <span class="n">dataloader</span><span class="p">,</span> <span class="n">data_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_dataloader</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_initialized</span><span class="p">()</span> <span class="ow">or</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">):</span> + <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">)</span> + <span class="n">output_writer</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">output_dir</span><span class="si">}</span><span class="s2">/evaluation.json&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> + + <span class="n">correct_number_list</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">batch_index</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span> + <span class="k">if</span> <span class="n">batch_index</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span><span class="o">.</span><span class="n">max_eval_samples</span><span class="p">:</span> + <span class="k">break</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span> <span class="o">&gt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span> + <span class="n">current_batch</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[:</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span><span class="p">]</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">current_batch</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span><span class="p">:(</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span><span class="p">]</span> + <span class="n">prompt_structure</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">prompt_structure</span> + <span class="nb">input</span> <span class="o">=</span> <span class="p">[</span><span class="n">prompt_structure</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">i</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">current_batch</span><span class="p">]</span> + <span class="n">output</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="s1">&#39;output&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">current_batch</span><span class="p">]</span> + <span class="n">input_idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="s1">&#39;input_idx&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">current_batch</span><span class="p">]</span> + <span class="n">batch_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">,</span><span class="n">padding</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">batch_input</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> + <span class="n">mask</span> <span class="o">=</span> <span class="n">batch_input</span><span class="p">[</span><span class="s1">&#39;attention_mask&#39;</span><span class="p">]</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">max_new_tokens</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">max_new_tokens</span><span class="p">,</span> <span class="n">attention_mask</span><span class="o">=</span><span class="n">mask</span><span class="p">,</span><span class="n">temperature</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">temperature</span><span class="p">,</span> <span class="n">repetition_penalty</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">repetition_penalty</span><span class="p">)</span> + <span class="n">text_out</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="c1"># # only return the generation, trucating the input</span> + <span class="n">decoded_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">,)</span> + <span class="n">prompt_length</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">decoded_input</span><span class="p">]</span> + <span class="n">text_out</span> <span class="o">=</span> <span class="p">[</span><span class="n">text_out</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">prompt_length</span><span class="p">[</span><span class="n">i</span><span class="p">]:]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">text_out</span><span class="p">))]</span> + <span class="n">answer_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">answer_type</span> + <span class="n">pred_answer</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">text_out</span><span class="p">:</span> + <span class="n">pred_answer</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">answer_extraction</span><span class="p">(</span> + <span class="n">i</span><span class="p">,</span> + <span class="n">answer_type</span><span class="o">=</span><span class="n">answer_type</span><span class="p">,</span> + <span class="p">))</span> + <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;batch_index</span><span class="si">{</span><span class="n">batch_index</span><span class="si">}</span><span class="s2"> rank</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="si">}</span><span class="s2">:</span><span class="se">\n</span><span class="s2"> question=</span><span class="si">{</span><span class="nb">input</span><span class="si">}</span><span class="se">\n</span><span class="s2"> prediction=</span><span class="si">{</span><span class="n">text_out</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;predicted answer: </span><span class="si">{</span><span class="n">pred_answer</span><span class="si">}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;groundtruth answer: </span><span class="si">{</span><span class="n">output</span><span class="si">}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span> <span class="o">&gt;=</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span> + <span class="n">correct_</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">correct_</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pred_answer</span><span class="p">)):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_match</span><span class="p">(</span><span class="n">pred_answer</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">output</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">answer_type</span><span class="p">):</span> + <span class="n">correct_</span> <span class="o">+=</span> <span class="mi">1</span> + + <span class="c1"># collect accuracy from all gpus</span> + <span class="n">all_process</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">correct_</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> + <span class="n">dist</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">all_process</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">ReduceOp</span><span class="o">.</span><span class="n">SUM</span><span class="p">,</span> <span class="n">async_op</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="n">correct_</span> <span class="o">=</span> <span class="n">all_process</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span> + <span class="n">correct_number_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">correct_</span><span class="p">)</span> + + <span class="c1"># collect predictions from all gpus</span> + <span class="n">output_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;question&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">,</span> + <span class="s2">&quot;prediction&quot;</span><span class="p">:</span> <span class="n">text_out</span><span class="p">,</span> + <span class="s2">&quot;pred_answer&quot;</span><span class="p">:</span> <span class="n">pred_answer</span><span class="p">,</span> + <span class="s2">&quot;answer&quot;</span><span class="p">:</span> <span class="n">output</span><span class="p">}</span> + <span class="n">all_process_list</span> <span class="o">=</span> <span class="p">[{}]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> + + <span class="n">dist</span><span class="o">.</span><span class="n">gather_object</span><span class="p">(</span><span class="n">output_dict</span><span class="p">,</span> <span class="n">all_process_list</span> <span class="k">if</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">dst</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_initialized</span><span class="p">()</span> <span class="ow">or</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">current_total</span> <span class="o">=</span> <span class="p">(</span><span class="n">batch_index</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span> + <span class="n">current_accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">current_total</span> <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">current_total</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">data_size</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">data_size</span> + <span class="nb">print</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y-%m-</span><span class="si">%d</span><span class="s2"> %H:%M:%S&quot;</span><span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">current_total</span><span class="p">)</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="nb">int</span><span class="p">(</span><span class="n">current_total</span><span class="p">)</span><span class="w"> </span><span class="o">&lt;</span><span class="w"> </span><span class="n">data_size</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="n">data_size</span><span class="si">}</span><span class="s2"> / </span><span class="si">{</span><span class="n">data_size</span><span class="si">}</span><span class="s2"> has been finished, # correct = </span><span class="si">{</span><span class="w"> </span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span><span class="si">}</span><span class="s2">, current accuracy = </span><span class="si">{</span><span class="n">current_accuracy</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="k">if</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">use_wandb</span> <span class="o">==</span> <span class="kc">True</span><span class="p">):</span> + <span class="n">wandb</span><span class="o">.</span><span class="n">log</span><span class="p">({</span><span class="s2">&quot;Accuracy&quot;</span><span class="p">:</span> <span class="n">current_accuracy</span><span class="p">})</span> + + <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">output</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">all_process_list</span><span class="p">):</span> + <span class="n">output_json</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">output</span><span class="p">)</span> + <span class="n">output_writer</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">output_json</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="n">dist</span><span class="o">.</span><span class="n">is_initialized</span><span class="p">()</span> <span class="ow">or</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">current_accuracy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">data_size</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;# Correct = </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span><span class="si">}</span><span class="s2">, # Total = </span><span class="si">{</span><span class="n">data_size</span><span class="si">}</span><span class="s2">, Final accuracy = &quot;</span><span class="p">,</span> <span class="n">current_accuracy</span><span class="p">)</span> + <span class="n">output_writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> + <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">correct_number_list</span><span class="p">)</span> <span class="o">/</span> <span class="n">data_size</span></div> + +<div class="viewcode-block" id="Evaluator._evaluate_ppl"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._evaluate_ppl">[docs]</a> <span class="k">def</span> <span class="nf">_evaluate_ppl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> + <span class="n">data_dict</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span> + <span class="k">if</span> <span class="n">data_dict</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;text2text&#39;</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;ppl evaluation is currently not supported for text2text dataset, please use text_only dataset.&quot;</span><span class="p">)</span> + <span class="n">texts</span> <span class="o">=</span> <span class="p">[</span> <span class="n">instance</span><span class="p">[</span><span class="s2">&quot;text&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="p">[</span><span class="s2">&quot;instances&quot;</span><span class="p">]</span> <span class="p">]</span> + <span class="n">encodings</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_tokenizer</span><span class="p">()(</span><span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">texts</span><span class="p">),</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span> + <span class="c1"># Define some constant</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span><span class="o">.</span><span class="n">truncate_to_model_max_length</span><span class="p">:</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">max_length</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_positions</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">get_max_length</span><span class="p">())</span> + <span class="k">except</span><span class="p">:</span> + <span class="n">max_length</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">get_max_length</span><span class="p">())</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">max_length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_size</span> + + <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The maximum sequence length : </span><span class="si">{</span><span class="n">max_length</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">seq_len</span> <span class="o">=</span> <span class="n">encodings</span><span class="o">.</span><span class="n">input_ids</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> + + <span class="n">nlls</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">prev_end_loc</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">for</span> <span class="n">begin_loc</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_size</span><span class="p">):</span> + <span class="n">end_loc</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">begin_loc</span> <span class="o">+</span> <span class="n">max_length</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">)</span> + <span class="n">trg_len</span> <span class="o">=</span> <span class="n">end_loc</span> <span class="o">-</span> <span class="n">prev_end_loc</span> <span class="c1"># may be different from block_size on last loop</span> + <span class="n">input_ids</span> <span class="o">=</span> <span class="n">encodings</span><span class="o">.</span><span class="n">input_ids</span><span class="p">[:,</span> <span class="n">begin_loc</span><span class="p">:</span><span class="n">end_loc</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> + <span class="n">target_ids</span> <span class="o">=</span> <span class="n">input_ids</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> + <span class="n">target_ids</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="n">trg_len</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">100</span> + + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()(</span><span class="n">input_ids</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">target_ids</span><span class="p">)</span> + <span class="c1"># loss is calculated using CrossEntropyLoss which averages over valid labels</span> + <span class="c1"># N.B. the model only calculates loss over trg_len - 1 labels, because it internally shifts the labels</span> + <span class="c1"># to the left by 1.</span> + <span class="n">neg_log_likelihood</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">loss</span> + + <span class="n">nlls</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neg_log_likelihood</span><span class="p">)</span> + <span class="n">prev_end_loc</span> <span class="o">=</span> <span class="n">end_loc</span> + <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Evaluating PPL: </span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">begin_loc</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">block_size</span><span class="p">)</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> / </span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">seq_len</span><span class="o">/</span><span class="bp">self</span><span class="o">.</span><span class="n">block_size</span><span class="p">)</span><span class="si">}</span><span class="s2"> Complete, current ppl : </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">nlls</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="n">end_loc</span> <span class="o">==</span> <span class="n">seq_len</span><span class="p">:</span> + <span class="k">break</span> + <span class="n">ppl</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">nlls</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> + <span class="k">return</span> <span class="n">ppl</span></div> + + +<div class="viewcode-block" id="Evaluator._evaluate_nll"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._evaluate_nll">[docs]</a> <span class="k">def</span> <span class="nf">_evaluate_nll</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> + <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Evaluates negative log likelihood of the model over a dataset.</span> + +<span class="sd"> NLL = -1/N sum_{i=1}^N sum_{j=1}^|w_i| ln(p(w_{i,j}|context_window)),</span> + +<span class="sd"> where N is the number of data samples, w_{i,j} is the j-th token in</span> +<span class="sd"> i-th sample. Here &quot;context_window&quot; = p(w_{i,start}, w_{i,start+1}, ...,</span> +<span class="sd"> p_{i,j-1} with start = max(0, j - window_length + 1). &quot;window_length&quot;</span> +<span class="sd"> is normally the maximum length accepted by the model.</span> + +<span class="sd"> Returns:</span> +<span class="sd"> A float which represents the negative log likelihood.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">data_dict</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span> + + <span class="c1"># Handles prompt structure</span> + <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_type</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;text2text&quot;</span><span class="p">:</span> + <span class="n">prompt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluator_args</span><span class="o">.</span><span class="n">prompt_structure</span> + <span class="n">data_dict</span><span class="p">[</span><span class="s2">&quot;instances&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span> + <span class="p">{</span> + <span class="s2">&quot;input&quot;</span><span class="p">:</span> <span class="n">prompt</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">instance</span><span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">]),</span> + <span class="s2">&quot;output&quot;</span><span class="p">:</span> <span class="n">instance</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">]</span> + <span class="p">}</span> + <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="p">[</span><span class="s2">&quot;instances&quot;</span><span class="p">]</span> + <span class="p">]</span> + + <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">data_dict</span><span class="p">)</span> + <span class="n">tokenized_dataset</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">add_special_tokens</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="n">tokenized_dataset</span> <span class="o">=</span> <span class="n">tokenized_dataset</span><span class="o">.</span><span class="n">get_backend_dataset</span><span class="p">()</span> + <span class="n">encoding_list</span> <span class="o">=</span> <span class="p">[</span> + <span class="p">{</span> + <span class="s2">&quot;input_ids&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">input_ids</span><span class="p">]),</span> + <span class="s2">&quot;labels&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">labels</span><span class="p">]),</span> + <span class="p">}</span> + <span class="k">for</span> <span class="n">input_ids</span><span class="p">,</span> <span class="n">labels</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">tokenized_dataset</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">],</span> + <span class="n">tokenized_dataset</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">])</span> + <span class="p">]</span> + + <span class="c1"># Gets context window length</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">max_length</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">n_positions</span><span class="p">,</span> + <span class="n">model</span><span class="o">.</span><span class="n">get_max_length</span><span class="p">())</span> + <span class="k">except</span><span class="p">:</span> + <span class="n">max_length</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">get_max_length</span><span class="p">())</span> + + <span class="n">nlls</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">full_nlls</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">num_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">encoding_list</span><span class="p">)</span> + <span class="k">for</span> <span class="n">sample_idx</span><span class="p">,</span> <span class="n">encodings</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">encoding_list</span><span class="p">):</span> + <span class="n">seq_len</span> <span class="o">=</span> <span class="n">encodings</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> + + <span class="n">prev_end_loc</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">for</span> <span class="n">begin_loc</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">block_size</span><span class="p">):</span> + <span class="n">end_loc</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">begin_loc</span> <span class="o">+</span> <span class="n">max_length</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">)</span> + + <span class="c1"># may be different from block_size on last loop</span> + <span class="n">trg_len</span> <span class="o">=</span> <span class="n">end_loc</span> <span class="o">-</span> <span class="n">prev_end_loc</span> + <span class="n">input_ids</span> <span class="o">=</span> <span class="n">encodings</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">][:,</span> <span class="n">begin_loc</span><span class="p">:</span><span class="n">end_loc</span><span class="p">]</span> + <span class="n">input_ids</span> <span class="o">=</span> <span class="n">input_ids</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> + + <span class="n">labels</span> <span class="o">=</span> <span class="n">encodings</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">][:,</span> <span class="n">begin_loc</span><span class="p">:</span><span class="n">end_loc</span><span class="p">]</span> + <span class="n">target_ids</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> + <span class="n">full_target_ids</span> <span class="o">=</span> <span class="n">input_ids</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> + + <span class="k">def</span> <span class="nf">get_nll</span><span class="p">(</span><span class="n">label_ids</span><span class="p">,</span> <span class="n">nll_list</span><span class="p">):</span> + <span class="n">label_ids</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="n">trg_len</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">100</span> + <span class="n">label_ids</span> <span class="o">=</span> <span class="n">label_ids</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> + + <span class="c1"># Valid labels are from 0 to `vocab_size`</span> + <span class="n">num_valid_labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">count_nonzero</span><span class="p">(</span><span class="n">label_ids</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">)</span> + <span class="k">if</span> <span class="n">label_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">100</span><span class="p">:</span> + <span class="n">num_valid_labels</span> <span class="o">-=</span> <span class="mi">1</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">label_ids</span> <span class="o">==</span> <span class="o">-</span><span class="mi">100</span><span class="p">):</span> + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()(</span> + <span class="n">input_ids</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">label_ids</span> + <span class="p">)</span> + <span class="c1"># loss is calculated using CrossEntropyLoss which</span> + <span class="c1"># sums over valid labels N.B. the model only</span> + <span class="c1"># calculates loss over trg_len - 1 labels, because</span> + <span class="c1"># it internally shifts the labels to the left by 1.</span> + <span class="n">neg_log_likelihood</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">loss</span> <span class="o">*</span> <span class="n">num_valid_labels</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">neg_log_likelihood</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">([])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span> + <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span> + <span class="p">)</span> + + <span class="n">nll_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">neg_log_likelihood</span><span class="p">)</span> + + <span class="n">get_nll</span><span class="p">(</span><span class="n">target_ids</span><span class="p">,</span> <span class="n">nlls</span><span class="p">)</span> + <span class="n">get_nll</span><span class="p">(</span><span class="n">full_target_ids</span><span class="p">,</span> <span class="n">full_nlls</span><span class="p">)</span> + + <span class="n">current_output_nll</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">nlls</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="p">(</span><span class="n">sample_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> + <span class="n">current_full_nll</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">full_nlls</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="p">(</span><span class="n">sample_idx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> + + <span class="n">prev_end_loc</span> <span class="o">=</span> <span class="n">end_loc</span> + <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span> + <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_type</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;text_only&quot;</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Evaluating negative log likelihood:&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">sample_idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> / </span><span class="si">{</span><span class="n">num_samples</span><span class="si">}</span><span class="s2"> Complete,&quot;</span> + <span class="sa">f</span><span class="s2">&quot; current nll: </span><span class="si">{</span><span class="n">current_full_nll</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_type</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;text2text&quot;</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Evaluating negative log likelihood:&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">sample_idx</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1</span><span class="si">}</span><span class="s2"> / </span><span class="si">{</span><span class="n">num_samples</span><span class="si">}</span><span class="s2"> Complete,&quot;</span> + <span class="sa">f</span><span class="s2">&quot; current full nll / input nll / output nll:&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">current_full_nll</span><span class="si">}</span><span class="s2"> /&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">current_full_nll</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">current_output_nll</span><span class="si">}</span><span class="s2"> /&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">current_output_nll</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="s2">&quot;f{dataset.get_type()} typed datasets are not&quot;</span> + <span class="s2">&quot; supported&quot;</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">end_loc</span> <span class="o">==</span> <span class="n">seq_len</span><span class="p">:</span> + <span class="k">break</span> + + <span class="n">mean_nll</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">nlls</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="n">num_samples</span> + <span class="k">return</span> <span class="n">mean_nll</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + 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href="../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.pipeline.finetuner</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.pipeline.finetuner</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;The Finetuner class simplifies the process of running finetuning process on a language model for a TunableModel instance with given dataset.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">import</span> <span class="nn">copy</span> +<span class="kn">import</span> <span class="nn">logging</span> +<span class="kn">import</span> <span class="nn">os</span> +<span class="kn">import</span> <span class="nn">sys</span> + +<span class="kn">import</span> <span class="nn">datasets</span> +<span class="kn">import</span> <span class="nn">transformers</span> +<span class="kn">import</span> <span class="nn">evaluate</span> +<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">chain</span> +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">Trainer</span><span class="p">,</span> + <span class="n">default_data_collator</span><span class="p">,</span> + <span class="n">set_seed</span><span class="p">,</span> +<span class="p">)</span> +<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span> +<span class="kn">from</span> <span class="nn">transformers.utils</span> <span class="kn">import</span> <span class="n">send_example_telemetry</span> +<span class="kn">from</span> <span class="nn">transformers.trainer_utils</span> <span class="kn">import</span> <span class="n">get_last_checkpoint</span> + +<span class="kn">from</span> <span class="nn">lmflow.datasets.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.base_tuner</span> <span class="kn">import</span> <span class="n">BaseTuner</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.utils.peft_trainer</span> <span class="kn">import</span> <span class="n">PeftTrainer</span><span class="p">,</span> <span class="n">PeftSavingCallback</span> + + +<div class="viewcode-block" id="logger"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/finetuner/index.html#lmflow.pipeline.finetuner.logger">[docs]</a><span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="Finetuner"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/finetuner/index.html#lmflow.pipeline.finetuner.Finetuner">[docs]</a><span class="k">class</span> <span class="nc">Finetuner</span><span class="p">(</span><span class="n">BaseTuner</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes the `Finetuner` class with given arguments.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> model_args : ModelArguments object.</span> +<span class="sd"> Contains the arguments required to load the model.</span> + +<span class="sd"> data_args : DatasetArguments object.</span> +<span class="sd"> Contains the arguments required to load the dataset.</span> + +<span class="sd"> finetuner_args : FinetunerArguments object.</span> +<span class="sd"> Contains the arguments required to perform finetuning.</span> + +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> + +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> + +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_args</span><span class="p">,</span> <span class="n">data_args</span><span class="p">,</span> <span class="n">finetuner_args</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span> <span class="o">=</span> <span class="n">model_args</span> + <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span> <span class="o">=</span> <span class="n">data_args</span> + <span class="bp">self</span><span class="o">.</span><span class="n">finetuner_args</span> <span class="o">=</span> <span class="n">finetuner_args</span> + + <span class="c1"># Sending telemetry. Tracking the example usage helps us better</span> + <span class="c1"># allocate resources to maintain them. The information sent is the one</span> + <span class="c1"># passed as arguments along with your Python/PyTorch versions.</span> + <span class="n">send_example_telemetry</span><span class="p">(</span><span class="s2">&quot;run_clm&quot;</span><span class="p">,</span> <span class="n">model_args</span><span class="p">,</span> <span class="n">data_args</span><span class="p">)</span> + + <span class="c1"># Setup logging</span> + <span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span> + <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%(asctime)s</span><span class="s2"> - </span><span class="si">%(levelname)s</span><span class="s2"> - </span><span class="si">%(name)s</span><span class="s2"> - </span><span class="si">%(message)s</span><span class="s2">&quot;</span><span class="p">,</span> + <span class="n">datefmt</span><span class="o">=</span><span class="s2">&quot;%m/</span><span class="si">%d</span><span class="s2">/%Y %H:%M:%S&quot;</span><span class="p">,</span> + <span class="n">handlers</span><span class="o">=</span><span class="p">[</span><span class="n">logging</span><span class="o">.</span><span class="n">StreamHandler</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">)],</span> + <span class="p">)</span> + + <span class="n">log_level</span> <span class="o">=</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">get_process_log_level</span><span class="p">()</span> + <span class="n">logger</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">log_level</span><span class="p">)</span> + <span class="n">datasets</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">logging</span><span class="o">.</span><span class="n">set_verbosity</span><span class="p">(</span><span class="n">log_level</span><span class="p">)</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">logging</span><span class="o">.</span><span class="n">set_verbosity</span><span class="p">(</span><span class="n">log_level</span><span class="p">)</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">logging</span><span class="o">.</span><span class="n">enable_default_handler</span><span class="p">()</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">logging</span><span class="o">.</span><span class="n">enable_explicit_format</span><span class="p">()</span> + + <span class="c1"># Log on each process the small summary:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Process rank: </span><span class="si">{</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">local_rank</span><span class="si">}</span><span class="s2">,&quot;</span> + <span class="sa">f</span><span class="s2">&quot; device: </span><span class="si">{</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">device</span><span class="si">}</span><span class="s2">,&quot;</span> + <span class="sa">f</span><span class="s2">&quot; n_gpu: </span><span class="si">{</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">n_gpu</span><span class="si">}</span><span class="s2">,&quot;</span> + <span class="sa">f</span><span class="s2">&quot;distributed training: </span><span class="si">{</span><span class="nb">bool</span><span class="p">(</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">local_rank</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="si">}</span><span class="s2">,&quot;</span> + <span class="sa">f</span><span class="s2">&quot; 16-bits training: </span><span class="si">{</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">fp16</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Training/evaluation parameters </span><span class="si">{</span><span class="n">finetuner_args</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="c1"># Detecting last checkpoint.</span> + <span class="n">last_checkpoint</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">)</span> <span class="ow">and</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">do_train</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">overwrite_output_dir</span><span class="p">:</span> + <span class="n">last_checkpoint</span> <span class="o">=</span> <span class="n">get_last_checkpoint</span><span class="p">(</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">)</span> + <span class="k">if</span> <span class="n">last_checkpoint</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Output directory (</span><span class="si">{</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">output_dir</span><span class="si">}</span><span class="s2">) already&quot;</span> + <span class="s2">&quot; exists and is not empty. &quot;</span> + <span class="s2">&quot;Use --overwrite_output_dir to overcome.&quot;</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="n">last_checkpoint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">resume_from_checkpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Checkpoint detected, resuming training at&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">last_checkpoint</span><span class="si">}</span><span class="s2">. To avoid this behavior, change&quot;</span> + <span class="s2">&quot; the `--output_dir` or add `--overwrite_output_dir` to&quot;</span> + <span class="s2">&quot; train from scratch.&quot;</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">last_checkpoint</span> <span class="o">=</span> <span class="n">last_checkpoint</span> + + <span class="c1"># Set seed before initializing model.</span> + <span class="n">set_seed</span><span class="p">(</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span> + + +<div class="viewcode-block" id="Finetuner.group_text"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/finetuner/index.html#lmflow.pipeline.finetuner.Finetuner.group_text">[docs]</a> <span class="k">def</span> <span class="nf">group_text</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tokenized_datasets</span><span class="p">,</span> <span class="n">model_max_length</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Groups texts together to form blocks of maximum length `model_max_length` and returns the processed data as</span> +<span class="sd"> a dictionary.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">data_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span> + <span class="n">finetuner_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">finetuner_args</span> + + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">block_size</span> <span class="o">=</span> <span class="n">model_max_length</span> + <span class="k">if</span> <span class="n">block_size</span> <span class="o">&gt;</span> <span class="mi">1024</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;The chosen tokenizer supports a `model_max_length` that is&quot;</span> + <span class="s2">&quot; longer than the default `block_size` value&quot;</span> + <span class="s2">&quot; of 1024. If you would like to use a longer `block_size`&quot;</span> + <span class="s2">&quot; up to `tokenizer.model_max_length` you can override this &quot;</span> + <span class="s2">&quot; default with `--block_size xxx`.&quot;</span> + <span class="p">)</span> + <span class="n">block_size</span> <span class="o">=</span> <span class="mi">1024</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span> <span class="o">&gt;</span> <span class="n">model_max_length</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span><span class="o">.</span><span class="n">truncate_to_model_max_length</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;The block_size passed (</span><span class="si">{</span><span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span><span class="si">}</span><span class="s2">) is larger&quot;</span> + <span class="sa">f</span><span class="s2">&quot; than the maximum length for the model&quot;</span> + <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">model_max_length</span><span class="si">}</span><span class="s2">).&quot;</span> + <span class="sa">f</span><span class="s2">&quot; Using block_size=</span><span class="si">{</span><span class="n">model_max_length</span><span class="si">}</span><span class="s2">.&quot;</span> + <span class="sa">f</span><span class="s2">&quot;If you would like to use a longer &#39;block_size&#39; that is&quot;</span> + <span class="sa">f</span><span class="s2">&quot; longer than the maximum length supported by the model,&quot;</span> + <span class="sa">f</span><span class="s2">&quot; you can override this behavior with&quot;</span> + <span class="sa">f</span><span class="s2">&quot;default with `--truncate_to_model_max_length False`.&quot;</span> + <span class="p">)</span> + <span class="n">block_size</span> <span class="o">=</span> <span class="n">model_max_length</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;The block_size passed (</span><span class="si">{</span><span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span><span class="si">}</span><span class="s2">) is larger&quot;</span> + <span class="sa">f</span><span class="s2">&quot;than the maximum length for the model&quot;</span> + <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">model_max_length</span><span class="si">}</span><span class="s2">).&quot;</span> + <span class="sa">f</span><span class="s2">&quot;Using block_size=</span><span class="si">{</span><span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span> + <span class="n">block_size</span> <span class="o">=</span> <span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">block_size</span> <span class="o">=</span> <span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span> + <span class="c1"># Main data processing function that will concatenate all texts from</span> + <span class="c1"># our dataset and generate chunks of block_size.</span> + <span class="k">def</span> <span class="nf">group_texts</span><span class="p">(</span><span class="n">examples</span><span class="p">):</span> + <span class="c1"># Concatenate all texts.</span> + <span class="n">concatenated_examples</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">chain</span><span class="p">(</span><span class="o">*</span><span class="n">examples</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">examples</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span> + <span class="n">total_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">concatenated_examples</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">examples</span><span class="o">.</span><span class="n">keys</span><span class="p">())[</span><span class="mi">0</span><span class="p">]])</span> + <span class="c1"># We drop the small remainder, we could add padding if the model</span> + <span class="c1"># supported it instead of this drop, you can customize this part to</span> + <span class="c1"># your needs.</span> + <span class="n">total_length</span> <span class="o">=</span> <span class="p">(</span><span class="n">total_length</span> <span class="o">//</span> <span class="n">block_size</span><span class="p">)</span> <span class="o">*</span> <span class="n">block_size</span> + <span class="c1"># Split by chunks of max_len.</span> + <span class="n">result</span> <span class="o">=</span> <span class="p">{</span> + <span class="n">k</span><span class="p">:</span> <span class="p">[</span><span class="n">t</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">block_size</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">total_length</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)]</span> + <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">concatenated_examples</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> + <span class="p">}</span> + <span class="k">return</span> <span class="n">result</span> + + <span class="c1"># Note that with `batched=True`, this map processes 1,000 texts</span> + <span class="c1"># together, so group_texts throws away a remainder for each of those</span> + <span class="c1"># groups of 1,000 texts. You can adjust that batch_size here but a</span> + <span class="c1"># higher value might be slower to preprocess.</span> + <span class="c1">#</span> + <span class="c1"># To speed up this part, we use multiprocessing. See the documentation</span> + <span class="c1"># of the map method for more information:</span> + <span class="c1"># https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map</span> + <span class="k">with</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">main_process_first</span><span class="p">(</span><span class="n">desc</span><span class="o">=</span><span class="s2">&quot;grouping texts together&quot;</span><span class="p">):</span> + <span class="n">group_batch_size</span> <span class="o">=</span> <span class="n">data_args</span><span class="o">.</span><span class="n">group_texts_batch_size</span> + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">disable_group_texts</span><span class="p">:</span> + <span class="n">group_batch_size</span> <span class="o">=</span> <span class="mi">1</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">data_args</span><span class="o">.</span><span class="n">streaming</span><span class="p">:</span> + <span class="n">lm_datasets</span> <span class="o">=</span> <span class="n">tokenized_datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> + <span class="n">group_texts</span><span class="p">,</span> + <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="n">group_batch_size</span><span class="p">,</span> + <span class="n">num_proc</span><span class="o">=</span><span class="n">data_args</span><span class="o">.</span><span class="n">preprocessing_num_workers</span><span class="p">,</span> + <span class="n">load_from_cache_file</span><span class="o">=</span><span class="ow">not</span> <span class="n">data_args</span><span class="o">.</span><span class="n">overwrite_cache</span><span class="p">,</span> + <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Grouping texts in chunks of </span><span class="si">{</span><span class="n">block_size</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">lm_datasets</span> <span class="o">=</span> <span class="n">tokenized_datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> + <span class="n">group_texts</span><span class="p">,</span> + <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="n">group_batch_size</span><span class="p">,</span> + <span class="p">)</span> + + <span class="k">return</span> <span class="n">lm_datasets</span></div> + + +<div class="viewcode-block" id="Finetuner.tune"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/finetuner/index.html#lmflow.pipeline.finetuner.Finetuner.tune">[docs]</a> <span class="k">def</span> <span class="nf">tune</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">transform_dataset_in_place</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform tuning for a model</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> model : TunableModel object.</span> +<span class="sd"> TunableModel to perform tuning.</span> + +<span class="sd"> dataset:</span> +<span class="sd"> dataset to train model.</span> + +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">model_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span> + <span class="n">data_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span> + <span class="n">finetuner_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">finetuner_args</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">transform_dataset_in_place</span><span class="p">:</span> + <span class="n">dataset</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> + + <span class="c1"># Tokenization and text grouping must be done in the main process</span> + <span class="k">with</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">main_process_first</span><span class="p">(</span><span class="n">desc</span><span class="o">=</span><span class="s2">&quot;dataset map tokenization&quot;</span><span class="p">):</span> + <span class="n">tokenized_dataset</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> + <span class="n">lm_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">group_text</span><span class="p">(</span> + <span class="n">tokenized_dataset</span><span class="p">,</span> + <span class="n">model_max_length</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">get_max_length</span><span class="p">(),</span> + <span class="p">)</span> + + <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">lm_dataset</span><span class="o">.</span><span class="n">get_backend_dataset</span><span class="p">()</span> + + <span class="k">if</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">do_eval</span><span class="p">:</span> + <span class="n">eval_dataset_args</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">data_args</span><span class="p">)</span> + <span class="n">eval_dataset_args</span><span class="o">.</span><span class="n">dataset_path</span> <span class="o">=</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">eval_dataset_path</span> + <span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">eval_dataset_args</span><span class="p">)</span> + <span class="k">with</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">main_process_first</span><span class="p">(</span><span class="n">desc</span><span class="o">=</span><span class="s2">&quot;dataset map tokenization&quot;</span><span class="p">):</span> + <span class="n">tokenized_dataset</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span> + <span class="n">lm_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">group_text</span><span class="p">(</span> + <span class="n">tokenized_dataset</span><span class="p">,</span> + <span class="n">model_max_length</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">get_max_length</span><span class="p">(),</span> + <span class="p">)</span> + <span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">lm_dataset</span><span class="o">.</span><span class="n">get_backend_dataset</span><span class="p">()</span> + + + <span class="k">def</span> <span class="nf">preprocess_logits_for_metrics</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span> + <span class="c1"># Depending on the model and config, logits may contain extra tensors,</span> + <span class="c1"># like past_key_values, but logits always come first</span> + <span class="n">logits</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + <span class="k">return</span> <span class="n">logits</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> + + <span class="n">metric</span> <span class="o">=</span> <span class="n">evaluate</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;accuracy&quot;</span><span class="p">)</span> + + <span class="k">def</span> <span class="nf">compute_metrics</span><span class="p">(</span><span class="n">eval_preds</span><span class="p">):</span> + <span class="c1"># import pdb; pdb.set_trace()</span> + <span class="n">preds</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">eval_preds</span> + <span class="c1"># preds have the same shape as the labels, after the argmax(-1) has been calculated</span> + <span class="c1"># by preprocess_logits_for_metrics but we need to shift the labels</span> + <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> + <span class="n">preds</span> <span class="o">=</span> <span class="n">preds</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> + <span class="k">return</span> <span class="n">metric</span><span class="o">.</span><span class="n">compute</span><span class="p">(</span><span class="n">predictions</span><span class="o">=</span><span class="n">preds</span><span class="p">,</span> <span class="n">references</span><span class="o">=</span><span class="n">labels</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">finetuner_args</span><span class="o">.</span><span class="n">do_train</span><span class="p">:</span> + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">max_train_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">max_train_samples</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">),</span> <span class="n">data_args</span><span class="o">.</span><span class="n">max_train_samples</span><span class="p">)</span> + <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">train_dataset</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">max_train_samples</span><span class="p">))</span> + + <span class="c1"># Initialize our Trainer</span> + <span class="n">training_args</span> <span class="o">=</span> <span class="n">finetuner_args</span> + + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_lora</span><span class="p">:</span> + <span class="n">FinetuningTrainer</span> <span class="o">=</span> <span class="n">PeftTrainer</span> + <span class="n">trainer_callbacks</span> <span class="o">=</span> <span class="p">[</span><span class="n">PeftSavingCallback</span><span class="p">]</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">FinetuningTrainer</span> <span class="o">=</span> <span class="n">Trainer</span> + <span class="n">trainer_callbacks</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="n">trainer</span> <span class="o">=</span> <span class="n">FinetuningTrainer</span><span class="p">(</span> + <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">(),</span> + <span class="n">args</span><span class="o">=</span><span class="n">training_args</span><span class="p">,</span> + <span class="n">train_dataset</span><span class="o">=</span><span class="n">train_dataset</span> <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">do_train</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">eval_dataset</span><span class="o">=</span><span class="n">eval_dataset</span> <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">do_eval</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">tokenizer</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">get_tokenizer</span><span class="p">(),</span> + <span class="c1"># Data collator will default to DataCollatorWithPadding, so we change it.</span> + <span class="n">data_collator</span><span class="o">=</span><span class="n">default_data_collator</span><span class="p">,</span> + <span class="n">compute_metrics</span><span class="o">=</span><span class="n">compute_metrics</span> <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">do_eval</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">preprocess_logits_for_metrics</span><span class="o">=</span><span class="n">preprocess_logits_for_metrics</span> <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">do_eval</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">callbacks</span><span class="o">=</span><span class="n">trainer_callbacks</span> + <span class="p">)</span> + + <span class="c1"># Training</span> + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">do_train</span><span class="p">:</span> + <span class="n">checkpoint</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">last_checkpoint</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_checkpoint</span> + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">resume_from_checkpoint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">training_args</span><span class="o">.</span><span class="n">resume_from_checkpoint</span> + <span class="k">elif</span> <span class="n">last_checkpoint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">checkpoint</span> <span class="o">=</span> <span class="n">last_checkpoint</span> + <span class="n">train_result</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="n">checkpoint</span><span class="p">)</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="n">model_args</span><span class="o">.</span><span class="n">use_lora</span><span class="p">:</span> + <span class="n">trainer</span><span class="o">.</span><span class="n">save_model</span><span class="p">()</span> <span class="c1"># Saves the tokenizer too for easy upload</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">model_args</span><span class="o">.</span><span class="n">save_aggregated_lora</span><span class="p">:</span> + <span class="n">model</span><span class="o">.</span><span class="n">merge_lora_weights</span><span class="p">()</span> + <span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">finetuner_args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span><span class="n">model_args</span><span class="o">.</span><span class="n">save_aggregated_lora</span><span class="p">)</span> + + <span class="n">metrics</span> <span class="o">=</span> <span class="n">train_result</span><span class="o">.</span><span class="n">metrics</span> + + <span class="n">max_train_samples</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">data_args</span><span class="o">.</span><span class="n">max_train_samples</span> <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">max_train_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">)</span> + <span class="p">)</span> + <span class="n">metrics</span><span class="p">[</span><span class="s2">&quot;train_samples&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">max_train_samples</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">))</span> + + <span class="n">trainer</span><span class="o">.</span><span class="n">log_metrics</span><span class="p">(</span><span class="s2">&quot;train&quot;</span><span class="p">,</span> <span class="n">metrics</span><span class="p">)</span> + <span class="n">trainer</span><span class="o">.</span><span class="n">save_metrics</span><span class="p">(</span><span class="s2">&quot;train&quot;</span><span class="p">,</span> <span class="n">metrics</span><span class="p">)</span> + <span class="n">trainer</span><span class="o">.</span><span class="n">save_state</span><span class="p">()</span> + + <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;finetuned_from&quot;</span><span class="p">:</span> <span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> <span class="s2">&quot;tasks&quot;</span><span class="p">:</span> <span class="s2">&quot;text-generation&quot;</span><span class="p">}</span> + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">dataset_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dataset_tags&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_args</span><span class="o">.</span><span class="n">dataset_name</span> + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">dataset_config_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dataset_args&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_args</span><span class="o">.</span><span class="n">dataset_config_name</span> + <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dataset&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">data_args</span><span class="o">.</span><span class="n">dataset_name</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">data_args</span><span class="o">.</span><span class="n">dataset_config_name</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dataset&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_args</span><span class="o">.</span><span class="n">dataset_name</span> + + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">push_to_hub</span><span class="p">:</span> + <span class="n">trainer</span><span class="o">.</span><span class="n">push_to_hub</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">trainer</span><span class="o">.</span><span class="n">create_model_card</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">model</span></div></div> 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coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;The Inferencer class simplifies the process of model inferencing.&quot;&quot;&quot;</span> + +<span class="kn">import</span> <span class="nn">copy</span> +<span class="kn">import</span> <span class="nn">os</span> +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">wandb</span> +<span class="kn">import</span> <span class="nn">deepspeed</span> +<span class="kn">import</span> <span class="nn">sys</span> +<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> +<span class="kn">import</span> <span class="nn">datetime</span> +<span class="kn">import</span> <span class="nn">json</span> +<span class="kn">import</span> <span class="nn">time</span> + +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoConfig</span> +<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span> + +<span class="kn">from</span> <span class="nn">lmflow.args</span> <span class="kn">import</span> <span class="n">DatasetArguments</span> +<span class="kn">from</span> <span class="nn">lmflow.datasets.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.base_pipeline</span> <span class="kn">import</span> <span class="n">BasePipeline</span> +<span class="kn">from</span> <span class="nn">lmflow.models.hf_decoder_model</span> <span class="kn">import</span> <span class="n">HFDecoderModel</span> +<span class="kn">from</span> <span class="nn">lmflow.utils.data_utils</span> <span class="kn">import</span> <span class="p">(</span><span class="n">set_random_seed</span><span class="p">,</span> <span class="n">batchlize</span><span class="p">,</span> + <span class="n">answer_extraction</span><span class="p">,</span> <span class="n">process_image_flag</span><span class="p">)</span> +<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;TOKENIZERS_PARALLELISM&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;false&quot;</span> <span class="c1"># To avoid warnings about parallelism in tokenizers</span> +<div class="viewcode-block" id="rstrip_partial_utf8"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.rstrip_partial_utf8">[docs]</a><span class="k">def</span> <span class="nf">rstrip_partial_utf8</span><span class="p">(</span><span class="n">string</span><span class="p">):</span> + <span class="k">return</span> <span class="n">string</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\ufffd</span><span class="s2">&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span></div> + +<div class="viewcode-block" id="supported_dataset_type"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.supported_dataset_type">[docs]</a><span class="n">supported_dataset_type</span> <span class="o">=</span> <span class="p">[</span> + <span class="s2">&quot;text_only&quot;</span><span class="p">,</span> + <span class="s2">&quot;image_text&quot;</span><span class="p">,</span> +<span class="p">]</span></div> + +<div class="viewcode-block" id="Inferencer"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.Inferencer">[docs]</a><span class="k">class</span> <span class="nc">Inferencer</span><span class="p">(</span><span class="n">BasePipeline</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes the `Inferencer` class with given arguments.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> model_args : ModelArguments object.</span> +<span class="sd"> Contains the arguments required to load the model.</span> +<span class="sd"> </span> +<span class="sd"> data_args : DatasetArguments object.</span> +<span class="sd"> Contains the arguments required to load the dataset.</span> + +<span class="sd"> inferencer_args : InferencerArguments object.</span> +<span class="sd"> Contains the arguments required to perform inference.</span> + + +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_args</span><span class="p">,</span> <span class="n">data_args</span><span class="p">,</span> <span class="n">inferencer_args</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span> <span class="o">=</span> <span class="n">data_args</span> + <span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span> <span class="o">=</span> <span class="n">inferencer_args</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span> <span class="o">=</span> <span class="n">model_args</span> + + <span class="n">set_random_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span><span class="o">.</span><span class="n">random_seed</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;LOCAL_RANK&quot;</span><span class="p">,</span> <span class="s2">&quot;0&quot;</span><span class="p">))</span> + <span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;WORLD_SIZE&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">))</span> + <span class="k">if</span> <span class="n">inferencer_args</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span><span class="p">:</span> + <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> <span class="c1"># NOTE: cpu-only machine will have error</span> + <span class="n">deepspeed</span><span class="o">.</span><span class="n">init_distributed</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;MASTER_ADDR&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;localhost&quot;</span> + <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;MASTER_PORT&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;15000&quot;</span> + <span class="n">dist</span><span class="o">.</span><span class="n">init_process_group</span><span class="p">(</span> + <span class="s2">&quot;gloo&quot;</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">world_size</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">config</span> <span class="o">=</span> <span class="n">AutoConfig</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">model_args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="p">,</span> <span class="n">trust_remote_code</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="k">try</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_hidden_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">hidden_size</span> + <span class="k">except</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Error in setting hidden size, use the default size 1024&quot;</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_hidden_size</span> <span class="o">=</span> <span class="mi">1024</span> <span class="c1"># gpt2 seems do not have hidden_size in config</span> + + +<div class="viewcode-block" id="Inferencer.create_dataloader"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.Inferencer.create_dataloader">[docs]</a> <span class="k">def</span> <span class="nf">create_dataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">):</span> +<span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Batchlize dataset and format it to dataloader.</span> + +<span class="sd"> Args:</span> +<span class="sd"> dataset (Dataset): the dataset object</span> + +<span class="sd"> Output:</span> +<span class="sd"> dataloader (batchlize): the dataloader object</span> +<span class="sd"> dataset_size (int): the length of the dataset</span> + +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_type</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;text_only&quot;</span><span class="p">:</span> + <span class="n">data_dict</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">instance</span><span class="p">[</span><span class="s2">&quot;text&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">data_dict</span><span class="p">[</span><span class="s2">&quot;instances&quot;</span><span class="p">]</span> <span class="p">]</span> + <span class="k">elif</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_type</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;image_text&quot;</span><span class="p">:</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">to_list</span><span class="p">()</span> + + <span class="n">dataset_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> + <span class="n">dataset_buf</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">dataset_size</span><span class="p">):</span> + <span class="n">dataset_buf</span><span class="o">.</span><span class="n">append</span><span class="p">({</span> + <span class="s2">&quot;input&quot;</span><span class="p">:</span> <span class="n">inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> + <span class="s2">&quot;input_idx&quot;</span><span class="p">:</span> <span class="n">idx</span> + <span class="p">})</span> + + <span class="n">dataloader</span> <span class="o">=</span> <span class="n">batchlize</span><span class="p">(</span> + <span class="n">dataset_buf</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="n">random_shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">dataloader</span><span class="p">,</span> <span class="n">dataset_size</span></div> + + +<div class="viewcode-block" id="Inferencer.inference"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.Inferencer.inference">[docs]</a> <span class="k">def</span> <span class="nf">inference</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> + <span class="n">max_new_tokens</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> + <span class="n">temperature</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> + <span class="n">prompt_structure</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">{input}</span><span class="s1">&#39;</span><span class="p">,</span> + <span class="n">remove_image_flag</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform inference for a model</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> model : TunableModel object.</span> +<span class="sd"> TunableModel to perform inference</span> + +<span class="sd"> dataset : Dataset object.</span> +<span class="sd"> </span> + +<span class="sd"> Returns:</span> + +<span class="sd"> output_dataset: Dataset object.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_type</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">supported_dataset_type</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="s1">&#39;input dataset should have type </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> + <span class="n">supported_dataset_type</span><span class="p">))</span> + <span class="n">dataloader</span><span class="p">,</span> <span class="n">data_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_dataloader</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> + + <span class="c1"># The output dataset</span> + <span class="n">output_dict</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;text_only&quot;</span><span class="p">,</span> + <span class="s2">&quot;instances&quot;</span><span class="p">:</span> <span class="p">[</span> + <span class="p">]</span> + <span class="p">}</span> + + <span class="k">for</span> <span class="n">batch_index</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span> + <span class="n">current_batch</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># batch size is 1</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">current_batch</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span> + <span class="nb">input</span> <span class="o">=</span> <span class="n">prompt_structure</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="n">current_batch</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">])</span> + <span class="k">else</span><span class="p">:</span> + <span class="nb">input</span> <span class="o">=</span> <span class="n">current_batch</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> + <span class="nb">input</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">prompt_structure</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="nb">input</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">])</span> + + <span class="k">if</span> <span class="s1">&#39;images&#39;</span> <span class="ow">in</span> <span class="nb">input</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">[</span><span class="s1">&#39;images&#39;</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span> + <span class="nb">input</span><span class="p">[</span><span class="s1">&#39;images&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">input</span><span class="p">[</span><span class="s1">&#39;images&#39;</span><span class="p">])</span> + + <span class="k">if</span> <span class="n">remove_image_flag</span><span class="p">:</span> + <span class="c1"># remove the image flag &lt;ImageHere&gt; in tokenization;</span> + <span class="nb">input</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;&lt;ImageHere&gt;&quot;</span><span class="p">)</span> + <span class="c1"># TODO remove this code by update the tokenizer</span> + <span class="n">input_ids</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">attention_mask</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">pixel_values</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">image_token_indexes</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">temp_input</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> + <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="nb">input</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">])):</span> + <span class="n">temp_input</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">input</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">][</span><span class="n">idx</span><span class="p">]</span> + <span class="n">temp_inputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span> + <span class="n">temp_input</span><span class="p">,</span> + <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">,</span> + <span class="n">add_special_tokens</span><span class="o">=</span><span class="n">idx</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> + <span class="n">input_ids</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">temp_inputs</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">])</span> + <span class="n">attention_mask</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">temp_inputs</span><span class="p">[</span><span class="s1">&#39;attention_mask&#39;</span><span class="p">])</span> + <span class="n">image_token_indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">temp_inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">image_token_indexes</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">image_token_indexes</span> <span class="o">=</span> <span class="n">image_token_indexes</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">temp_inputs</span> + <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">input_ids</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> + <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;attention_mask&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">attention_mask</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span><span class="p">:</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;cpu&quot;</span><span class="p">:</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;device </span><span class="se">\&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span><span class="o">.</span><span class="n">device</span><span class="si">}</span><span class="se">\&quot;</span><span class="s2"> is not supported&quot;</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">remove_image_flag</span><span class="p">:</span> + <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;image_token_indexes&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">image_token_indexes</span> + <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;one_sample_multiple_images&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span> + + <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span> + <span class="n">inputs</span><span class="p">,</span> + <span class="n">max_new_tokens</span><span class="o">=</span><span class="n">max_new_tokens</span><span class="p">,</span> + <span class="n">temperature</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span><span class="o">.</span><span class="n">temperature</span><span class="p">,</span> + <span class="n">repetition_penalty</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span><span class="o">.</span><span class="n">repetition_penalty</span><span class="p">,</span> + <span class="n">do_sample</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span><span class="o">.</span><span class="n">do_sample</span><span class="p">,</span> + <span class="p">)</span> + + <span class="c1"># only return the generation, trucating the input</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span><span class="o">.</span><span class="n">arch_type</span> <span class="o">!=</span> <span class="s2">&quot;vision_encoder_decoder&quot;</span><span class="p">:</span> + <span class="n">text_out</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">prompt_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">,))</span> + <span class="n">text_out</span> <span class="o">=</span> <span class="n">text_out</span><span class="p">[</span><span class="n">prompt_length</span><span class="p">:]</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># to avoid redundant/missing leading space problem, we use a</span> + <span class="c1"># part of the input text</span> + <span class="n">input_text</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">:]</span> + <span class="n">text_out</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">input_text</span><span class="p">,</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]]),</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">prompt_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">input_text</span><span class="p">,</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">,))</span> + <span class="n">text_out</span> <span class="o">=</span> <span class="n">text_out</span><span class="p">[</span><span class="n">prompt_length</span><span class="p">:]</span> + + <span class="n">output_dict</span><span class="p">[</span><span class="s2">&quot;instances&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">({</span> <span class="s2">&quot;text&quot;</span><span class="p">:</span> <span class="n">text_out</span> <span class="p">})</span> + + <span class="n">output_dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">DatasetArguments</span><span class="p">(</span><span class="n">dataset_path</span> <span class="o">=</span> <span class="kc">None</span><span class="p">))</span> + <span class="n">output_dataset</span> <span class="o">=</span> <span class="n">output_dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">output_dict</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">output_dataset</span></div> + +<div class="viewcode-block" id="Inferencer.stream_inference"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.Inferencer.stream_inference">[docs]</a> <span class="k">def</span> <span class="nf">stream_inference</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">context</span><span class="p">,</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">max_new_tokens</span><span class="p">,</span> + <span class="n">token_per_step</span><span class="p">,</span> + <span class="n">temperature</span><span class="p">,</span> + <span class="n">end_string</span><span class="p">,</span> + <span class="n">input_dataset</span><span class="p">,</span> + <span class="n">remove_image_flag</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="p">):</span> + <span class="n">response</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> + <span class="n">history</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">if</span> <span class="s2">&quot;ChatGLMModel&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">architectures</span><span class="p">:</span> + <span class="k">for</span> <span class="n">response</span><span class="p">,</span> <span class="n">history</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()</span><span class="o">.</span><span class="n">stream_chat</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">get_tokenizer</span><span class="p">(),</span> <span class="n">context</span><span class="p">,</span> <span class="n">history</span><span class="o">=</span><span class="n">history</span><span class="p">):</span> + <span class="n">response</span> <span class="o">=</span> <span class="n">rstrip_partial_utf8</span><span class="p">(</span><span class="n">response</span><span class="p">)</span> + <span class="k">yield</span> <span class="n">response</span><span class="p">,</span> <span class="kc">False</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span><span class="o">.</span><span class="n">max_new_tokens</span> <span class="o">//</span> <span class="n">token_per_step</span><span class="p">):</span> + <span class="n">output_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span> + <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> + <span class="n">dataset</span><span class="o">=</span><span class="n">input_dataset</span><span class="p">,</span> + <span class="n">max_new_tokens</span><span class="o">=</span><span class="n">token_per_step</span><span class="p">,</span> + <span class="n">temperature</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">inferencer_args</span><span class="o">.</span><span class="n">temperature</span><span class="p">,</span> + <span class="n">remove_image_flag</span><span class="o">=</span><span class="n">remove_image_flag</span><span class="p">,</span> + <span class="p">)</span> + + <span class="n">new_append_text</span> <span class="o">=</span> <span class="n">output_dataset</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()[</span><span class="s2">&quot;instances&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;text&quot;</span><span class="p">]</span> + <span class="n">new_append_text</span> <span class="o">=</span> <span class="n">rstrip_partial_utf8</span><span class="p">(</span><span class="n">new_append_text</span><span class="p">)</span> + <span class="n">response</span> <span class="o">+=</span> <span class="n">new_append_text</span> + + <span class="n">input_dict</span> <span class="o">=</span> <span class="n">input_dataset</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()</span> + <span class="n">input_dict</span><span class="p">[</span><span class="s2">&quot;instances&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;text&quot;</span><span class="p">]</span> <span class="o">+=</span> <span class="n">new_append_text</span> + <span class="n">input_dataset</span> <span class="o">=</span> <span class="n">input_dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">input_dict</span><span class="p">)</span> + + <span class="n">flag_break</span> <span class="o">=</span> <span class="kc">False</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">index</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">end_string</span><span class="p">)</span> + <span class="n">flag_break</span> <span class="o">=</span> <span class="kc">True</span> + <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span> + <span class="n">response</span> <span class="o">+=</span> <span class="n">end_string</span> + <span class="n">index</span> <span class="o">=</span> <span class="n">response</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">end_string</span><span class="p">)</span> + + <span class="n">response</span> <span class="o">=</span> <span class="n">response</span><span class="p">[:</span><span class="n">index</span><span class="p">]</span> + + <span class="k">yield</span> <span class="n">response</span><span class="p">,</span> <span class="n">flag_break</span></div></div> +</pre></div> + + </article> + + + + 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coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="sd">The Aligner class simplifies the process of running alignment.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">import</span> <span class="nn">logging</span> +<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> +<span class="kn">import</span> <span class="nn">os</span> +<span class="kn">import</span> <span class="nn">sys</span> +<span class="kn">import</span> <span class="nn">time</span> +<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">chain</span> + +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span> +<span class="kn">import</span> <span class="nn">transformers</span> +<span class="kn">from</span> <span class="nn">datasets</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">set_caching_enabled</span><span class="p">,</span> + <span class="n">Dataset</span><span class="p">,</span> + <span class="n">DatasetDict</span><span class="p">,</span> +<span class="p">)</span> +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">default_data_collator</span><span class="p">,</span> + <span class="n">pipeline</span><span class="p">,</span> + <span class="n">set_seed</span><span class="p">,</span> +<span class="p">)</span> +<span class="kn">from</span> <span class="nn">transformers.testing_utils</span> <span class="kn">import</span> <span class="n">CaptureLogger</span> + +<span class="kn">from</span> <span class="nn">lmflow.args</span> <span class="kn">import</span> <span class="n">DatasetArguments</span> +<span class="kn">from</span> <span class="nn">lmflow.datasets.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span> <span class="k">as</span> <span class="n">LMFlowDataset</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.base_aligner</span> <span class="kn">import</span> <span class="n">BaseAligner</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.utils.raft_trainer</span> <span class="kn">import</span> <span class="n">RaftTrainer</span> + +<div class="viewcode-block" id="logger"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.logger">[docs]</a><span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="RaftAligner"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner">[docs]</a><span class="k">class</span> <span class="nc">RaftAligner</span><span class="p">(</span><span class="n">BaseAligner</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes the `RaftAligner` class with given arguments.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> model_args : ModelArguments object.</span> +<span class="sd"> Contains the arguments required to load the model.</span> +<span class="sd"> </span> +<span class="sd"> data_args : DatasetArguments object.</span> +<span class="sd"> Contains the arguments required to load the dataset.</span> + +<span class="sd"> raft_aligner_args : RaftAlignerArguments object.</span> +<span class="sd"> Contains the arguments required to perform alignment.</span> + +<span class="sd"> args : Optional.</span> +<span class="sd"> Positional arguments.</span> +<span class="sd"> </span> +<span class="sd"> kwargs : Optional.</span> +<span class="sd"> Keyword arguments.</span> + +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_args</span><span class="p">,</span> <span class="n">data_args</span><span class="p">,</span> <span class="n">aligner_args</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span> <span class="o">=</span> <span class="n">model_args</span> + <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span> <span class="o">=</span> <span class="n">data_args</span> + <span class="bp">self</span><span class="o">.</span><span class="n">aligner_args</span> <span class="o">=</span> <span class="n">aligner_args</span> + + <span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span> + <span class="nb">format</span><span class="o">=</span><span class="s2">&quot;</span><span class="si">%(asctime)s</span><span class="s2"> - </span><span class="si">%(levelname)s</span><span class="s2"> - </span><span class="si">%(name)s</span><span class="s2"> - </span><span class="si">%(message)s</span><span class="s2">&quot;</span><span class="p">,</span> + <span class="n">datefmt</span><span class="o">=</span><span class="s2">&quot;%m/</span><span class="si">%d</span><span class="s2">/%Y %H:%M:%S&quot;</span><span class="p">,</span> + <span class="n">handlers</span><span class="o">=</span><span class="p">[</span><span class="n">logging</span><span class="o">.</span><span class="n">StreamHandler</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">)],</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">INF</span> <span class="o">=</span> <span class="mi">888888888</span> + <span class="n">logger</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">)</span> + + <span class="n">output_reward_path</span> <span class="o">=</span> <span class="n">aligner_args</span><span class="o">.</span><span class="n">output_reward_path</span> + <span class="k">if</span> <span class="n">output_reward_path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">output_reward_path</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="c1"># Deletes a maybe-exist file</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">output_reward_path</span><span class="p">)</span> + <span class="k">except</span> <span class="ne">OSError</span><span class="p">:</span> + <span class="k">pass</span> + + +<div class="viewcode-block" id="RaftAligner._initialize_trainer"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._initialize_trainer">[docs]</a> <span class="k">def</span> <span class="nf">_initialize_trainer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">training_args</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> This function takes the model and tokenizer as the input and initialize the trainer.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">trainer</span> <span class="o">=</span> <span class="n">RaftTrainer</span><span class="p">(</span> + <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> + <span class="n">args</span><span class="o">=</span><span class="n">training_args</span><span class="p">,</span> + <span class="n">train_dataset</span><span class="o">=</span><span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">({</span><span class="s2">&quot;text&quot;</span><span class="p">:</span> <span class="p">[</span> <span class="s2">&quot; &quot;</span> <span class="p">]</span> <span class="p">}),</span> + <span class="n">eval_dataset</span><span class="o">=</span><span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">({}),</span> + <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span> + <span class="n">data_collator</span><span class="o">=</span><span class="n">default_data_collator</span><span class="p">,</span> + <span class="n">compute_metrics</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">preprocess_logits_for_metrics</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">trainer</span></div> + + +<div class="viewcode-block" id="RaftAligner._load_dataset"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._load_dataset">[docs]</a> <span class="k">def</span> <span class="nf">_load_dataset</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">selected_dataset</span><span class="p">,</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">tokenizer</span><span class="p">,</span> + <span class="n">model_args</span><span class="p">,</span> + <span class="n">data_args</span><span class="p">,</span> + <span class="n">training_args</span><span class="p">,</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&#39;&#39;&#39;</span> +<span class="sd"> This function prepares the dataset for every iteration.</span> +<span class="sd"> &#39;&#39;&#39;</span> + <span class="n">raw_datasets</span> <span class="o">=</span> <span class="n">selected_dataset</span> + + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">do_train</span><span class="p">:</span> + <span class="n">column_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">raw_datasets</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">features</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">column_names</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">raw_datasets</span><span class="p">[</span><span class="s2">&quot;validation&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">features</span><span class="p">)</span> + <span class="n">text_column_name</span> <span class="o">=</span> <span class="s2">&quot;text&quot;</span> <span class="k">if</span> <span class="s2">&quot;text&quot;</span> <span class="ow">in</span> <span class="n">column_names</span> <span class="k">else</span> <span class="n">column_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + + <span class="c1"># since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function</span> + <span class="n">tok_logger</span> <span class="o">=</span> <span class="n">transformers</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">logging</span><span class="o">.</span><span class="n">get_logger</span><span class="p">(</span><span class="s2">&quot;transformers.tokenization_utils_base&quot;</span><span class="p">)</span> + + <span class="k">def</span> <span class="nf">tokenize_function</span><span class="p">(</span><span class="n">examples</span><span class="p">):</span> + <span class="k">with</span> <span class="n">CaptureLogger</span><span class="p">(</span><span class="n">tok_logger</span><span class="p">)</span> <span class="k">as</span> <span class="n">cl</span><span class="p">:</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">examples</span><span class="p">[</span><span class="n">text_column_name</span><span class="p">])</span> + <span class="c1"># clm input could be much much longer than block_size</span> + <span class="k">if</span> <span class="s2">&quot;Token indices sequence length is longer than the&quot;</span> <span class="ow">in</span> <span class="n">cl</span><span class="o">.</span><span class="n">out</span><span class="p">:</span> + <span class="n">tok_logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits&quot;</span> + <span class="s2">&quot; before being passed to the model.&quot;</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">output</span> + + <span class="k">with</span> <span class="n">training_args</span><span class="o">.</span><span class="n">main_process_first</span><span class="p">(</span><span class="n">desc</span><span class="o">=</span><span class="s2">&quot;dataset map tokenization&quot;</span><span class="p">):</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">data_args</span><span class="o">.</span><span class="n">streaming</span><span class="p">:</span> + <span class="n">tokenized_datasets</span> <span class="o">=</span> <span class="n">raw_datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> + <span class="n">tokenize_function</span><span class="p">,</span> + <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">num_proc</span><span class="o">=</span><span class="n">data_args</span><span class="o">.</span><span class="n">preprocessing_num_workers</span><span class="p">,</span> + <span class="n">remove_columns</span><span class="o">=</span><span class="n">column_names</span><span class="p">,</span> + <span class="n">load_from_cache_file</span><span class="o">=</span><span class="ow">not</span> <span class="n">data_args</span><span class="o">.</span><span class="n">overwrite_cache</span><span class="p">,</span> + <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Running tokenizer on dataset&quot;</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">tokenized_datasets</span> <span class="o">=</span> <span class="n">raw_datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> + <span class="n">tokenize_function</span><span class="p">,</span> + <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">remove_columns</span><span class="o">=</span><span class="n">column_names</span><span class="p">,</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">block_size</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">model_max_length</span> + <span class="k">if</span> <span class="n">block_size</span> <span class="o">&gt;</span> <span class="mi">1024</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value&quot;</span> + <span class="s2">&quot; of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can&quot;</span> + <span class="s2">&quot; override this default with `--block_size xxx`.&quot;</span> + <span class="p">)</span> + <span class="n">block_size</span> <span class="o">=</span> <span class="mi">512</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span> <span class="o">&gt;</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">model_max_length</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;The block_size passed (</span><span class="si">{</span><span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span><span class="si">}</span><span class="s2">) is larger than the maximum length for the model&quot;</span> + <span class="sa">f</span><span class="s2">&quot;(</span><span class="si">{</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">model_max_length</span><span class="si">}</span><span class="s2">). Using block_size=</span><span class="si">{</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">model_max_length</span><span class="si">}</span><span class="s2">.&quot;</span> + <span class="p">)</span> + <span class="n">block_size</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">data_args</span><span class="o">.</span><span class="n">block_size</span><span class="p">,</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">model_max_length</span><span class="p">)</span> + + <span class="c1"># Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.</span> + <span class="k">def</span> <span class="nf">group_texts</span><span class="p">(</span><span class="n">examples</span><span class="p">):</span> + <span class="c1"># Concatenate all texts.</span> + <span class="n">concatenated_examples</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">chain</span><span class="p">(</span><span class="o">*</span><span class="n">examples</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">examples</span><span class="o">.</span><span class="n">keys</span><span class="p">()}</span> + <span class="n">total_length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">concatenated_examples</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">examples</span><span class="o">.</span><span class="n">keys</span><span class="p">())[</span><span class="mi">0</span><span class="p">]])</span> + <span class="c1"># We drop the small remainder, we could add padding if the model supported it instead of this drop, you can</span> + <span class="c1"># customize this part to your needs.</span> + <span class="k">if</span> <span class="n">total_length</span> <span class="o">&gt;=</span> <span class="n">block_size</span><span class="p">:</span> + <span class="n">total_length</span> <span class="o">=</span> <span class="p">(</span><span class="n">total_length</span> <span class="o">//</span> <span class="n">block_size</span><span class="p">)</span> <span class="o">*</span> <span class="n">block_size</span> + <span class="c1"># Split by chunks of max_len.</span> + <span class="n">result</span> <span class="o">=</span> <span class="p">{</span> + <span class="n">k</span><span class="p">:</span> <span class="p">[</span><span class="n">t</span><span class="p">[</span><span class="n">i</span> <span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">block_size</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">total_length</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)]</span> + <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">concatenated_examples</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> + <span class="p">}</span> + <span class="n">result</span><span class="p">[</span><span class="s2">&quot;labels&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> + <span class="k">return</span> <span class="n">result</span> + + <span class="c1"># Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder</span> + <span class="c1"># for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower</span> + <span class="c1"># to preprocess.</span> + <span class="c1">#</span> + <span class="c1"># To speed up this part, we use multiprocessing. See the documentation of the map method for more information:</span> + <span class="c1"># https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map</span> + + <span class="k">with</span> <span class="n">training_args</span><span class="o">.</span><span class="n">main_process_first</span><span class="p">(</span><span class="n">desc</span><span class="o">=</span><span class="s2">&quot;grouping texts together&quot;</span><span class="p">):</span> + <span class="n">group_batch_size</span> <span class="o">=</span> <span class="mi">1000</span> + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">disable_group_texts</span><span class="p">:</span> + <span class="n">group_batch_size</span> <span class="o">=</span> <span class="mi">1</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">data_args</span><span class="o">.</span><span class="n">streaming</span><span class="p">:</span> + <span class="n">lm_datasets</span> <span class="o">=</span> <span class="n">tokenized_datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> + <span class="n">group_texts</span><span class="p">,</span> + <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="n">group_batch_size</span><span class="p">,</span> + <span class="n">num_proc</span><span class="o">=</span><span class="n">data_args</span><span class="o">.</span><span class="n">preprocessing_num_workers</span><span class="p">,</span> + <span class="n">load_from_cache_file</span><span class="o">=</span><span class="ow">not</span> <span class="n">data_args</span><span class="o">.</span><span class="n">overwrite_cache</span><span class="p">,</span> + <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;Grouping texts in chunks of </span><span class="si">{</span><span class="n">block_size</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">lm_datasets</span> <span class="o">=</span> <span class="n">tokenized_datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span> + <span class="n">group_texts</span><span class="p">,</span> + <span class="n">batched</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="n">group_batch_size</span><span class="p">,</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">do_train</span><span class="p">:</span> + <span class="k">if</span> <span class="s2">&quot;train&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">tokenized_datasets</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;--do_train requires a train dataset&quot;</span><span class="p">)</span> + <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">lm_datasets</span><span class="p">[</span><span class="s2">&quot;train&quot;</span><span class="p">]</span> + <span class="k">if</span> <span class="n">data_args</span><span class="o">.</span><span class="n">max_train_samples</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">max_train_samples</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">),</span> <span class="n">data_args</span><span class="o">.</span><span class="n">max_train_samples</span><span class="p">)</span> + <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">train_dataset</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">max_train_samples</span><span class="p">))</span> + + <span class="k">return</span> <span class="n">train_dataset</span></div> + + +<div class="viewcode-block" id="RaftAligner._load_input_dataset"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._load_input_dataset">[docs]</a> <span class="k">def</span> <span class="nf">_load_input_dataset</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Load input dataset (i.e. prompt/question dataset) for training.</span> + +<span class="sd"> Args:</span> +<span class="sd"> dataset: A Dataset object.</span> +<span class="sd"> The dataset to be loaded.</span> + +<span class="sd"> Returns:</span> +<span class="sd"> dataloader (`torch.utils.data.DataLoader`):</span> +<span class="sd"> The dataloader for the dataset.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">ds</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">get_backend_dataset</span><span class="p">()</span> + + <span class="k">def</span> <span class="nf">tokenize</span><span class="p">(</span><span class="n">sample</span><span class="p">):</span> + <span class="n">sample</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="s2">&quot;text&quot;</span><span class="p">])</span> + <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">])</span> + <span class="k">return</span> <span class="n">sample</span> + + <span class="n">ds</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">tokenize</span><span class="p">,</span> <span class="n">batched</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="n">ds</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="mi">256</span><span class="p">)</span> + + <span class="n">ds</span><span class="o">.</span><span class="n">set_format</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">&#39;torch&#39;</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">ds</span></div> + +<div class="viewcode-block" id="RaftAligner._clean_text"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._clean_text">[docs]</a> <span class="k">def</span> <span class="nf">_clean_text</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">text</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">return</span> <span class="n">text</span> + <span class="n">stext</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;###Human&quot;</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span><span class="p">]</span> + <span class="k">return</span> <span class="n">stext</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;#&quot;</span><span class="p">)</span> </div> + +<div class="viewcode-block" id="RaftAligner._discard_sample"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._discard_sample">[docs]</a> <span class="k">def</span> <span class="nf">_discard_sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span> + <span class="k">if</span> <span class="s2">&quot;#&quot;</span> <span class="ow">in</span> <span class="n">text</span><span class="p">:</span> + <span class="k">return</span> <span class="kc">True</span> + <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">text</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span> <span class="c1"># delete empty sample</span> + <span class="k">return</span> <span class="kc">True</span> + <span class="k">return</span> <span class="kc">False</span></div> + +<div class="viewcode-block" id="RaftAligner._get_batch_dataset_top"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_top">[docs]</a> <span class="k">def</span> <span class="nf">_get_batch_dataset_top</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">batch_input</span><span class="p">,</span> + <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> + <span class="n">iter_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> + <span class="n">local_rank</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> + <span class="n">output_min_length</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> + <span class="n">output_max_length</span><span class="o">=</span><span class="mi">48</span><span class="p">,</span> + <span class="n">infer_batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> + <span class="n">generation_kwargs</span><span class="o">=</span><span class="p">{},</span> + <span class="n">tokenizer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">training_args</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">reward_model</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">output_reward_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> :param batch_input: input prompts</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># we will get the batch dataset via Dataset.from_dict</span> + <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + + <span class="n">query_tensors</span> <span class="o">=</span> <span class="n">batch_input</span><span class="p">[</span><span class="s1">&#39;input_ids&#39;</span><span class="p">]</span> + <span class="n">querys</span> <span class="o">=</span> <span class="n">batch_input</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> + <span class="n">data_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">querys</span><span class="p">)</span> + + <span class="n">reward_eva</span> <span class="o">=</span> <span class="p">[]</span> <span class="c1"># record the reward of the samples</span> + <span class="n">input_texts</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">responses</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">query_tensor</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">query_tensors</span><span class="p">):</span> + <span class="n">query</span> <span class="o">=</span> <span class="n">querys</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> + <span class="n">input_texts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">query</span><span class="p">)</span> + <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">infer_batch_size</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span> <span class="o">==</span> <span class="n">data_size</span><span class="p">):</span> + <span class="n">gen_len</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">output_min_length</span><span class="p">,</span> <span class="n">output_max_length</span><span class="p">)</span> + <span class="n">generation_kwargs</span><span class="p">[</span><span class="s2">&quot;max_new_tokens&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gen_len</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">input_texts</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">training_args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">generation_kwargs</span><span class="p">)</span> + <span class="n">generated_texts</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">batch_decode</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">generated_texts</span> <span class="o">=</span> <span class="p">[</span> + <span class="n">generated_text</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">input_texts</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">generated_text</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">generated_texts</span><span class="p">)</span> + <span class="p">]</span> + <span class="n">texts_for_rewards</span> <span class="o">=</span> <span class="p">[</span><span class="n">q</span> <span class="o">+</span> <span class="n">r</span> <span class="k">for</span> <span class="n">q</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">input_texts</span><span class="p">,</span> <span class="n">generated_texts</span><span class="p">)]</span> + + <span class="n">texts_for_reward_dataset</span> <span class="o">=</span> <span class="n">LMFlowDataset</span><span class="o">.</span><span class="n">create_from_dict</span><span class="p">({</span> + <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;text_only&quot;</span><span class="p">,</span> + <span class="s2">&quot;instances&quot;</span><span class="p">:</span> <span class="p">[</span> + <span class="p">{</span> <span class="s2">&quot;text&quot;</span><span class="p">:</span> <span class="n">text</span> <span class="p">}</span> <span class="k">for</span> <span class="n">text</span> <span class="ow">in</span> <span class="n">texts_for_rewards</span> + <span class="p">],</span> + <span class="p">})</span> + + <span class="n">reward_dataset</span> <span class="o">=</span> <span class="n">reward_model</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">texts_for_reward_dataset</span><span class="p">)</span> + <span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span> <span class="n">sample</span><span class="p">[</span><span class="s2">&quot;value&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="n">reward_dataset</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()[</span><span class="s2">&quot;instances&quot;</span><span class="p">]</span> <span class="p">]</span> + + <span class="n">reward_eva</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">rewards</span><span class="p">)</span> + <span class="n">responses</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">generated_texts</span><span class="p">)</span> + <span class="n">input_texts</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="n">data</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">&quot;input&quot;</span><span class="p">:</span> <span class="n">querys</span><span class="p">[</span><span class="n">j</span><span class="p">],</span> <span class="s2">&quot;output&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">responses</span><span class="p">[</span><span class="n">j</span><span class="p">]]}</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">reward_eva</span><span class="p">))]</span> + + <span class="n">world_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;WORLD_SIZE&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">))</span> + <span class="n">all_process_list</span> <span class="o">=</span><span class="p">[{}]</span> <span class="o">*</span> <span class="n">world_size</span> + + + <span class="n">data_to_send</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">reward_eva</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))]</span> + <span class="n">dist</span><span class="o">.</span><span class="n">all_gather_object</span><span class="p">(</span><span class="n">all_process_list</span><span class="p">,</span> <span class="n">data_to_send</span><span class="p">)</span> + <span class="n">gathered_data</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">gathered_reward</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">world_size</span><span class="p">):</span> + <span class="n">tmp_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">tmp</span> <span class="ow">in</span> <span class="n">all_process_list</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> + <span class="n">gathered_data</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp_data</span><span class="p">)</span> + + <span class="n">tmp_reward</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">tmp</span> <span class="ow">in</span> <span class="n">all_process_list</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> + <span class="n">gathered_reward</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp_reward</span><span class="p">)</span> + + <span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">gathered_reward</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">][:</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">gathered_reward</span><span class="p">)</span> <span class="o">*</span> <span class="n">alpha</span><span class="p">)]</span> + <span class="n">gathered_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">gathered_data</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">idx</span><span class="p">]</span> + <span class="n">reward_train</span> <span class="o">=</span> <span class="p">[</span><span class="n">gathered_reward</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">idx</span><span class="p">]</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">reward_seq</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gathered_reward</span><span class="p">))</span> + <span class="bp">self</span><span class="o">.</span><span class="n">train_reawrd</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">reward_train</span><span class="p">))</span> + <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reward_seq</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">)</span> + <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_reawrd</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">)</span> + <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;Model reward&quot;</span><span class="p">,</span> <span class="s2">&quot;Reward of SFT Set&quot;</span><span class="p">])</span> + <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_dir</span> <span class="o">+</span> <span class="s1">&#39;/training_reward.png&#39;</span><span class="p">)</span> + <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> + + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;collected data of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">gathered_data</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gathered_reward</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">reward_train</span><span class="p">)])</span> + + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">output_reward_path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">output_reward_path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span> + <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;mean reward: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gathered_reward</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;mean reward in training set: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">reward_train</span><span class="p">)))</span> + <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> + + + <span class="n">prompt_structure</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{definition}{input}{output}</span><span class="s2">&quot;</span> + <span class="n">tmp_output_dataset</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;text&quot;</span><span class="p">:</span> <span class="p">[</span> <span class="n">prompt_structure</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> + <span class="n">definition</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="n">sample</span><span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">],</span> <span class="n">output</span><span class="o">=</span><span class="n">sample</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> + <span class="p">)</span> <span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="n">gathered_data</span> + <span class="p">]</span> + <span class="p">}</span> + + <span class="c1"># We store the training set for monitoring the RAFT training</span> + <span class="n">all_texts</span> <span class="o">=</span> <span class="n">tmp_output_dataset</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span> + <span class="n">output_eval_dataset</span> <span class="o">=</span> <span class="p">{}</span> + <span class="n">output_eval_dataset</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;text_only&#39;</span> + <span class="n">output_eval_dataset</span><span class="p">[</span><span class="s1">&#39;instances&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;text&#39;</span><span class="p">:</span> <span class="n">i_text</span><span class="p">}</span> <span class="k">for</span> <span class="n">i_text</span> <span class="ow">in</span> <span class="n">all_texts</span><span class="p">]</span> + <span class="kn">import</span> <span class="nn">json</span> + <span class="k">if</span> <span class="n">local_rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_dir</span> <span class="o">+</span> <span class="s2">&quot;/train_set_&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">iter_id</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;.json&quot;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf8&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> + <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">output_eval_dataset</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">ensure_ascii</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + + + <span class="c1"># We need to make sure that the order of the samples are the same for each agent</span> + <span class="n">all_process_list</span> <span class="o">=</span> <span class="p">[{}]</span> <span class="o">*</span> <span class="n">world_size</span> + <span class="n">data_to_send</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_output_dataset</span><span class="p">,</span> <span class="n">local_rank</span><span class="p">]</span> + <span class="n">dist</span><span class="o">.</span><span class="n">all_gather_object</span><span class="p">(</span><span class="n">all_process_list</span><span class="p">,</span> <span class="n">data_to_send</span><span class="p">)</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">world_size</span><span class="p">):</span> + <span class="k">if</span> <span class="n">all_process_list</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">output_dataset</span> <span class="o">=</span> <span class="n">all_process_list</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> + <span class="k">break</span> + + <span class="k">return</span> <span class="n">DatasetDict</span><span class="p">({</span> <span class="s2">&quot;train&quot;</span><span class="p">:</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">output_dataset</span><span class="p">)</span> <span class="p">})</span></div> + +<div class="viewcode-block" id="RaftAligner._get_batch_dataset_local"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_local">[docs]</a> <span class="k">def</span> <span class="nf">_get_batch_dataset_local</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">batch_input</span><span class="p">,</span> + <span class="n">K</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> + <span class="n">iter_id</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> + <span class="n">local_rank</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> + <span class="n">output_min_length</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> + <span class="n">output_max_length</span><span class="o">=</span><span class="mi">48</span><span class="p">,</span> + <span class="n">infer_batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> + <span class="n">generation_kwargs</span><span class="o">=</span><span class="p">{},</span> + <span class="n">tokenizer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">training_args</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">reward_model</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">output_reward_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> :param batch_input: input prompts</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># we will get the batch dataset via Dataset.from_dict</span> + <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + + <span class="n">querys</span> <span class="o">=</span> <span class="n">batch_input</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> + <span class="n">data_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">querys</span><span class="p">)</span> + + <span class="n">reward_eva</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">reward_train</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="n">input_texts</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">responses</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">record_querys</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">all_outputs</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">query</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">querys</span><span class="p">):</span> + <span class="n">input_texts</span> <span class="o">=</span> <span class="p">[</span><span class="n">query</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">K</span><span class="p">)]</span> + + <span class="n">gen_len</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">output_min_length</span><span class="p">,</span> <span class="n">output_max_length</span><span class="p">)</span> + <span class="n">generation_kwargs</span><span class="p">[</span><span class="s2">&quot;max_new_tokens&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">gen_len</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">input_texts</span><span class="p">,</span> <span class="n">return_tensors</span><span class="o">=</span><span class="s2">&quot;pt&quot;</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">training_args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">generation_kwargs</span><span class="p">)</span> + <span class="n">generated_texts</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">batch_decode</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">skip_special_tokens</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">generated_texts</span> <span class="o">=</span> <span class="p">[</span> + <span class="n">generated_text</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">input_texts</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">generated_text</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">generated_texts</span><span class="p">)</span> + <span class="p">]</span> + <span class="n">generated_texts</span> <span class="o">=</span> <span class="p">[</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_clean_text</span><span class="p">(</span><span class="n">generated_text</span><span class="p">)</span> <span class="k">for</span> <span class="n">generated_text</span> <span class="ow">in</span> <span class="n">generated_texts</span> + <span class="p">]</span> + <span class="n">texts_for_rewards</span> <span class="o">=</span> <span class="p">[</span><span class="n">q</span> <span class="o">+</span> <span class="n">r</span> <span class="k">for</span> <span class="n">q</span><span class="p">,</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">input_texts</span><span class="p">,</span> <span class="n">generated_texts</span><span class="p">)]</span> + + <span class="n">texts_for_reward_dataset</span> <span class="o">=</span> <span class="n">LMFlowDataset</span><span class="o">.</span><span class="n">create_from_dict</span><span class="p">({</span> + <span class="s2">&quot;type&quot;</span><span class="p">:</span> <span class="s2">&quot;text_only&quot;</span><span class="p">,</span> + <span class="s2">&quot;instances&quot;</span><span class="p">:</span> <span class="p">[</span> + <span class="p">{</span> <span class="s2">&quot;text&quot;</span><span class="p">:</span> <span class="n">texts_for_rewards</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="p">}</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">texts_for_rewards</span><span class="p">))</span> + <span class="p">],</span> + <span class="p">})</span> + + <span class="n">reward_dataset</span> <span class="o">=</span> <span class="n">reward_model</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">texts_for_reward_dataset</span><span class="p">)</span> + <span class="n">rewards</span> <span class="o">=</span> <span class="p">[</span> <span class="n">sample</span><span class="p">[</span><span class="s2">&quot;value&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="n">reward_dataset</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()[</span><span class="s2">&quot;instances&quot;</span><span class="p">]</span> <span class="p">]</span> + <span class="n">reward_eva</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rewards</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> + + <span class="c1">################################</span> + <span class="c1"># we impose some post-detection and discard the samples with certain criteria.</span> + <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">K</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_discard_sample</span><span class="p">(</span><span class="n">generated_texts</span><span class="p">[</span><span class="n">kk</span><span class="p">]):</span> + <span class="n">rewards</span><span class="p">[</span><span class="n">kk</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">INF</span> + <span class="c1">################################</span> + + <span class="n">idx_to_record</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">rewards</span><span class="p">)</span> + <span class="n">all_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">generated_texts</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> + + <span class="c1"># if we discard all the samples, we do not record the sample </span> + <span class="k">if</span> <span class="n">rewards</span><span class="p">[</span><span class="n">idx_to_record</span><span class="p">]</span> <span class="o">!=</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">INF</span><span class="p">:</span> + <span class="n">responses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">generated_texts</span><span class="p">[</span><span class="n">idx_to_record</span><span class="p">])</span> + <span class="n">reward_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rewards</span><span class="p">[</span><span class="n">idx_to_record</span><span class="p">])</span> + <span class="n">record_querys</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">query</span><span class="p">)</span> + <span class="n">input_texts</span> <span class="o">=</span> <span class="p">[]</span> + + + <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">reward_train</span><span class="p">)):</span> + <span class="n">sample</span> <span class="o">=</span> <span class="p">{}</span> + <span class="n">sample</span><span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">record_querys</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> + <span class="n">sample</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">responses</span><span class="p">[</span><span class="n">j</span><span class="p">]]</span> + <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span> + + + <span class="n">world_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;WORLD_SIZE&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">))</span> + <span class="n">all_process_data</span> <span class="o">=</span><span class="p">[{}]</span> <span class="o">*</span> <span class="n">world_size</span> + <span class="n">dist</span><span class="o">.</span><span class="n">all_gather_object</span><span class="p">(</span><span class="n">all_process_data</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span> + + <span class="n">all_process_eval_reward</span> <span class="o">=</span><span class="p">[{}]</span> <span class="o">*</span> <span class="n">world_size</span> + <span class="n">dist</span><span class="o">.</span><span class="n">all_gather_object</span><span class="p">(</span><span class="n">all_process_eval_reward</span><span class="p">,</span> <span class="n">reward_eva</span><span class="p">)</span> + <span class="n">all_process_train_set_reward</span> <span class="o">=</span><span class="p">[{}]</span> <span class="o">*</span> <span class="n">world_size</span> + <span class="n">dist</span><span class="o">.</span><span class="n">all_gather_object</span><span class="p">(</span><span class="n">all_process_train_set_reward</span><span class="p">,</span> <span class="n">reward_train</span><span class="p">)</span> + + + <span class="n">gathered_data</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">gathered_reward</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">gathered_train_reward</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">world_size</span><span class="p">):</span> + <span class="n">gathered_data</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">all_process_data</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> + <span class="n">gathered_reward</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">all_process_eval_reward</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> + <span class="n">gathered_train_reward</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">all_process_train_set_reward</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> + + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">output_reward_path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">output_reward_path</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;a&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span> + <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;mean reward: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gathered_reward</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;mean reward in training set: &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gathered_train_reward</span><span class="p">)))</span> + <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gathered_reward</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gathered_train_reward</span><span class="p">)])</span> + + + <span class="bp">self</span><span class="o">.</span><span class="n">reward_seq</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">gathered_reward</span><span class="p">))</span> + <span class="bp">self</span><span class="o">.</span><span class="n">train_reawrd</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">reward_train</span><span class="p">))</span> + <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reward_seq</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;o&quot;</span><span class="p">)</span> + <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_reawrd</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s2">&quot;*&quot;</span><span class="p">)</span> + <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;Model reward&quot;</span><span class="p">,</span> <span class="s2">&quot;Reward of SFT Set&quot;</span><span class="p">])</span> + <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_dir</span> <span class="o">+</span> <span class="s1">&#39;/training_reward.png&#39;</span><span class="p">)</span> + <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> + + + <span class="n">prompt_structure</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{definition}{input}{output}</span><span class="s2">&quot;</span> + <span class="n">tmp_output_dataset</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;text&quot;</span><span class="p">:</span> <span class="p">[</span> <span class="n">prompt_structure</span><span class="o">.</span><span class="n">format</span><span class="p">(</span> + <span class="n">definition</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="nb">input</span><span class="o">=</span><span class="n">sample</span><span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">],</span> <span class="n">output</span><span class="o">=</span><span class="n">sample</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> + <span class="p">)</span> <span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="n">gathered_data</span> + <span class="p">]</span> + <span class="p">}</span> + + <span class="c1"># We store the training set for monitoring the RAFT training</span> + <span class="n">all_texts</span> <span class="o">=</span> <span class="n">tmp_output_dataset</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span> + <span class="n">output_eval_dataset</span> <span class="o">=</span> <span class="p">{}</span> + <span class="n">output_eval_dataset</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;text_only&#39;</span> + <span class="n">output_eval_dataset</span><span class="p">[</span><span class="s1">&#39;instances&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[{</span><span class="s1">&#39;text&#39;</span><span class="p">:</span> <span class="n">i_text</span><span class="p">}</span> <span class="k">for</span> <span class="n">i_text</span> <span class="ow">in</span> <span class="n">all_texts</span><span class="p">]</span> + <span class="kn">import</span> <span class="nn">json</span> + <span class="k">if</span> <span class="n">local_rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">store_dir</span> <span class="o">+</span> <span class="s2">&quot;/train_set_&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">iter_id</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;.json&quot;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf8&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> + <span class="n">json</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">output_eval_dataset</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">ensure_ascii</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + + + <span class="c1"># We need to make sure that the order of the samples are the same for each agent</span> + <span class="n">all_process_list</span> <span class="o">=</span> <span class="p">[{}]</span> <span class="o">*</span> <span class="n">world_size</span> + <span class="n">data_to_send</span> <span class="o">=</span> <span class="p">[</span><span class="n">tmp_output_dataset</span><span class="p">,</span> <span class="n">local_rank</span><span class="p">]</span> + <span class="n">dist</span><span class="o">.</span><span class="n">all_gather_object</span><span class="p">(</span><span class="n">all_process_list</span><span class="p">,</span> <span class="n">data_to_send</span><span class="p">)</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">world_size</span><span class="p">):</span> + <span class="k">if</span> <span class="n">all_process_list</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">output_dataset</span> <span class="o">=</span> <span class="n">all_process_list</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> + <span class="k">break</span> + + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;collected data of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">output_dataset</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + + <span class="k">return</span> <span class="n">DatasetDict</span><span class="p">({</span> <span class="s2">&quot;train&quot;</span><span class="p">:</span> <span class="n">Dataset</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">output_dataset</span><span class="p">)</span> <span class="p">})</span></div> + + +<div class="viewcode-block" id="RaftAligner.align"><a class="viewcode-back" href="../../../autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner.align">[docs]</a> <span class="k">def</span> <span class="nf">align</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="n">reward_model</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform alignment for a model</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> model : BaseModel object.</span> +<span class="sd"> dataset: Dataset object.</span> +<span class="sd"> Input dataset for model to generate outputs. The input and output</span> +<span class="sd"> will then be feed into reward model to get the reward for</span> +<span class="sd"> alignment.</span> +<span class="sd"> reward_model: RegressionModel object.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_tokenizer</span><span class="p">()</span> + <span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token</span> + <span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token_id</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span> + <span class="n">tokenizer</span><span class="o">.</span><span class="n">padding_side</span> <span class="o">=</span> <span class="s2">&quot;left&quot;</span> + + <span class="n">dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_load_input_dataset</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">)</span> + <span class="n">set_caching_enabled</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> + + <span class="n">wrapped_model</span> <span class="o">=</span> <span class="n">model</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">get_backend_model</span><span class="p">()</span> + + <span class="n">generation_kwargs</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;min_length&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> + <span class="s2">&quot;top_k&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> + <span class="s2">&quot;top_p&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> + <span class="s2">&quot;do_sample&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span> + <span class="s2">&quot;pad_token_id&quot;</span><span class="p">:</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span><span class="p">,</span> + <span class="s2">&quot;temperature&quot;</span><span class="p">:</span><span class="mf">0.85</span><span class="p">,</span> + <span class="p">}</span> + + <span class="n">aligner_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aligner_args</span> + <span class="n">training_args</span> <span class="o">=</span> <span class="n">aligner_args</span> + <span class="n">model_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_args</span> + <span class="n">data_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_args</span> + <span class="n">world_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;WORLD_SIZE&quot;</span><span class="p">,</span> <span class="s2">&quot;1&quot;</span><span class="p">))</span> + + + <span class="n">set_seed</span><span class="p">(</span><span class="mi">42</span> <span class="o">+</span> <span class="n">training_args</span><span class="o">.</span><span class="n">local_rank</span><span class="p">)</span> + <span class="n">ITERATION</span> <span class="o">=</span> <span class="n">aligner_args</span><span class="o">.</span><span class="n">num_raft_iteration</span> + <span class="n">collection_strategy</span> <span class="o">=</span> <span class="n">aligner_args</span><span class="o">.</span><span class="n">collection_strategy</span> + <span class="n">sft_batch_size</span> <span class="o">=</span> <span class="n">aligner_args</span><span class="o">.</span><span class="n">raft_batch_size</span> + + <span class="k">if</span> <span class="n">collection_strategy</span> <span class="o">==</span> <span class="s2">&quot;top&quot;</span><span class="p">:</span> + <span class="n">alpha</span> <span class="o">=</span> <span class="n">aligner_args</span><span class="o">.</span><span class="n">top_reward_percentage</span> + <span class="n">M</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sft_batch_size</span> <span class="o">/</span> <span class="n">world_size</span> <span class="o">/</span> <span class="n">alpha</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">collection_strategy</span> <span class="o">==</span> <span class="s2">&quot;local&quot;</span><span class="p">:</span> + <span class="n">K</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">aligner_args</span><span class="o">.</span><span class="n">top_reward_percentage</span><span class="p">)</span> + <span class="n">M</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">sft_batch_size</span> <span class="o">/</span> <span class="n">world_size</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;We only support two data collection strategies&quot;</span><span class="p">)</span> + + <span class="nb">print</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">K</span><span class="p">)</span> + <span class="k">if</span> <span class="n">training_args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="n">aligner_args</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">store_dir</span> <span class="o">=</span> <span class="n">aligner_args</span><span class="o">.</span><span class="n">output_dir</span> + <span class="bp">self</span><span class="o">.</span><span class="n">reward_seq</span> <span class="o">=</span> <span class="p">[]</span> + <span class="bp">self</span><span class="o">.</span><span class="n">train_reawrd</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="n">data_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">])</span> + <span class="n">lr</span> <span class="o">=</span> <span class="n">training_args</span><span class="o">.</span><span class="n">learning_rate</span> + <span class="n">random_idxs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">data_size</span><span class="p">)</span> + <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">random_idxs</span><span class="p">)</span> + + <span class="n">raft_trainer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_initialize_trainer</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">training_args</span><span class="p">)</span> + <span class="n">raft_trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">is_first_time</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + + <span class="k">for</span> <span class="n">iteration</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ITERATION</span><span class="p">):</span> + <span class="n">set_seed</span><span class="p">(</span><span class="mi">666</span> <span class="o">+</span> <span class="n">training_args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">+</span> <span class="n">world_size</span> <span class="o">*</span> <span class="p">(</span><span class="n">iteration</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span> + + <span class="n">end_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">([</span><span class="n">data_size</span><span class="p">,</span> <span class="p">(</span><span class="n">iteration</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">M</span><span class="p">])</span> + <span class="n">batch_input</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="n">random_idxs</span><span class="p">[</span><span class="n">iteration</span> <span class="o">*</span> <span class="n">M</span> <span class="p">:</span> <span class="n">end_idx</span><span class="p">])</span> + <span class="n">model</span><span class="o">.</span><span class="n">gradient_checkpointing_disable</span><span class="p">()</span> + <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">use_cache</span> <span class="o">=</span> <span class="kc">True</span> + + <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + <span class="k">if</span> <span class="n">collection_strategy</span> <span class="o">==</span> <span class="s2">&quot;top&quot;</span><span class="p">:</span> + <span class="n">selected_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_batch_dataset_top</span><span class="p">(</span> + <span class="n">raft_trainer</span><span class="o">.</span><span class="n">tmp_model</span><span class="p">,</span> + <span class="n">batch_input</span><span class="p">,</span> + <span class="n">alpha</span><span class="p">,</span> + <span class="n">iteration</span><span class="p">,</span> + <span class="n">training_args</span><span class="o">.</span><span class="n">local_rank</span><span class="p">,</span> + <span class="n">output_min_length</span><span class="o">=</span><span class="n">aligner_args</span><span class="o">.</span><span class="n">output_min_length</span><span class="p">,</span> + <span class="n">output_max_length</span><span class="o">=</span><span class="n">aligner_args</span><span class="o">.</span><span class="n">output_max_length</span><span class="p">,</span> + <span class="n">infer_batch_size</span><span class="o">=</span><span class="n">aligner_args</span><span class="o">.</span><span class="n">inference_batch_size_per_device</span><span class="p">,</span> + <span class="n">generation_kwargs</span><span class="o">=</span><span class="n">generation_kwargs</span><span class="p">,</span> + <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span> + <span class="n">training_args</span><span class="o">=</span><span class="n">training_args</span><span class="p">,</span> + <span class="n">reward_model</span><span class="o">=</span><span class="n">reward_model</span><span class="p">,</span> + <span class="n">output_reward_path</span><span class="o">=</span><span class="n">aligner_args</span><span class="o">.</span><span class="n">output_reward_path</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="n">collection_strategy</span> <span class="o">==</span> <span class="s2">&quot;local&quot;</span><span class="p">:</span> + <span class="n">selected_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_batch_dataset_local</span><span class="p">(</span> + <span class="n">raft_trainer</span><span class="o">.</span><span class="n">tmp_model</span><span class="p">,</span> + <span class="n">batch_input</span><span class="p">,</span> + <span class="n">K</span><span class="p">,</span> + <span class="n">iteration</span><span class="p">,</span> + <span class="n">training_args</span><span class="o">.</span><span class="n">local_rank</span><span class="p">,</span> + <span class="n">output_min_length</span><span class="o">=</span><span class="n">aligner_args</span><span class="o">.</span><span class="n">output_min_length</span><span class="p">,</span> + <span class="n">output_max_length</span><span class="o">=</span><span class="n">aligner_args</span><span class="o">.</span><span class="n">output_max_length</span><span class="p">,</span> + <span class="n">infer_batch_size</span><span class="o">=</span><span class="n">K</span><span class="p">,</span> + <span class="n">generation_kwargs</span><span class="o">=</span><span class="n">generation_kwargs</span><span class="p">,</span> + <span class="n">tokenizer</span><span class="o">=</span><span class="n">tokenizer</span><span class="p">,</span> + <span class="n">training_args</span><span class="o">=</span><span class="n">training_args</span><span class="p">,</span> + <span class="n">reward_model</span><span class="o">=</span><span class="n">reward_model</span><span class="p">,</span> + <span class="n">output_reward_path</span><span class="o">=</span><span class="n">aligner_args</span><span class="o">.</span><span class="n">output_reward_path</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;It takes </span><span class="si">%.2f</span><span class="s2"> s to inference one stage&quot;</span><span class="p">,</span> <span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span> + + <span class="n">raft_trainer</span><span class="o">.</span><span class="n">train_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_load_dataset</span><span class="p">(</span> + <span class="n">selected_dataset</span><span class="p">,</span> + <span class="n">raft_trainer</span><span class="o">.</span><span class="n">tmp_model</span><span class="p">,</span> + <span class="n">tokenizer</span><span class="p">,</span> + <span class="n">model_args</span><span class="p">,</span> + <span class="n">data_args</span><span class="p">,</span> + <span class="n">training_args</span><span class="p">,</span> + <span class="p">)</span> + + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;iter </span><span class="si">{</span><span class="n">iteration</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + <span class="n">model</span><span class="o">.</span><span class="n">gradient_checkpointing_enable</span><span class="p">()</span> + <span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">use_cache</span> <span class="o">=</span> <span class="kc">False</span> + + + <span class="n">train_result</span> <span class="o">=</span> <span class="n">raft_trainer</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;It takes </span><span class="si">%.2f</span><span class="s2"> s to train one stage&quot;</span><span class="p">,</span> <span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span> + <span class="k">if</span> <span class="p">(</span><span class="n">iteration</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">M</span> <span class="o">&gt;</span> <span class="n">data_size</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;One epoch is completed.&quot;</span><span class="p">)</span> + <span class="k">break</span> + +<span class="w"> </span><span class="sd">&#39;&#39;&#39;</span> +<span class="sd"> if training_args.local_rank == 0 and iteration % 2 == 0:</span> +<span class="sd"> wrapped_model.save(aligner_args.output_dir + &quot;/&quot; + &quot;model&quot; + str(iteration))</span> +<span class="sd"> print(iteration, &quot;I save a model with&quot;, self.reward_seq[-1])</span> +<span class="sd"> &#39;&#39;&#39;</span> + + <span class="k">if</span> <span class="n">aligner_args</span><span class="o">.</span><span class="n">output_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">wrapped_model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">aligner_args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">wrapped_model</span> </div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + 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class="header-article-items__start"> + + <div class="header-article-item"> + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.pipeline.utils.peft_trainer</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.pipeline.utils.peft_trainer</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;Trainer for Peft models</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span> +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">Trainer</span> +<span class="kn">from</span> <span class="nn">transformers.trainer_utils</span> <span class="kn">import</span> <span class="n">PREFIX_CHECKPOINT_DIR</span> +<span class="kn">from</span> <span class="nn">transformers.trainer_callback</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">TrainerCallback</span><span class="p">,</span> + <span class="n">TrainerControl</span><span class="p">,</span> + <span class="n">TrainerState</span><span class="p">,</span> +<span class="p">)</span> +<span class="kn">from</span> <span class="nn">transformers.training_args</span> <span class="kn">import</span> <span class="n">TrainingArguments</span> +<span class="kn">import</span> <span class="nn">os</span> +<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> + +<div class="viewcode-block" id="PeftTrainer"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftTrainer">[docs]</a><span class="k">class</span> <span class="nc">PeftTrainer</span><span class="p">(</span><span class="n">Trainer</span><span class="p">):</span> +<div class="viewcode-block" id="PeftTrainer._save_checkpoint"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftTrainer._save_checkpoint">[docs]</a> <span class="k">def</span> <span class="nf">_save_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">trial</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot; Don&#39;t save base model, optimizer etc.</span> +<span class="sd"> but create checkpoint folder (needed for saving adapter) &quot;&quot;&quot;</span> + <span class="n">checkpoint_folder</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">PREFIX_CHECKPOINT_DIR</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="si">}</span><span class="s2">&quot;</span> + + <span class="n">run_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_output_dir</span><span class="p">(</span><span class="n">trial</span><span class="o">=</span><span class="n">trial</span><span class="p">)</span> + <span class="n">output_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">run_dir</span><span class="p">,</span> <span class="n">checkpoint_folder</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">metrics</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">metric_for_best_model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">metric_to_check</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">metric_for_best_model</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">metric_to_check</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;eval_&quot;</span><span class="p">):</span> + <span class="n">metric_to_check</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;eval_</span><span class="si">{</span><span class="n">metric_to_check</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="n">metric_value</span> <span class="o">=</span> <span class="n">metrics</span><span class="p">[</span><span class="n">metric_to_check</span><span class="p">]</span> + + <span class="n">operator</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">greater</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">greater_is_better</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">less</span> + <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_metric</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span> <span class="ow">is</span> <span class="kc">None</span> + <span class="ow">or</span> <span class="n">operator</span><span class="p">(</span><span class="n">metric_value</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_metric</span><span class="p">)):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_metric</span> <span class="o">=</span> <span class="n">metric_value</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span> <span class="o">=</span> <span class="n">output_dir</span> + + <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_rotate_checkpoints</span><span class="p">(</span><span class="n">use_mtime</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">output_dir</span><span class="o">=</span><span class="n">run_dir</span><span class="p">)</span></div></div> + +<div class="viewcode-block" id="PeftSavingCallback"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback">[docs]</a><span class="k">class</span> <span class="nc">PeftSavingCallback</span><span class="p">(</span><span class="n">TrainerCallback</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot; Correctly save PEFT model and not full model &quot;&quot;&quot;</span> +<div class="viewcode-block" id="PeftSavingCallback._save"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback._save">[docs]</a> <span class="k">def</span> <span class="nf">_save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">folder</span><span class="p">):</span> + <span class="k">if</span> <span class="n">folder</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">folder</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> + <span class="n">peft_model_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="s2">&quot;adapter_model&quot;</span><span class="p">)</span> + <span class="n">model</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">peft_model_path</span><span class="p">)</span></div> + +<div class="viewcode-block" id="PeftSavingCallback.on_train_end"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_train_end">[docs]</a> <span class="k">def</span> <span class="nf">on_train_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">:</span> <span class="n">TrainingArguments</span><span class="p">,</span> <span class="n">state</span><span class="p">:</span> <span class="n">TrainerState</span><span class="p">,</span> + <span class="n">control</span><span class="p">:</span> <span class="n">TrainerControl</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot; Save final best model adapter &quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_save</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;model&#39;</span><span class="p">],</span> <span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="p">)</span></div> + +<div class="viewcode-block" id="PeftSavingCallback.on_epoch_end"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_epoch_end">[docs]</a> <span class="k">def</span> <span class="nf">on_epoch_end</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">:</span> <span class="n">TrainingArguments</span><span class="p">,</span> <span class="n">state</span><span class="p">:</span> <span class="n">TrainerState</span><span class="p">,</span> + <span class="n">control</span><span class="p">:</span> <span class="n">TrainerControl</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot; Save intermediate model adapters in case of interrupted training &quot;&quot;&quot;</span> + <span class="n">folder</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">PREFIX_CHECKPOINT_DIR</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_save</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;model&#39;</span><span class="p">],</span> <span class="n">folder</span><span class="p">)</span></div> + +<div class="viewcode-block" id="PeftSavingCallback.on_save"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_save">[docs]</a> <span class="k">def</span> <span class="nf">on_save</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">args</span><span class="p">:</span> <span class="n">TrainingArguments</span><span class="p">,</span> + <span class="n">state</span><span class="p">:</span> <span class="n">TrainerState</span><span class="p">,</span> + <span class="n">control</span><span class="p">:</span> <span class="n">TrainerControl</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> + <span class="p">):</span> + <span class="n">checkpoint_folder</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span> + <span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">PREFIX_CHECKPOINT_DIR</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_save</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;model&#39;</span><span class="p">],</span> <span class="n">checkpoint_folder</span><span class="p">)</span> + + <span class="n">peft_model_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint_folder</span><span class="p">,</span> <span class="s2">&quot;adapter_model&quot;</span><span class="p">)</span> + <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;model&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">peft_model_path</span><span class="p">)</span> + <span class="k">return</span> <span class="n">control</span></div></div> +</pre></div> + + </article> + + + + <footer 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<h1>Source code for lmflow.pipeline.utils.raft_trainer</h1><div class="highlight"><pre> +<span></span><span class="kn">import</span> <span class="nn">contextlib</span> +<span class="kn">import</span> <span class="nn">functools</span> +<span class="kn">import</span> <span class="nn">glob</span> +<span class="kn">import</span> <span class="nn">inspect</span> +<span class="kn">import</span> <span class="nn">math</span> +<span class="kn">import</span> <span class="nn">os</span> +<span class="kn">import</span> <span class="nn">random</span> +<span class="kn">import</span> <span class="nn">re</span> +<span class="kn">import</span> <span class="nn">shutil</span> +<span class="kn">import</span> <span class="nn">sys</span> +<span class="kn">import</span> <span class="nn">time</span> +<span class="kn">import</span> <span class="nn">warnings</span> +<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Mapping</span> +<span class="kn">from</span> <span class="nn">distutils.util</span> <span class="kn">import</span> <span class="n">strtobool</span> +<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span> +<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">TYPE_CHECKING</span><span class="p">,</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span> + +<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span> + + +<span class="c1"># Integrations must be imported before ML frameworks:</span> +<span class="c1"># isort: off</span> + +<span class="c1">### Fix the import bug in the latest version</span> +<span class="k">try</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">transformers.integrations</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">default_hp_search_backend</span><span class="p">,</span> + <span class="n">get_reporting_integration_callbacks</span><span class="p">,</span> + <span class="n">hp_params</span><span class="p">,</span> + <span class="n">is_fairscale_available</span><span class="p">,</span> + <span class="n">is_optuna_available</span><span class="p">,</span> + <span class="n">is_ray_tune_available</span><span class="p">,</span> + <span class="n">is_sigopt_available</span><span class="p">,</span> + <span class="n">is_wandb_available</span><span class="p">,</span> + <span class="n">run_hp_search_optuna</span><span class="p">,</span> + <span class="n">run_hp_search_ray</span><span class="p">,</span> + <span class="n">run_hp_search_sigopt</span><span class="p">,</span> + <span class="n">run_hp_search_wandb</span><span class="p">,</span> + <span class="p">)</span> +<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">transformers.integrations</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">get_reporting_integration_callbacks</span><span class="p">,</span> + <span class="n">hp_params</span><span class="p">,</span> + <span class="n">is_fairscale_available</span><span class="p">,</span> + <span class="p">)</span> + <span class="kn">from</span> <span class="nn">transformers.hyperparameter_search</span> <span class="kn">import</span> <span class="n">default_hp_search_backend</span><span class="p">,</span><span class="n">ALL_HYPERPARAMETER_SEARCH_BACKENDS</span> + + + +<span class="c1"># isort: on</span> + +<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span> +<span class="kn">from</span> <span class="nn">huggingface_hub</span> <span class="kn">import</span> <span class="n">Repository</span><span class="p">,</span> <span class="n">create_repo</span> +<span class="kn">from</span> <span class="nn">packaging</span> <span class="kn">import</span> <span class="n">version</span> +<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span> +<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">RandomSampler</span><span class="p">,</span> <span class="n">SequentialSampler</span> +<span class="kn">from</span> <span class="nn">torch.utils.data.distributed</span> <span class="kn">import</span> <span class="n">DistributedSampler</span> + + +<span class="kn">from</span> <span class="nn">transformers.configuration_utils</span> <span class="kn">import</span> <span class="n">PretrainedConfig</span> +<span class="kn">from</span> <span class="nn">transformers.data.data_collator</span> <span class="kn">import</span> <span class="n">DataCollator</span><span class="p">,</span> <span class="n">DataCollatorWithPadding</span><span class="p">,</span> <span class="n">default_data_collator</span> +<span class="kn">from</span> <span class="nn">transformers.debug_utils</span> <span class="kn">import</span> <span class="n">DebugOption</span><span class="p">,</span> <span class="n">DebugUnderflowOverflow</span> +<span class="kn">from</span> <span class="nn">transformers.deepspeed</span> <span class="kn">import</span> <span class="n">deepspeed_init</span><span class="p">,</span> <span class="n">is_deepspeed_zero3_enabled</span> +<span class="kn">from</span> <span class="nn">transformers.dependency_versions_check</span> <span class="kn">import</span> <span class="n">dep_version_check</span> +<span class="kn">from</span> <span class="nn">transformers.modelcard</span> <span class="kn">import</span> <span class="n">TrainingSummary</span> +<span class="kn">from</span> <span class="nn">transformers.modeling_utils</span> <span class="kn">import</span> <span class="n">PreTrainedModel</span><span class="p">,</span> <span class="n">load_sharded_checkpoint</span><span class="p">,</span> <span class="n">unwrap_model</span> +<span class="kn">from</span> <span class="nn">transformers.models.auto.modeling_auto</span> <span class="kn">import</span> <span class="n">MODEL_FOR_CAUSAL_LM_MAPPING_NAMES</span><span class="p">,</span> <span class="n">MODEL_MAPPING_NAMES</span> +<span class="kn">from</span> <span class="nn">transformers.optimization</span> <span class="kn">import</span> <span class="n">Adafactor</span><span class="p">,</span> <span class="n">get_scheduler</span> +<span class="kn">from</span> <span class="nn">transformers.pytorch_utils</span> <span class="kn">import</span> <span class="n">ALL_LAYERNORM_LAYERS</span> + +<div class="viewcode-block" id="is_torch_greater_or_equal_than_1_10"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.is_torch_greater_or_equal_than_1_10">[docs]</a><span class="n">is_torch_greater_or_equal_than_1_10</span> <span class="o">=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="s2">&quot;1.10&quot;</span><span class="p">)</span></div> +<div class="viewcode-block" id="is_torch_less_than_1_11"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.is_torch_less_than_1_11">[docs]</a><span class="n">is_torch_less_than_1_11</span> <span class="o">=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="s2">&quot;1.11&quot;</span><span class="p">)</span></div> +<span class="kn">from</span> <span class="nn">transformers.tokenization_utils_base</span> <span class="kn">import</span> <span class="n">PreTrainedTokenizerBase</span> +<span class="kn">from</span> <span class="nn">transformers.trainer_callback</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">CallbackHandler</span><span class="p">,</span> + <span class="n">DefaultFlowCallback</span><span class="p">,</span> + <span class="n">PrinterCallback</span><span class="p">,</span> + <span class="n">ProgressCallback</span><span class="p">,</span> + <span class="n">TrainerCallback</span><span class="p">,</span> + <span class="n">TrainerControl</span><span class="p">,</span> + <span class="n">TrainerState</span><span class="p">,</span> +<span class="p">)</span> +<span class="kn">from</span> <span class="nn">transformers.trainer_pt_utils</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">DistributedLengthGroupedSampler</span><span class="p">,</span> + <span class="n">DistributedSamplerWithLoop</span><span class="p">,</span> + <span class="n">DistributedTensorGatherer</span><span class="p">,</span> + <span class="n">IterableDatasetShard</span><span class="p">,</span> + <span class="n">LabelSmoother</span><span class="p">,</span> + <span class="n">LengthGroupedSampler</span><span class="p">,</span> + <span class="n">SequentialDistributedSampler</span><span class="p">,</span> + <span class="n">ShardSampler</span><span class="p">,</span> + <span class="n">distributed_broadcast_scalars</span><span class="p">,</span> + <span class="n">distributed_concat</span><span class="p">,</span> + <span class="n">find_batch_size</span><span class="p">,</span> + <span class="n">get_module_class_from_name</span><span class="p">,</span> + <span class="n">get_parameter_names</span><span class="p">,</span> + <span class="n">nested_concat</span><span class="p">,</span> + <span class="n">nested_detach</span><span class="p">,</span> + <span class="n">nested_numpify</span><span class="p">,</span> + <span class="n">nested_truncate</span><span class="p">,</span> + <span class="n">nested_xla_mesh_reduce</span><span class="p">,</span> + <span class="n">reissue_pt_warnings</span><span class="p">,</span> +<span class="p">)</span> +<span class="kn">from</span> <span class="nn">transformers.trainer_utils</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">PREFIX_CHECKPOINT_DIR</span><span class="p">,</span> + <span class="n">BestRun</span><span class="p">,</span> + <span class="n">EvalLoopOutput</span><span class="p">,</span> + <span class="n">EvalPrediction</span><span class="p">,</span> + <span class="n">FSDPOption</span><span class="p">,</span> + <span class="n">HPSearchBackend</span><span class="p">,</span> + <span class="n">HubStrategy</span><span class="p">,</span> + <span class="n">IntervalStrategy</span><span class="p">,</span> + <span class="n">PredictionOutput</span><span class="p">,</span> + <span class="n">RemoveColumnsCollator</span><span class="p">,</span> + <span class="n">ShardedDDPOption</span><span class="p">,</span> + <span class="n">TrainerMemoryTracker</span><span class="p">,</span> + <span class="n">TrainOutput</span><span class="p">,</span> + <span class="n">default_compute_objective</span><span class="p">,</span> + <span class="n">denumpify_detensorize</span><span class="p">,</span> + <span class="n">enable_full_determinism</span><span class="p">,</span> + <span class="n">find_executable_batch_size</span><span class="p">,</span> + <span class="n">get_last_checkpoint</span><span class="p">,</span> + <span class="n">has_length</span><span class="p">,</span> + <span class="n">number_of_arguments</span><span class="p">,</span> + <span class="n">seed_worker</span><span class="p">,</span> + <span class="n">set_seed</span><span class="p">,</span> + <span class="n">speed_metrics</span><span class="p">,</span> +<span class="p">)</span> +<span class="kn">from</span> <span class="nn">transformers.training_args</span> <span class="kn">import</span> <span class="n">OptimizerNames</span><span class="p">,</span> <span class="n">ParallelMode</span><span class="p">,</span> <span class="n">TrainingArguments</span> +<span class="kn">from</span> <span class="nn">transformers.utils</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">CONFIG_NAME</span><span class="p">,</span> + <span class="n">WEIGHTS_INDEX_NAME</span><span class="p">,</span> + <span class="n">WEIGHTS_NAME</span><span class="p">,</span> + <span class="n">can_return_loss</span><span class="p">,</span> + <span class="n">find_labels</span><span class="p">,</span> + <span class="n">get_full_repo_name</span><span class="p">,</span> + <span class="n">is_accelerate_available</span><span class="p">,</span> + <span class="n">is_apex_available</span><span class="p">,</span> + <span class="n">is_datasets_available</span><span class="p">,</span> + <span class="n">is_in_notebook</span><span class="p">,</span> + <span class="n">is_ipex_available</span><span class="p">,</span> + <span class="n">is_sagemaker_dp_enabled</span><span class="p">,</span> + <span class="n">is_sagemaker_mp_enabled</span><span class="p">,</span> + <span class="n">is_torch_compile_available</span><span class="p">,</span> + <span class="n">is_torch_neuroncore_available</span><span class="p">,</span> + <span class="n">is_torch_tpu_available</span><span class="p">,</span> + <span class="n">logging</span><span class="p">,</span> +<span class="p">)</span> +<span class="kn">from</span> <span class="nn">transformers.utils.generic</span> <span class="kn">import</span> <span class="n">ContextManagers</span> + + +<div class="viewcode-block" id="_is_native_cpu_amp_available"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer._is_native_cpu_amp_available">[docs]</a><span class="n">_is_native_cpu_amp_available</span> <span class="o">=</span> <span class="n">is_torch_greater_or_equal_than_1_10</span></div> + +<div class="viewcode-block" id="DEFAULT_CALLBACKS"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.DEFAULT_CALLBACKS">[docs]</a><span class="n">DEFAULT_CALLBACKS</span> <span class="o">=</span> <span class="p">[</span><span class="n">DefaultFlowCallback</span><span class="p">]</span></div> +<span class="n">DEFAULT_PROGRESS_CALLBACK</span> <span class="o">=</span> <span class="n">ProgressCallback</span> + +<span class="k">if</span> <span class="n">is_in_notebook</span><span class="p">():</span> + <span class="kn">from</span> <span class="nn">transformers.utils.notebook</span> <span class="kn">import</span> <span class="n">NotebookProgressCallback</span> + +<div class="viewcode-block" id="DEFAULT_PROGRESS_CALLBACK"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK">[docs]</a> <span class="n">DEFAULT_PROGRESS_CALLBACK</span> <span class="o">=</span> <span class="n">NotebookProgressCallback</span></div> + +<span class="k">if</span> <span class="n">is_apex_available</span><span class="p">():</span> + <span class="kn">from</span> <span class="nn">apex</span> <span class="kn">import</span> <span class="n">amp</span> + +<span class="k">if</span> <span class="n">is_datasets_available</span><span class="p">():</span> + <span class="kn">import</span> <span class="nn">datasets</span> + +<span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">(</span><span class="n">check_device</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> + <span class="kn">import</span> <span class="nn">torch_xla.core.xla_model</span> <span class="k">as</span> <span class="nn">xm</span> + <span class="kn">import</span> <span class="nn">torch_xla.debug.metrics</span> <span class="k">as</span> <span class="nn">met</span> + <span class="kn">import</span> <span class="nn">torch_xla.distributed.parallel_loader</span> <span class="k">as</span> <span class="nn">pl</span> + +<span class="k">if</span> <span class="n">is_fairscale_available</span><span class="p">():</span> + <span class="n">dep_version_check</span><span class="p">(</span><span class="s2">&quot;fairscale&quot;</span><span class="p">)</span> + <span class="kn">import</span> <span class="nn">fairscale</span> + <span class="kn">from</span> <span class="nn">fairscale.nn.data_parallel</span> <span class="kn">import</span> <span class="n">FullyShardedDataParallel</span> <span class="k">as</span> <span class="n">FullyShardedDDP</span> + <span class="kn">from</span> <span class="nn">fairscale.nn.data_parallel</span> <span class="kn">import</span> <span class="n">ShardedDataParallel</span> <span class="k">as</span> <span class="n">ShardedDDP</span> + <span class="kn">from</span> <span class="nn">fairscale.nn.wrap</span> <span class="kn">import</span> <span class="n">auto_wrap</span> + <span class="kn">from</span> <span class="nn">fairscale.optim</span> <span class="kn">import</span> <span class="n">OSS</span> + <span class="kn">from</span> <span class="nn">fairscale.optim.grad_scaler</span> <span class="kn">import</span> <span class="n">ShardedGradScaler</span> + + +<span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="kn">import</span> <span class="nn">smdistributed.modelparallel.torch</span> <span class="k">as</span> <span class="nn">smp</span> + <span class="kn">from</span> <span class="nn">smdistributed.modelparallel</span> <span class="kn">import</span> <span class="n">__version__</span> <span class="k">as</span> <span class="n">SMP_VERSION</span> + +<div class="viewcode-block" id="IS_SAGEMAKER_MP_POST_1_10"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.IS_SAGEMAKER_MP_POST_1_10">[docs]</a> <span class="n">IS_SAGEMAKER_MP_POST_1_10</span> <span class="o">=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">SMP_VERSION</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="s2">&quot;1.10&quot;</span><span class="p">)</span></div> + + <span class="kn">from</span> <span class="nn">transformers.trainer_pt_utils</span> <span class="kn">import</span> <span class="n">smp_forward_backward</span><span class="p">,</span> <span class="n">smp_forward_only</span><span class="p">,</span> <span class="n">smp_gather</span><span class="p">,</span> <span class="n">smp_nested_concat</span> +<span class="k">else</span><span class="p">:</span> + <span class="n">IS_SAGEMAKER_MP_POST_1_10</span> <span class="o">=</span> <span class="kc">False</span> + + +<div class="viewcode-block" id="skip_first_batches"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.skip_first_batches">[docs]</a><span class="n">skip_first_batches</span> <span class="o">=</span> <span class="kc">None</span></div> + + + +<div class="viewcode-block" id="logger"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.logger">[docs]</a><span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">get_logger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span></div> + + +<span class="c1"># Name of the files used for checkpointing</span> +<div class="viewcode-block" id="TRAINING_ARGS_NAME"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.TRAINING_ARGS_NAME">[docs]</a><span class="n">TRAINING_ARGS_NAME</span> <span class="o">=</span> <span class="s2">&quot;training_args.bin&quot;</span></div> +<div class="viewcode-block" id="TRAINER_STATE_NAME"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.TRAINER_STATE_NAME">[docs]</a><span class="n">TRAINER_STATE_NAME</span> <span class="o">=</span> <span class="s2">&quot;trainer_state.json&quot;</span></div> +<div class="viewcode-block" id="OPTIMIZER_NAME"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.OPTIMIZER_NAME">[docs]</a><span class="n">OPTIMIZER_NAME</span> <span class="o">=</span> <span class="s2">&quot;optimizer.pt&quot;</span></div> +<div class="viewcode-block" id="SCHEDULER_NAME"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.SCHEDULER_NAME">[docs]</a><span class="n">SCHEDULER_NAME</span> <span class="o">=</span> <span class="s2">&quot;scheduler.pt&quot;</span></div> +<div class="viewcode-block" id="SCALER_NAME"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.SCALER_NAME">[docs]</a><span class="n">SCALER_NAME</span> <span class="o">=</span> <span class="s2">&quot;scaler.pt&quot;</span></div> + + +<div class="viewcode-block" id="RaftTrainer"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer">[docs]</a><span class="k">class</span> <span class="nc">RaftTrainer</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.</span> +<span class="sd"> Args:</span> +<span class="sd"> model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*):</span> +<span class="sd"> The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed.</span> +<span class="sd"> &lt;Tip&gt;</span> +<span class="sd"> [`Trainer`] is optimized to work with the [`PreTrainedModel`] provided by the library. You can still use</span> +<span class="sd"> your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers</span> +<span class="sd"> models.</span> +<span class="sd"> &lt;/Tip&gt;</span> +<span class="sd"> args ([`TrainingArguments`], *optional*):</span> +<span class="sd"> The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the</span> +<span class="sd"> `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided.</span> +<span class="sd"> data_collator (`DataCollator`, *optional*):</span> +<span class="sd"> The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will</span> +<span class="sd"> default to [`default_data_collator`] if no `tokenizer` is provided, an instance of</span> +<span class="sd"> [`DataCollatorWithPadding`] otherwise.</span> +<span class="sd"> train_dataset (`torch.utils.data.Dataset` or `torch.utils.data.IterableDataset`, *optional*):</span> +<span class="sd"> The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the</span> +<span class="sd"> `model.forward()` method are automatically removed.</span> +<span class="sd"> Note that if it&#39;s a `torch.utils.data.IterableDataset` with some randomization and you are training in a</span> +<span class="sd"> distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a</span> +<span class="sd"> `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will</span> +<span class="sd"> manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally</span> +<span class="sd"> sets the seed of the RNGs used.</span> +<span class="sd"> eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`]), *optional*):</span> +<span class="sd"> The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the</span> +<span class="sd"> `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each</span> +<span class="sd"> dataset prepending the dictionary key to the metric name.</span> +<span class="sd"> tokenizer ([`PreTrainedTokenizerBase`], *optional*):</span> +<span class="sd"> The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the</span> +<span class="sd"> maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an</span> +<span class="sd"> interrupted training or reuse the fine-tuned model.</span> +<span class="sd"> model_init (`Callable[[], PreTrainedModel]`, *optional*):</span> +<span class="sd"> A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start</span> +<span class="sd"> from a new instance of the model as given by this function.</span> +<span class="sd"> The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to</span> +<span class="sd"> be able to choose different architectures according to hyper parameters (such as layer count, sizes of</span> +<span class="sd"> inner layers, dropout probabilities etc).</span> +<span class="sd"> compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*):</span> +<span class="sd"> The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return</span> +<span class="sd"> a dictionary string to metric values.</span> +<span class="sd"> callbacks (List of [`TrainerCallback`], *optional*):</span> +<span class="sd"> A list of callbacks to customize the training loop. Will add those to the list of default callbacks</span> +<span class="sd"> detailed in [here](callback).</span> +<span class="sd"> If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method.</span> +<span class="sd"> optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*): A tuple</span> +<span class="sd"> containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your model</span> +<span class="sd"> and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`.</span> +<span class="sd"> preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*):</span> +<span class="sd"> A function that preprocess the logits right before caching them at each evaluation step. Must take two</span> +<span class="sd"> tensors, the logits and the labels, and return the logits once processed as desired. The modifications made</span> +<span class="sd"> by this function will be reflected in the predictions received by `compute_metrics`.</span> +<span class="sd"> Note that the labels (second parameter) will be `None` if the dataset does not have them.</span> +<span class="sd"> Important attributes:</span> +<span class="sd"> - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`]</span> +<span class="sd"> subclass.</span> +<span class="sd"> - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the</span> +<span class="sd"> original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`,</span> +<span class="sd"> the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner</span> +<span class="sd"> model hasn&#39;t been wrapped, then `self.model_wrapped` is the same as `self.model`.</span> +<span class="sd"> - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from</span> +<span class="sd"> data parallelism, this means some of the model layers are split on different GPUs).</span> +<span class="sd"> - **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set</span> +<span class="sd"> to `False` if model parallel or deepspeed is used, or if the default</span> +<span class="sd"> `TrainingArguments.place_model_on_device` is overridden to return `False` .</span> +<span class="sd"> - **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while</span> +<span class="sd"> in `train`)</span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="kn">from</span> <span class="nn">transformers.trainer_pt_utils</span> <span class="kn">import</span> <span class="n">_get_learning_rate</span><span class="p">,</span> <span class="n">log_metrics</span><span class="p">,</span> <span class="n">metrics_format</span><span class="p">,</span> <span class="n">save_metrics</span><span class="p">,</span> <span class="n">save_state</span> + + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">PreTrainedModel</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">args</span><span class="p">:</span> <span class="n">TrainingArguments</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">data_collator</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">DataCollator</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">train_dataset</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dataset</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">eval_dataset</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dataset</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">tokenizer</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">PreTrainedTokenizerBase</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">model_init</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[],</span> <span class="n">PreTrainedModel</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">compute_metrics</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">EvalPrediction</span><span class="p">],</span> <span class="n">Dict</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">callbacks</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">TrainerCallback</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">optimizers</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Optimizer</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">LambdaLR</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> + <span class="n">preprocess_logits_for_metrics</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="p">):</span> + <span class="c1">############</span> + <span class="bp">self</span><span class="o">.</span><span class="n">save_counter</span> <span class="o">=</span> <span class="mi">0</span> + <span class="c1">##############</span> + <span class="k">if</span> <span class="n">args</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">output_dir</span> <span class="o">=</span> <span class="s2">&quot;tmp_trainer&quot;</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No `TrainingArguments` passed, using `output_dir=</span><span class="si">{</span><span class="n">output_dir</span><span class="si">}</span><span class="s2">`.&quot;</span><span class="p">)</span> + <span class="n">args</span> <span class="o">=</span> <span class="n">TrainingArguments</span><span class="p">(</span><span class="n">output_dir</span><span class="o">=</span><span class="n">output_dir</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">args</span> <span class="o">=</span> <span class="n">args</span> + <span class="c1"># Seed must be set before instantiating the model when using model</span> + <span class="n">enable_full_determinism</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">full_determinism</span> <span class="k">else</span> <span class="n">set_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">hp_name</span> <span class="o">=</span> <span class="kc">None</span> + <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span> <span class="o">=</span> <span class="kc">None</span> + <span class="bp">self</span><span class="o">.</span><span class="n">is_in_train</span> <span class="o">=</span> <span class="kc">False</span> + + <span class="c1"># memory metrics - must set up as early as possible</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_memory_tracker</span> <span class="o">=</span> <span class="n">TrainerMemoryTracker</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">skip_memory_metrics</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_memory_tracker</span><span class="o">.</span><span class="n">start</span><span class="p">()</span> + + <span class="c1"># set the correct log level depending on the node</span> + <span class="n">log_level</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">get_process_log_level</span><span class="p">()</span> + <span class="n">logging</span><span class="o">.</span><span class="n">set_verbosity</span><span class="p">(</span><span class="n">log_level</span><span class="p">)</span> + + <span class="c1"># force device and distributed setup init explicitly</span> + <span class="n">args</span><span class="o">.</span><span class="n">_setup_devices</span> + + <span class="k">if</span> <span class="n">model</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="n">model_init</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_init</span> <span class="o">=</span> <span class="n">model_init</span> + <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">call_model_init</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;`Trainer` requires either a `model` or `model_init` argument&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">model_init</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span> + <span class="s2">&quot;`Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will&quot;</span> + <span class="s2">&quot; overwrite your model when calling the `train` method. This will become a fatal error in the next&quot;</span> + <span class="s2">&quot; release.&quot;</span><span class="p">,</span> + <span class="ne">FutureWarning</span><span class="p">,</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_init</span> <span class="o">=</span> <span class="n">model_init</span> + + <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="ow">in</span> <span class="n">MODEL_MAPPING_NAMES</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;The model you have picked (</span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">) cannot be used as is for training: it only &quot;</span> + <span class="s2">&quot;computes hidden states and does not accept any labels. You should choose a model with a head &quot;</span> + <span class="s2">&quot;suitable for your task like any of the `AutoModelForXxx` listed at &quot;</span> + <span class="s2">&quot;https://huggingface.co/docs/transformers/model_doc/auto.&quot;</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;is_parallelizable&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">model</span><span class="o">.</span><span class="n">is_parallelizable</span> <span class="ow">and</span> <span class="n">model</span><span class="o">.</span><span class="n">model_parallel</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">is_model_parallel</span> <span class="o">=</span> <span class="kc">True</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">is_model_parallel</span> <span class="o">=</span> <span class="kc">False</span> + + <span class="c1"># At this stage the model is already loaded</span> + <span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;is_loaded_in_8bit&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span> + <span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;_is_int8_training_enabled&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span> + <span class="s2">&quot;The model is loaded in 8-bit precision. To train this model you need to add additional modules&quot;</span> + <span class="s2">&quot; inside the model such as adapters using `peft` library and freeze the model weights. Please&quot;</span> + <span class="s2">&quot; check &quot;</span> + <span class="s2">&quot; the examples in https://github.com/huggingface/peft for more details.&quot;</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;The model you want to train is loaded in 8-bit precision. if you want to fine-tune an 8-bit&quot;</span> + <span class="s2">&quot; model, please make sure that you have installed `bitsandbytes&gt;=0.37.0`. &quot;</span> + <span class="p">)</span> + + <span class="c1"># Setup Sharded DDP training</span> + <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">sharded_ddp</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;Using --sharded_ddp xxx together with --deepspeed is not possible, deactivate one of those flags.&quot;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;Using --sharded_ddp xxx together with --fsdp is not possible, deactivate one of those flags.&quot;</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Using sharded DDP only works in distributed training.&quot;</span><span class="p">)</span> + <span class="k">elif</span> <span class="ow">not</span> <span class="n">is_fairscale_available</span><span class="p">():</span> + <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;Sharded DDP training requires fairscale: `pip install fairscale`.&quot;</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">SIMPLE</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">args</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="ow">and</span> <span class="n">FullyShardedDDP</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span> + <span class="s2">&quot;Sharded DDP in a mode other than simple training requires fairscale version &gt;= 0.3, found &quot;</span> + <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">fairscale</span><span class="o">.</span><span class="n">__version__</span><span class="si">}</span><span class="s2">. Upgrade your fairscale library: `pip install --upgrade fairscale`.&quot;</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">SIMPLE</span> <span class="ow">in</span> <span class="n">args</span><span class="o">.</span><span class="n">sharded_ddp</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="o">=</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">SIMPLE</span> + <span class="k">elif</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">ZERO_DP_2</span> <span class="ow">in</span> <span class="n">args</span><span class="o">.</span><span class="n">sharded_ddp</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="o">=</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">ZERO_DP_2</span> + <span class="k">elif</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">ZERO_DP_3</span> <span class="ow">in</span> <span class="n">args</span><span class="o">.</span><span class="n">sharded_ddp</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="o">=</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">ZERO_DP_3</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;Using --fsdp xxx together with --deepspeed is not possible, deactivate one of those flags.&quot;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="p">[</span><span class="s2">&quot;xla&quot;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Using fsdp only works in distributed training.&quot;</span><span class="p">)</span> + + <span class="c1"># dep_version_check(&quot;torch&gt;=1.12.0&quot;)</span> + <span class="c1"># Would have to update setup.py with torch&gt;=1.12.0</span> + <span class="c1"># which isn&#39;t ideally given that it will force people not using FSDP to also use torch&gt;=1.12.0</span> + <span class="c1"># below is the current alternative.</span> + <span class="k">if</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span><span class="o">.</span><span class="n">base_version</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="s2">&quot;1.12.0&quot;</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;FSDP requires PyTorch &gt;= 1.12.0&quot;</span><span class="p">)</span> + + <span class="kn">from</span> <span class="nn">torch.distributed.fsdp.fully_sharded_data_parallel</span> <span class="kn">import</span> <span class="n">BackwardPrefetch</span><span class="p">,</span> <span class="n">ShardingStrategy</span> + + <span class="k">if</span> <span class="n">FSDPOption</span><span class="o">.</span><span class="n">FULL_SHARD</span> <span class="ow">in</span> <span class="n">args</span><span class="o">.</span><span class="n">fsdp</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="o">=</span> <span class="n">ShardingStrategy</span><span class="o">.</span><span class="n">FULL_SHARD</span> + <span class="k">elif</span> <span class="n">FSDPOption</span><span class="o">.</span><span class="n">SHARD_GRAD_OP</span> <span class="ow">in</span> <span class="n">args</span><span class="o">.</span><span class="n">fsdp</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="o">=</span> <span class="n">ShardingStrategy</span><span class="o">.</span><span class="n">SHARD_GRAD_OP</span> + <span class="k">elif</span> <span class="n">FSDPOption</span><span class="o">.</span><span class="n">NO_SHARD</span> <span class="ow">in</span> <span class="n">args</span><span class="o">.</span><span class="n">fsdp</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="o">=</span> <span class="n">ShardingStrategy</span><span class="o">.</span><span class="n">NO_SHARD</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">backward_prefetch</span> <span class="o">=</span> <span class="n">BackwardPrefetch</span><span class="o">.</span><span class="n">BACKWARD_PRE</span> + <span class="k">if</span> <span class="s2">&quot;backward_prefetch&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span> <span class="ow">and</span> <span class="s2">&quot;backward_pos&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">backward_prefetch</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">backward_prefetch</span> <span class="o">=</span> <span class="n">BackwardPrefetch</span><span class="o">.</span><span class="n">BACKWARD_POST</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">forword_prefetch</span> <span class="o">=</span> <span class="kc">False</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;forword_prefect&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">forword_prefetch</span> <span class="o">=</span> <span class="kc">True</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">limit_all_gathers</span> <span class="o">=</span> <span class="kc">False</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;limit_all_gathers&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">limit_all_gathers</span> <span class="o">=</span> <span class="kc">True</span> + + <span class="c1"># one place to sort out whether to place the model on device or not</span> + <span class="c1"># postpone switching model to cuda when:</span> + <span class="c1"># 1. MP - since we are trying to fit a much bigger than 1 gpu model</span> + <span class="c1"># 2. fp16-enabled DeepSpeed loads the model in half the size and it doesn&#39;t need .to() anyway,</span> + <span class="c1"># and we only use deepspeed for training at the moment</span> + <span class="c1"># 3. full bf16 or fp16 eval - since the model needs to be cast to the right dtype first</span> + <span class="c1"># 4. Sharded DDP - same as MP</span> + <span class="c1"># 5. FSDP - same as MP</span> + <span class="bp">self</span><span class="o">.</span><span class="n">place_model_on_device</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">place_model_on_device</span> + <span class="k">if</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">is_model_parallel</span> + <span class="ow">or</span> <span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span> + <span class="ow">or</span> <span class="p">((</span><span class="n">args</span><span class="o">.</span><span class="n">fp16_full_eval</span> <span class="ow">or</span> <span class="n">args</span><span class="o">.</span><span class="n">bf16_full_eval</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">do_train</span><span class="p">)</span> + <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="ow">in</span> <span class="p">[</span><span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">ZERO_DP_2</span><span class="p">,</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">ZERO_DP_3</span><span class="p">])</span> + <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> + <span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">place_model_on_device</span> <span class="o">=</span> <span class="kc">False</span> + + <span class="n">default_collator</span> <span class="o">=</span> <span class="n">default_data_collator</span> <span class="k">if</span> <span class="n">tokenizer</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">DataCollatorWithPadding</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">data_collator</span> <span class="o">=</span> <span class="n">data_collator</span> <span class="k">if</span> <span class="n">data_collator</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">default_collator</span> + <span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span> <span class="o">=</span> <span class="n">train_dataset</span> + <span class="bp">self</span><span class="o">.</span><span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">eval_dataset</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="o">=</span> <span class="n">tokenizer</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">place_model_on_device</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;is_loaded_in_8bit&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_move_model_to_device</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="c1"># Force n_gpu to 1 to avoid DataParallel as MP will manage the GPUs</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_model_parallel</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">_n_gpu</span> <span class="o">=</span> <span class="mi">1</span> + + <span class="c1"># later use `self.model is self.model_wrapped` to check if it&#39;s wrapped or not</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="o">=</span> <span class="n">model</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span> <span class="o">=</span> <span class="n">compute_metrics</span> + <span class="bp">self</span><span class="o">.</span><span class="n">preprocess_logits_for_metrics</span> <span class="o">=</span> <span class="n">preprocess_logits_for_metrics</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">optimizers</span> + <span class="k">if</span> <span class="n">model_init</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> + <span class="s2">&quot;Passing a `model_init` is incompatible with providing the `optimizers` argument. &quot;</span> + <span class="s2">&quot;You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method.&quot;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">()</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span> + <span class="n">model_device</span> <span class="o">=</span> <span class="n">param</span><span class="o">.</span><span class="n">device</span> + <span class="k">break</span> + <span class="k">for</span> <span class="n">param_group</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">param_group</span><span class="p">[</span><span class="s2">&quot;params&quot;</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">optimizer_device</span> <span class="o">=</span> <span class="n">param_group</span><span class="p">[</span><span class="s2">&quot;params&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">device</span> + <span class="k">break</span> + <span class="k">if</span> <span class="n">model_device</span> <span class="o">!=</span> <span class="n">optimizer_device</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;The model and the optimizer parameters are not on the same device, which probably means you&quot;</span> + <span class="s2">&quot; created an optimizer around your model **before** putting on the device and passing it to the&quot;</span> + <span class="s2">&quot; `Trainer`. Make sure the lines `import torch_xla.core.xla_model as xm` and&quot;</span> + <span class="s2">&quot; `model.to(xm.xla_device())` is performed before the optimizer creation in your script.&quot;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span> <span class="ow">or</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">))</span> <span class="ow">and</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="p">):</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> + <span class="s2">&quot;Passing `optimizers` is not allowed if Fairscale, Deepspeed or PyTorch FSDP is enabled.&quot;</span> + <span class="s2">&quot;You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method.&quot;</span> + <span class="p">)</span> + <span class="n">default_callbacks</span> <span class="o">=</span> <span class="n">DEFAULT_CALLBACKS</span> <span class="o">+</span> <span class="n">get_reporting_integration_callbacks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">report_to</span><span class="p">)</span> + <span class="n">callbacks</span> <span class="o">=</span> <span class="n">default_callbacks</span> <span class="k">if</span> <span class="n">callbacks</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">default_callbacks</span> <span class="o">+</span> <span class="n">callbacks</span> + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span> <span class="o">=</span> <span class="n">CallbackHandler</span><span class="p">(</span> + <span class="n">callbacks</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">add_callback</span><span class="p">(</span><span class="n">PrinterCallback</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">disable_tqdm</span> <span class="k">else</span> <span class="n">DEFAULT_PROGRESS_CALLBACK</span><span class="p">)</span> + + <span class="c1"># Will be set to True by `self._setup_loggers()` on first call to `self.log()`.</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_loggers_initialized</span> <span class="o">=</span> <span class="kc">False</span> + + <span class="c1"># Create clone of distant repo and output directory if needed</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">push_to_hub</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">init_git_repo</span><span class="p">(</span><span class="n">at_init</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="c1"># In case of pull, we need to make sure every process has the latest.</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">xm</span><span class="o">.</span><span class="n">rendezvous</span><span class="p">(</span><span class="s2">&quot;init git repo&quot;</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="n">dist</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="nb">callable</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_collator</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">callable</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_collator</span><span class="p">,</span> <span class="s2">&quot;collate_batch&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The `data_collator` should be a simple callable (function, class with `__call__`).&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;max_steps is given, it will override any value given in num_train_epochs&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">train_dataset</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">has_length</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">)</span> <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;train_dataset does not implement __len__, max_steps has to be specified&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="p">(</span> + <span class="n">train_dataset</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">IterableDataset</span><span class="p">)</span> + <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">group_by_length</span> + <span class="p">):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;the `--group_by_length` option is only available for `Dataset`, not `IterableDataset&quot;</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">_signature_columns</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="c1"># Mixed precision setup</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_apex</span> <span class="o">=</span> <span class="kc">False</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_cuda_amp</span> <span class="o">=</span> <span class="kc">False</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_cpu_amp</span> <span class="o">=</span> <span class="kc">False</span> + + <span class="c1"># Mixed precision setup for SageMaker Model Parallel</span> + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="c1"># BF16 + model parallelism in SageMaker: currently not supported, raise an error</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">bf16</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;SageMaker Model Parallelism does not support BF16 yet. Please use FP16 instead &quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">IS_SAGEMAKER_MP_POST_1_10</span><span class="p">:</span> + <span class="c1"># When there&#39;s mismatch between SMP config and trainer argument, use SMP config as truth</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">fp16</span> <span class="o">!=</span> <span class="n">smp</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">cfg</span><span class="o">.</span><span class="n">fp16</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;FP16 provided in SM_HP_MP_PARAMETERS is </span><span class="si">{</span><span class="n">smp</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">cfg</span><span class="o">.</span><span class="n">fp16</span><span class="si">}</span><span class="s2">,&quot;</span> + <span class="sa">f</span><span class="s2">&quot;but FP16 provided in trainer argument is </span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">fp16</span><span class="si">}</span><span class="s2">,&quot;</span> + <span class="sa">f</span><span class="s2">&quot;setting to </span><span class="si">{</span><span class="n">smp</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">cfg</span><span class="o">.</span><span class="n">fp16</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="p">)</span> + <span class="n">args</span><span class="o">.</span><span class="n">fp16</span> <span class="o">=</span> <span class="n">smp</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">cfg</span><span class="o">.</span><span class="n">fp16</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># smp &lt; 1.10 does not support fp16 in trainer.</span> + <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">smp</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">cfg</span><span class="p">,</span> <span class="s2">&quot;fp16&quot;</span><span class="p">):</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;FP16 provided in SM_HP_MP_PARAMETERS is </span><span class="si">{</span><span class="n">smp</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">cfg</span><span class="o">.</span><span class="n">fp16</span><span class="si">}</span><span class="s2">, &quot;</span> + <span class="s2">&quot;but SageMaker Model Parallelism &lt; 1.10 does not support FP16 in trainer.&quot;</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">fp16</span> <span class="ow">or</span> <span class="n">args</span><span class="o">.</span><span class="n">bf16</span><span class="p">:</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">half_precision_backend</span> <span class="o">==</span> <span class="s2">&quot;auto&quot;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">):</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">fp16</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Tried to use `fp16` but it is not supported on cpu&quot;</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">_is_native_cpu_amp_available</span><span class="p">:</span> + <span class="n">args</span><span class="o">.</span><span class="n">half_precision_backend</span> <span class="o">=</span> <span class="s2">&quot;cpu_amp&quot;</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Tried to use cpu amp but native cpu amp is not available&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">args</span><span class="o">.</span><span class="n">half_precision_backend</span> <span class="o">=</span> <span class="s2">&quot;cuda_amp&quot;</span> + + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Using </span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">half_precision_backend</span><span class="si">}</span><span class="s2"> half precision backend&quot;</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span> <span class="o">=</span> <span class="kc">False</span> + <span class="k">if</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">fp16</span> <span class="ow">or</span> <span class="n">args</span><span class="o">.</span><span class="n">bf16</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span> <span class="ow">or</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> <span class="ow">or</span> <span class="n">is_torch_tpu_available</span><span class="p">()):</span> + <span class="c1"># deepspeed and SageMaker Model Parallel manage their own half precision</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">half_precision_backend</span> <span class="o">==</span> <span class="s2">&quot;cuda_amp&quot;</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_cuda_amp</span> <span class="o">=</span> <span class="kc">True</span> + <span class="bp">self</span><span class="o">.</span><span class="n">amp_dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">fp16</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span> + <span class="c1"># bf16 does not need grad scaling</span> + <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">amp_dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span> <span class="o">=</span> <span class="n">ShardedGradScaler</span><span class="p">()</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">torch.distributed.fsdp.sharded_grad_scaler</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">ShardedGradScaler</span> <span class="k">as</span> <span class="n">FSDPShardedGradScaler</span><span class="p">,</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span> <span class="o">=</span> <span class="n">FSDPShardedGradScaler</span><span class="p">()</span> + <span class="k">elif</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="kn">from</span> <span class="nn">torch_xla.amp</span> <span class="kn">import</span> <span class="n">GradScaler</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span> <span class="o">=</span> <span class="n">GradScaler</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">GradScaler</span><span class="p">()</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">half_precision_backend</span> <span class="o">==</span> <span class="s2">&quot;cpu_amp&quot;</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_cpu_amp</span> <span class="o">=</span> <span class="kc">True</span> + <span class="bp">self</span><span class="o">.</span><span class="n">amp_dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">is_apex_available</span><span class="p">():</span> + <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span> + <span class="s2">&quot;Using FP16 with APEX but APEX is not installed, please refer to&quot;</span> + <span class="s2">&quot; https://www.github.com/nvidia/apex.&quot;</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_apex</span> <span class="o">=</span> <span class="kc">True</span> + + <span class="c1"># FP16 + model parallelism in SageMaker: gradient clipping does not work for now so we raise a helpful error.</span> + <span class="k">if</span> <span class="p">(</span> + <span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> + <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cuda_amp</span> + <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">max_grad_norm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">max_grad_norm</span> <span class="o">&gt;</span> <span class="mi">0</span> + <span class="p">):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;SageMaker Model Parallelism in mixed precision mode does not support gradient clipping yet. Pass &quot;</span> + <span class="s2">&quot;along &#39;max_grad_norm&#39;: 0 in your hyperparameters.&quot;</span> + <span class="p">)</span> + + <span class="c1"># Label smoothing</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">label_smoothing_factor</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">label_smoother</span> <span class="o">=</span> <span class="n">LabelSmoother</span><span class="p">(</span><span class="n">epsilon</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">label_smoothing_factor</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">label_smoother</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">state</span> <span class="o">=</span> <span class="n">TrainerState</span><span class="p">(</span> + <span class="n">is_local_process_zero</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">is_local_process_zero</span><span class="p">(),</span> + <span class="n">is_world_process_zero</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">is_world_process_zero</span><span class="p">(),</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="n">TrainerControl</span><span class="p">()</span> + <span class="c1"># Internal variable to count flos in each process, will be accumulated in `self.state.total_flos` then</span> + <span class="c1"># returned to 0 every time flos need to be logged</span> + <span class="bp">self</span><span class="o">.</span><span class="n">current_flos</span> <span class="o">=</span> <span class="mi">0</span> + <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">=</span> <span class="kc">None</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_tune_checkpoints</span> <span class="o">=</span> <span class="kc">False</span> + <span class="n">default_label_names</span> <span class="o">=</span> <span class="n">find_labels</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span> <span class="o">=</span> <span class="n">default_label_names</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">label_names</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">label_names</span> + <span class="bp">self</span><span class="o">.</span><span class="n">can_return_loss</span> <span class="o">=</span> <span class="n">can_return_loss</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_init_end</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + + <span class="c1"># Internal variables to keep track of the original batch size</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_train_batch_size</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">train_batch_size</span> + + <span class="c1"># very last</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_memory_tracker</span><span class="o">.</span><span class="n">stop_and_update_metrics</span><span class="p">()</span> + + <span class="c1"># torch.compile</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">torch_compile</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">is_torch_compile_available</span><span class="p">():</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Using torch.compile requires PyTorch 2.0 or higher.&quot;</span><span class="p">)</span> + +<div class="viewcode-block" id="RaftTrainer.add_callback"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.add_callback">[docs]</a> <span class="k">def</span> <span class="nf">add_callback</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callback</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Add a callback to the current list of [`~transformer.TrainerCallback`].</span> +<span class="sd"> Args:</span> +<span class="sd"> callback (`type` or [`~transformer.TrainerCallback`]):</span> +<span class="sd"> A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the</span> +<span class="sd"> first case, will instantiate a member of that class.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">add_callback</span><span class="p">(</span><span class="n">callback</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.pop_callback"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.pop_callback">[docs]</a> <span class="k">def</span> <span class="nf">pop_callback</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callback</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Remove a callback from the current list of [`~transformer.TrainerCallback`] and returns it.</span> +<span class="sd"> If the callback is not found, returns `None` (and no error is raised).</span> +<span class="sd"> Args:</span> +<span class="sd"> callback (`type` or [`~transformer.TrainerCallback`]):</span> +<span class="sd"> A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the</span> +<span class="sd"> first case, will pop the first member of that class found in the list of callbacks.</span> +<span class="sd"> Returns:</span> +<span class="sd"> [`~transformer.TrainerCallback`]: The callback removed, if found.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">pop_callback</span><span class="p">(</span><span class="n">callback</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.remove_callback"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.remove_callback">[docs]</a> <span class="k">def</span> <span class="nf">remove_callback</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">callback</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Remove a callback from the current list of [`~transformer.TrainerCallback`].</span> +<span class="sd"> Args:</span> +<span class="sd"> callback (`type` or [`~transformer.TrainerCallback`]):</span> +<span class="sd"> A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the</span> +<span class="sd"> first case, will remove the first member of that class found in the list of callbacks.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">remove_callback</span><span class="p">(</span><span class="n">callback</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._move_model_to_device"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._move_model_to_device">[docs]</a> <span class="k">def</span> <span class="nf">_move_model_to_device</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> + <span class="c1"># Moving a model to an XLA device disconnects the tied weights, so we have to retie them.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_mode</span> <span class="o">==</span> <span class="n">ParallelMode</span><span class="o">.</span><span class="n">TPU</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;tie_weights&quot;</span><span class="p">):</span> + <span class="n">model</span><span class="o">.</span><span class="n">tie_weights</span><span class="p">()</span></div> + +<div class="viewcode-block" id="RaftTrainer._set_signature_columns_if_needed"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._set_signature_columns_if_needed">[docs]</a> <span class="k">def</span> <span class="nf">_set_signature_columns_if_needed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_signature_columns</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="c1"># Inspect model forward signature to keep only the arguments it accepts.</span> + <span class="n">signature</span> <span class="o">=</span> <span class="n">inspect</span><span class="o">.</span><span class="n">signature</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forward</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_signature_columns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">signature</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> + <span class="c1"># Labels may be named label or label_ids, the default data collator handles that.</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_signature_columns</span> <span class="o">+=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">([</span><span class="s2">&quot;label&quot;</span><span class="p">,</span> <span class="s2">&quot;label_ids&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">))</span></div> + +<div class="viewcode-block" id="RaftTrainer._remove_unused_columns"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._remove_unused_columns">[docs]</a> <span class="k">def</span> <span class="nf">_remove_unused_columns</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="s2">&quot;datasets.Dataset&quot;</span><span class="p">,</span> <span class="n">description</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">remove_unused_columns</span><span class="p">:</span> + <span class="k">return</span> <span class="n">dataset</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_set_signature_columns_if_needed</span><span class="p">()</span> + <span class="n">signature_columns</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_signature_columns</span> + + <span class="n">ignored_columns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">column_names</span><span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">signature_columns</span><span class="p">))</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ignored_columns</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">dset_description</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> <span class="k">if</span> <span class="n">description</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="sa">f</span><span class="s2">&quot;in the </span><span class="si">{</span><span class="n">description</span><span class="si">}</span><span class="s2"> set&quot;</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;The following columns </span><span class="si">{</span><span class="n">dset_description</span><span class="si">}</span><span class="s2"> don&#39;t have a corresponding argument in &quot;</span> + <span class="sa">f</span><span class="s2">&quot;`</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.forward` and have been ignored: </span><span class="si">{</span><span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ignored_columns</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span> + <span class="sa">f</span><span class="s2">&quot; If </span><span class="si">{</span><span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">ignored_columns</span><span class="p">)</span><span class="si">}</span><span class="s2"> are not expected by `</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">.forward`, &quot;</span> + <span class="s2">&quot; you can safely ignore this message.&quot;</span> + <span class="p">)</span> + + <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">signature_columns</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">column_names</span><span class="p">]</span> + + <span class="k">if</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">datasets</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="s2">&quot;1.4.0&quot;</span><span class="p">):</span> + <span class="n">dataset</span><span class="o">.</span><span class="n">set_format</span><span class="p">(</span> + <span class="nb">type</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">format</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">format_kwargs</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">format</span><span class="p">[</span><span class="s2">&quot;format_kwargs&quot;</span><span class="p">]</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">dataset</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">return</span> <span class="n">dataset</span><span class="o">.</span><span class="n">remove_columns</span><span class="p">(</span><span class="n">ignored_columns</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._get_collator_with_removed_columns"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_collator_with_removed_columns">[docs]</a> <span class="k">def</span> <span class="nf">_get_collator_with_removed_columns</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">data_collator</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">description</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;Wrap the data collator in a callable removing unused columns.&quot;&quot;&quot;</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">remove_unused_columns</span><span class="p">:</span> + <span class="k">return</span> <span class="n">data_collator</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_set_signature_columns_if_needed</span><span class="p">()</span> + <span class="n">signature_columns</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_signature_columns</span> + + <span class="n">remove_columns_collator</span> <span class="o">=</span> <span class="n">RemoveColumnsCollator</span><span class="p">(</span> + <span class="n">data_collator</span><span class="o">=</span><span class="n">data_collator</span><span class="p">,</span> + <span class="n">signature_columns</span><span class="o">=</span><span class="n">signature_columns</span><span class="p">,</span> + <span class="n">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">,</span> + <span class="n">description</span><span class="o">=</span><span class="n">description</span><span class="p">,</span> + <span class="n">model_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">remove_columns_collator</span></div> + +<div class="viewcode-block" id="RaftTrainer._get_train_sampler"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_train_sampler">[docs]</a> <span class="k">def</span> <span class="nf">_get_train_sampler</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Sampler</span><span class="p">]:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">has_length</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span><span class="p">):</span> + <span class="k">return</span> <span class="kc">None</span> + + <span class="n">generator</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">generator</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Generator</span><span class="p">()</span> + <span class="c1"># for backwards compatibility, we generate a seed here (which is sampled from a generator seeded with</span> + <span class="c1"># `args.seed`) if data_seed isn&#39;t provided.</span> + <span class="c1"># Further on in this method, we default to `args.seed` instead.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">data_seed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">seed</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span><span class="o">.</span><span class="n">random_</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">())</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">seed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">data_seed</span> + <span class="n">generator</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span> + + <span class="n">seed</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">data_seed</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">data_seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span> + + <span class="c1"># Build the sampler.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">group_by_length</span><span class="p">:</span> + <span class="k">if</span> <span class="n">is_datasets_available</span><span class="p">()</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">datasets</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span> + <span class="n">lengths</span> <span class="o">=</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">length_column_name</span><span class="p">]</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">length_column_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span><span class="o">.</span><span class="n">column_names</span> + <span class="k">else</span> <span class="kc">None</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">lengths</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">model_input_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">model_input_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span> + <span class="k">return</span> <span class="n">LengthGroupedSampler</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">train_batch_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span><span class="p">,</span> + <span class="n">dataset</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span><span class="p">,</span> + <span class="n">lengths</span><span class="o">=</span><span class="n">lengths</span><span class="p">,</span> + <span class="n">model_input_name</span><span class="o">=</span><span class="n">model_input_name</span><span class="p">,</span> + <span class="n">generator</span><span class="o">=</span><span class="n">generator</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">return</span> <span class="n">DistributedLengthGroupedSampler</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">train_batch_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span><span class="p">,</span> + <span class="n">dataset</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span><span class="p">,</span> + <span class="n">num_replicas</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span> + <span class="n">rank</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span><span class="p">,</span> + <span class="n">lengths</span><span class="o">=</span><span class="n">lengths</span><span class="p">,</span> + <span class="n">model_input_name</span><span class="o">=</span><span class="n">model_input_name</span><span class="p">,</span> + <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span> + <span class="p">)</span> + + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span> + <span class="k">return</span> <span class="n">RandomSampler</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">generator</span><span class="o">=</span><span class="n">generator</span><span class="p">)</span> + <span class="k">elif</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">parallel_mode</span> <span class="ow">in</span> <span class="p">[</span><span class="n">ParallelMode</span><span class="o">.</span><span class="n">TPU</span><span class="p">,</span> <span class="n">ParallelMode</span><span class="o">.</span><span class="n">SAGEMAKER_MODEL_PARALLEL</span><span class="p">]</span> + <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_drop_last</span> + <span class="p">):</span> + <span class="c1"># Use a loop for TPUs when drop_last is False to have all batches have the same size.</span> + <span class="k">return</span> <span class="n">DistributedSamplerWithLoop</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">per_device_train_batch_size</span><span class="p">,</span> + <span class="n">num_replicas</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span> + <span class="n">rank</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span><span class="p">,</span> + <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">return</span> <span class="n">DistributedSampler</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span><span class="p">,</span> + <span class="n">num_replicas</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span> + <span class="n">rank</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span><span class="p">,</span> + <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">,</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.get_train_dataloader"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_train_dataloader">[docs]</a> <span class="k">def</span> <span class="nf">get_train_dataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataLoader</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns the training [`~torch.utils.data.DataLoader`].</span> +<span class="sd"> Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed</span> +<span class="sd"> training if necessary) otherwise.</span> +<span class="sd"> Subclass and override this method if you want to inject some custom behavior.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Trainer: training requires a train_dataset.&quot;</span><span class="p">)</span> + + <span class="n">train_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_dataset</span> + <span class="n">data_collator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_collator</span> + <span class="k">if</span> <span class="n">is_datasets_available</span><span class="p">()</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">datasets</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span> + <span class="n">train_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_remove_unused_columns</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;training&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">data_collator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_collator_with_removed_columns</span><span class="p">(</span><span class="n">data_collator</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;training&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">train_dataset</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">IterableDataset</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">IterableDatasetShard</span><span class="p">(</span> + <span class="n">train_dataset</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_batch_size</span><span class="p">,</span> + <span class="n">drop_last</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_drop_last</span><span class="p">,</span> + <span class="n">num_processes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span> + <span class="n">process_index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span><span class="p">,</span> + <span class="p">)</span> + + <span class="k">return</span> <span class="n">DataLoader</span><span class="p">(</span> + <span class="n">train_dataset</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_batch_size</span><span class="p">,</span> + <span class="n">collate_fn</span><span class="o">=</span><span class="n">data_collator</span><span class="p">,</span> + <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_num_workers</span><span class="p">,</span> + <span class="n">pin_memory</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_pin_memory</span><span class="p">,</span> + <span class="p">)</span> + + <span class="n">train_sampler</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_train_sampler</span><span class="p">()</span> + + <span class="k">return</span> <span class="n">DataLoader</span><span class="p">(</span> + <span class="n">train_dataset</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_train_batch_size</span><span class="p">,</span> + <span class="n">sampler</span><span class="o">=</span><span class="n">train_sampler</span><span class="p">,</span> + <span class="n">collate_fn</span><span class="o">=</span><span class="n">data_collator</span><span class="p">,</span> + <span class="n">drop_last</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_drop_last</span><span class="p">,</span> + <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_num_workers</span><span class="p">,</span> + <span class="n">pin_memory</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_pin_memory</span><span class="p">,</span> + <span class="n">worker_init_fn</span><span class="o">=</span><span class="n">seed_worker</span><span class="p">,</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._get_eval_sampler"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_eval_sampler">[docs]</a> <span class="k">def</span> <span class="nf">_get_eval_sampler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eval_dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Sampler</span><span class="p">]:</span> + <span class="c1"># Deprecated code</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">use_legacy_prediction_loop</span><span class="p">:</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="k">return</span> <span class="n">SequentialDistributedSampler</span><span class="p">(</span> + <span class="n">eval_dataset</span><span class="p">,</span> <span class="n">num_replicas</span><span class="o">=</span><span class="n">xm</span><span class="o">.</span><span class="n">xrt_world_size</span><span class="p">(),</span> <span class="n">rank</span><span class="o">=</span><span class="n">xm</span><span class="o">.</span><span class="n">get_ordinal</span><span class="p">()</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="k">return</span> <span class="n">SequentialDistributedSampler</span><span class="p">(</span> + <span class="n">eval_dataset</span><span class="p">,</span> + <span class="n">num_replicas</span><span class="o">=</span><span class="n">smp</span><span class="o">.</span><span class="n">dp_size</span><span class="p">(),</span> + <span class="n">rank</span><span class="o">=</span><span class="n">smp</span><span class="o">.</span><span class="n">dp_rank</span><span class="p">(),</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">per_device_eval_batch_size</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="k">return</span> <span class="n">SequentialDistributedSampler</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">return</span> <span class="n">SequentialSampler</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span> + <span class="k">return</span> <span class="n">SequentialSampler</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">return</span> <span class="n">ShardSampler</span><span class="p">(</span> + <span class="n">eval_dataset</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">per_device_eval_batch_size</span><span class="p">,</span> + <span class="n">num_processes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span> + <span class="n">process_index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span><span class="p">,</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.get_eval_dataloader"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_eval_dataloader">[docs]</a> <span class="k">def</span> <span class="nf">get_eval_dataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">eval_dataset</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dataset</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataLoader</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns the evaluation [`~torch.utils.data.DataLoader`].</span> +<span class="sd"> Subclass and override this method if you want to inject some custom behavior.</span> +<span class="sd"> Args:</span> +<span class="sd"> eval_dataset (`torch.utils.data.Dataset`, *optional*):</span> +<span class="sd"> If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted</span> +<span class="sd"> by the `model.forward()` method are automatically removed. It must implement `__len__`.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="n">eval_dataset</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">eval_dataset</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Trainer: evaluation requires an eval_dataset.&quot;</span><span class="p">)</span> + <span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">eval_dataset</span> <span class="k">if</span> <span class="n">eval_dataset</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">eval_dataset</span> + <span class="n">data_collator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_collator</span> + + <span class="k">if</span> <span class="n">is_datasets_available</span><span class="p">()</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">,</span> <span class="n">datasets</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span> + <span class="n">eval_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_remove_unused_columns</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;evaluation&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">data_collator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_collator_with_removed_columns</span><span class="p">(</span><span class="n">data_collator</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;evaluation&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">IterableDataset</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">IterableDatasetShard</span><span class="p">(</span> + <span class="n">eval_dataset</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">per_device_eval_batch_size</span><span class="p">,</span> + <span class="n">drop_last</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_drop_last</span><span class="p">,</span> + <span class="n">num_processes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span> + <span class="n">process_index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">DataLoader</span><span class="p">(</span> + <span class="n">eval_dataset</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span><span class="p">,</span> + <span class="n">collate_fn</span><span class="o">=</span><span class="n">data_collator</span><span class="p">,</span> + <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_num_workers</span><span class="p">,</span> + <span class="n">pin_memory</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_pin_memory</span><span class="p">,</span> + <span class="p">)</span> + + <span class="n">eval_sampler</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_eval_sampler</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">DataLoader</span><span class="p">(</span> + <span class="n">eval_dataset</span><span class="p">,</span> + <span class="n">sampler</span><span class="o">=</span><span class="n">eval_sampler</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span><span class="p">,</span> + <span class="n">collate_fn</span><span class="o">=</span><span class="n">data_collator</span><span class="p">,</span> + <span class="n">drop_last</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_drop_last</span><span class="p">,</span> + <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_num_workers</span><span class="p">,</span> + <span class="n">pin_memory</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_pin_memory</span><span class="p">,</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.get_test_dataloader"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_test_dataloader">[docs]</a> <span class="k">def</span> <span class="nf">get_test_dataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">test_dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataLoader</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns the test [`~torch.utils.data.DataLoader`].</span> +<span class="sd"> Subclass and override this method if you want to inject some custom behavior.</span> +<span class="sd"> Args:</span> +<span class="sd"> test_dataset (`torch.utils.data.Dataset`, *optional*):</span> +<span class="sd"> The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the</span> +<span class="sd"> `model.forward()` method are automatically removed. It must implement `__len__`.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">data_collator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_collator</span> + + <span class="k">if</span> <span class="n">is_datasets_available</span><span class="p">()</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">datasets</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span> + <span class="n">test_dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_remove_unused_columns</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;test&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">data_collator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_collator_with_removed_columns</span><span class="p">(</span><span class="n">data_collator</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;test&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">IterableDataset</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">test_dataset</span> <span class="o">=</span> <span class="n">IterableDatasetShard</span><span class="p">(</span> + <span class="n">test_dataset</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span><span class="p">,</span> + <span class="n">drop_last</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_drop_last</span><span class="p">,</span> + <span class="n">num_processes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span> + <span class="n">process_index</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">DataLoader</span><span class="p">(</span> + <span class="n">test_dataset</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span><span class="p">,</span> + <span class="n">collate_fn</span><span class="o">=</span><span class="n">data_collator</span><span class="p">,</span> + <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_num_workers</span><span class="p">,</span> + <span class="n">pin_memory</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_pin_memory</span><span class="p">,</span> + <span class="p">)</span> + + <span class="n">test_sampler</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_eval_sampler</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">)</span> + + <span class="c1"># We use the same batch_size as for eval.</span> + <span class="k">return</span> <span class="n">DataLoader</span><span class="p">(</span> + <span class="n">test_dataset</span><span class="p">,</span> + <span class="n">sampler</span><span class="o">=</span><span class="n">test_sampler</span><span class="p">,</span> + <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span><span class="p">,</span> + <span class="n">collate_fn</span><span class="o">=</span><span class="n">data_collator</span><span class="p">,</span> + <span class="n">drop_last</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_drop_last</span><span class="p">,</span> + <span class="n">num_workers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_num_workers</span><span class="p">,</span> + <span class="n">pin_memory</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">dataloader_pin_memory</span><span class="p">,</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.create_optimizer_and_scheduler"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer_and_scheduler">[docs]</a> <span class="k">def</span> <span class="nf">create_optimizer_and_scheduler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_training_steps</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Setup the optimizer and the learning rate scheduler.</span> +<span class="sd"> We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the</span> +<span class="sd"> Trainer&#39;s init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or</span> +<span class="sd"> `create_scheduler`) in a subclass.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">create_optimizer</span><span class="p">()</span> + <span class="k">if</span> <span class="n">IS_SAGEMAKER_MP_POST_1_10</span> <span class="ow">and</span> <span class="n">smp</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">cfg</span><span class="o">.</span><span class="n">fp16</span><span class="p">:</span> + <span class="c1"># If smp &gt;= 1.10 and fp16 is enabled, we unwrap the optimizer</span> + <span class="n">optimizer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">optimizer</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">optimizer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> + <span class="bp">self</span><span class="o">.</span><span class="n">create_scheduler</span><span class="p">(</span><span class="n">num_training_steps</span><span class="o">=</span><span class="n">num_training_steps</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.create_optimizer"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer">[docs]</a> <span class="k">def</span> <span class="nf">create_optimizer</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Setup the optimizer.</span> +<span class="sd"> We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the</span> +<span class="sd"> Trainer&#39;s init through `optimizers`, or subclass and override this method in a subclass.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">opt_model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">decay_parameters</span> <span class="o">=</span> <span class="n">get_parameter_names</span><span class="p">(</span><span class="n">opt_model</span><span class="p">,</span> <span class="n">ALL_LAYERNORM_LAYERS</span><span class="p">)</span> + <span class="n">decay_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="n">name</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">decay_parameters</span> <span class="k">if</span> <span class="s2">&quot;bias&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">name</span><span class="p">]</span> + <span class="n">optimizer_grouped_parameters</span> <span class="o">=</span> <span class="p">[</span> + <span class="p">{</span> + <span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="p">[</span> + <span class="n">p</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">opt_model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()</span> <span class="k">if</span> <span class="p">(</span><span class="n">n</span> <span class="ow">in</span> <span class="n">decay_parameters</span> <span class="ow">and</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)</span> + <span class="p">],</span> + <span class="s2">&quot;weight_decay&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">weight_decay</span><span class="p">,</span> + <span class="p">},</span> + <span class="p">{</span> + <span class="s2">&quot;params&quot;</span><span class="p">:</span> <span class="p">[</span> + <span class="n">p</span> <span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">opt_model</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()</span> <span class="k">if</span> <span class="p">(</span><span class="n">n</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">decay_parameters</span> <span class="ow">and</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)</span> + <span class="p">],</span> + <span class="s2">&quot;weight_decay&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> + <span class="p">},</span> + <span class="p">]</span> + + <span class="n">optimizer_cls</span><span class="p">,</span> <span class="n">optimizer_kwargs</span> <span class="o">=</span> <span class="n">Trainer</span><span class="o">.</span><span class="n">get_optimizer_cls_and_kwargs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="o">==</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">SIMPLE</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">OSS</span><span class="p">(</span> + <span class="n">params</span><span class="o">=</span><span class="n">optimizer_grouped_parameters</span><span class="p">,</span> + <span class="n">optim</span><span class="o">=</span><span class="n">optimizer_cls</span><span class="p">,</span> + <span class="o">**</span><span class="n">optimizer_kwargs</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer_cls</span><span class="p">(</span><span class="n">optimizer_grouped_parameters</span><span class="p">,</span> <span class="o">**</span><span class="n">optimizer_kwargs</span><span class="p">)</span> + <span class="k">if</span> <span class="n">optimizer_cls</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;Adam8bit&quot;</span><span class="p">:</span> + <span class="kn">import</span> <span class="nn">bitsandbytes</span> + + <span class="n">manager</span> <span class="o">=</span> <span class="n">bitsandbytes</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">GlobalOptimManager</span><span class="o">.</span><span class="n">get_instance</span><span class="p">()</span> + + <span class="n">skipped</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">opt_model</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">):</span> + <span class="n">skipped</span> <span class="o">+=</span> <span class="nb">sum</span><span class="p">({</span><span class="n">p</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">():</span> <span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">parameters</span><span class="p">()}</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;skipped </span><span class="si">{</span><span class="n">module</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">skipped</span><span class="o">/</span><span class="mi">2</span><span class="o">**</span><span class="mi">20</span><span class="si">}</span><span class="s2">M params&quot;</span><span class="p">)</span> + <span class="n">manager</span><span class="o">.</span><span class="n">register_module_override</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="s2">&quot;weight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;optim_bits&quot;</span><span class="p">:</span> <span class="mi">32</span><span class="p">})</span> + <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;bitsandbytes: will optimize </span><span class="si">{</span><span class="n">module</span><span class="si">}</span><span class="s2"> in fp32&quot;</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;skipped: </span><span class="si">{</span><span class="n">skipped</span><span class="o">/</span><span class="mi">2</span><span class="o">**</span><span class="mi">20</span><span class="si">}</span><span class="s2">M params&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">smp</span><span class="o">.</span><span class="n">DistributedOptimizer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> + + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span></div> + + <span class="nd">@staticmethod</span> +<div class="viewcode-block" id="RaftTrainer.get_optimizer_cls_and_kwargs"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_optimizer_cls_and_kwargs">[docs]</a> <span class="k">def</span> <span class="nf">get_optimizer_cls_and_kwargs</span><span class="p">(</span><span class="n">args</span><span class="p">:</span> <span class="n">TrainingArguments</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Returns the optimizer class and optimizer parameters based on the training arguments.</span> +<span class="sd"> Args:</span> +<span class="sd"> args (`transformers.training_args.TrainingArguments`):</span> +<span class="sd"> The training arguments for the training session.</span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="c1"># parse args.optim_args</span> + <span class="n">optim_args</span> <span class="o">=</span> <span class="p">{}</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">optim_args</span><span class="p">:</span> + <span class="k">for</span> <span class="n">mapping</span> <span class="ow">in</span> <span class="n">args</span><span class="o">.</span><span class="n">optim_args</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">):</span> + <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">mapping</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;=&quot;</span><span class="p">)</span> + <span class="n">optim_args</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span> + + <span class="n">optimizer_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;lr&quot;</span><span class="p">:</span> <span class="n">args</span><span class="o">.</span><span class="n">learning_rate</span><span class="p">}</span> + + <span class="n">adam_kwargs</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;betas&quot;</span><span class="p">:</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">adam_beta1</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">adam_beta2</span><span class="p">),</span> + <span class="s2">&quot;eps&quot;</span><span class="p">:</span> <span class="n">args</span><span class="o">.</span><span class="n">adam_epsilon</span><span class="p">,</span> + <span class="p">}</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="o">==</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAFACTOR</span><span class="p">:</span> + <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">Adafactor</span> + <span class="n">optimizer_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s2">&quot;scale_parameter&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="s2">&quot;relative_step&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">})</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="o">==</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAMW_HF</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">transformers.optimization</span> <span class="kn">import</span> <span class="n">AdamW</span> + + <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">AdamW</span> + <span class="n">optimizer_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">adam_kwargs</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="ow">in</span> <span class="p">[</span><span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAMW_TORCH</span><span class="p">,</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAMW_TORCH_FUSED</span><span class="p">]:</span> + <span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">AdamW</span> + + <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">AdamW</span> + <span class="n">optimizer_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">adam_kwargs</span><span class="p">)</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="o">==</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAMW_TORCH_FUSED</span><span class="p">:</span> + <span class="n">optimizer_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s2">&quot;fused&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="o">==</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAMW_TORCH_XLA</span><span class="p">:</span> + <span class="k">try</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">torch_xla.amp.syncfree</span> <span class="kn">import</span> <span class="n">AdamW</span> + + <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">AdamW</span> + <span class="n">optimizer_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">adam_kwargs</span><span class="p">)</span> + <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Trainer failed to import syncfree AdamW from torch_xla.&quot;</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="o">==</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAMW_APEX_FUSED</span><span class="p">:</span> + <span class="k">try</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">apex.optimizers</span> <span class="kn">import</span> <span class="n">FusedAdam</span> + + <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">FusedAdam</span> + <span class="n">optimizer_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">adam_kwargs</span><span class="p">)</span> + <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Trainer tried to instantiate apex FusedAdam but apex is not installed!&quot;</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="o">==</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAMW_BNB</span><span class="p">:</span> + <span class="k">try</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">bitsandbytes.optim</span> <span class="kn">import</span> <span class="n">Adam8bit</span> + + <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">Adam8bit</span> + <span class="n">optimizer_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">adam_kwargs</span><span class="p">)</span> + <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Trainer tried to instantiate bnb Adam8bit but bnb is not installed!&quot;</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="o">==</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAMW_ANYPRECISION</span><span class="p">:</span> + <span class="k">try</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">torchdistx.optimizers</span> <span class="kn">import</span> <span class="n">AnyPrecisionAdamW</span> + + <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">AnyPrecisionAdamW</span> + <span class="n">optimizer_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">adam_kwargs</span><span class="p">)</span> + + <span class="c1"># TODO Change dtypes back to M=FP32, Var = BF16, Kahan = False once they can be cast together in torchdistx.</span> + <span class="n">optimizer_kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span> + <span class="p">{</span> + <span class="s2">&quot;use_kahan_summation&quot;</span><span class="p">:</span> <span class="n">strtobool</span><span class="p">(</span><span class="n">optim_args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;use_kahan_summation&quot;</span><span class="p">,</span> <span class="s2">&quot;False&quot;</span><span class="p">)),</span> + <span class="s2">&quot;momentum_dtype&quot;</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">torch</span><span class="p">,</span> <span class="n">optim_args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;momentum_dtype&quot;</span><span class="p">,</span> <span class="s2">&quot;float32&quot;</span><span class="p">)),</span> + <span class="s2">&quot;variance_dtype&quot;</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">torch</span><span class="p">,</span> <span class="n">optim_args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;variance_dtype&quot;</span><span class="p">,</span> <span class="s2">&quot;float32&quot;</span><span class="p">)),</span> + <span class="s2">&quot;compensation_buffer_dtype&quot;</span><span class="p">:</span> <span class="nb">getattr</span><span class="p">(</span> + <span class="n">torch</span><span class="p">,</span> <span class="n">optim_args</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;compensation_buffer_dtype&quot;</span><span class="p">,</span> <span class="s2">&quot;bfloat16&quot;</span><span class="p">)</span> + <span class="p">),</span> + <span class="p">}</span> + <span class="p">)</span> + <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Please install https://github.com/pytorch/torchdistx&quot;</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="o">==</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">SGD</span><span class="p">:</span> + <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">SGD</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">optim</span> <span class="o">==</span> <span class="n">OptimizerNames</span><span class="o">.</span><span class="n">ADAGRAD</span><span class="p">:</span> + <span class="n">optimizer_cls</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adagrad</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Trainer cannot instantiate unsupported optimizer: </span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">optim</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">return</span> <span class="n">optimizer_cls</span><span class="p">,</span> <span class="n">optimizer_kwargs</span></div> + +<div class="viewcode-block" id="RaftTrainer.create_scheduler"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_scheduler">[docs]</a> <span class="k">def</span> <span class="nf">create_scheduler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_training_steps</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Optimizer</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or</span> +<span class="sd"> passed as an argument.</span> +<span class="sd"> Args:</span> +<span class="sd"> num_training_steps (int): The number of training steps to do.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1">############</span> + <span class="n">num_training_steps</span> <span class="o">*=</span> <span class="mi">3</span> + <span class="c1">############</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">get_scheduler</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">lr_scheduler_type</span><span class="p">,</span> + <span class="n">optimizer</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="k">if</span> <span class="n">optimizer</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">optimizer</span><span class="p">,</span> + <span class="n">num_warmup_steps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">get_warmup_steps</span><span class="p">(</span><span class="n">num_training_steps</span><span class="p">),</span> + <span class="n">num_training_steps</span><span class="o">=</span><span class="n">num_training_steps</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span></div> + +<div class="viewcode-block" id="RaftTrainer.num_examples"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.num_examples">[docs]</a> <span class="k">def</span> <span class="nf">num_examples</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dataloader</span><span class="p">:</span> <span class="n">DataLoader</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When</span> +<span class="sd"> dataloader.dataset does not exist or has no length, estimates as best it can</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">dataset</span> <span class="o">=</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">dataset</span> + <span class="c1"># Special case for IterableDatasetShard, we need to dig deeper</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">IterableDatasetShard</span><span class="p">):</span> + <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span> + <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span> + <span class="k">except</span> <span class="p">(</span><span class="ne">NameError</span><span class="p">,</span> <span class="ne">AttributeError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span> <span class="c1"># no dataset or length, estimate by length of dataloader</span> + <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">per_device_train_batch_size</span></div> + +<div class="viewcode-block" id="RaftTrainer._hp_search_setup"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._hp_search_setup">[docs]</a> <span class="k">def</span> <span class="nf">_hp_search_setup</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trial</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s2">&quot;optuna.Trial&quot;</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;HP search setup code&quot;&quot;&quot;</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_trial</span> <span class="o">=</span> <span class="n">trial</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">trial</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">return</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">OPTUNA</span><span class="p">:</span> + <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_space</span><span class="p">(</span><span class="n">trial</span><span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">RAY</span><span class="p">:</span> + <span class="n">params</span> <span class="o">=</span> <span class="n">trial</span> + <span class="n">params</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;wandb&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">SIGOPT</span><span class="p">:</span> + <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="n">k</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">trial</span><span class="o">.</span><span class="n">assignments</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">WANDB</span><span class="p">:</span> + <span class="n">params</span> <span class="o">=</span> <span class="n">trial</span> + + <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Trying to set </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2"> in the hyperparameter search but there is no corresponding field in&quot;</span> + <span class="s2">&quot; `TrainingArguments`.&quot;</span> + <span class="p">)</span> + <span class="k">continue</span> + <span class="n">old_attr</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> + <span class="c1"># Casting value to the proper type</span> + <span class="k">if</span> <span class="n">old_attr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">value</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span><span class="n">old_attr</span><span class="p">)(</span><span class="n">value</span><span class="p">)</span> + <span class="nb">setattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">OPTUNA</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Trial: </span><span class="si">{</span><span class="n">trial</span><span class="o">.</span><span class="n">params</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">SIGOPT</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;SigOpt Assignments: </span><span class="si">{</span><span class="n">trial</span><span class="o">.</span><span class="n">assignments</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">WANDB</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;W&amp;B Sweep parameters: </span><span class="si">{</span><span class="n">trial</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># Rebuild the deepspeed config to reflect the updated training parameters</span> + <span class="kn">from</span> <span class="nn">transformers.deepspeed</span> <span class="kn">import</span> <span class="n">HfTrainerDeepSpeedConfig</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hf_deepspeed_config</span> <span class="o">=</span> <span class="n">HfTrainerDeepSpeedConfig</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hf_deepspeed_config</span><span class="o">.</span><span class="n">trainer_config_process</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._report_to_hp_search"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._report_to_hp_search">[docs]</a> <span class="k">def</span> <span class="nf">_report_to_hp_search</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trial</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s2">&quot;optuna.Trial&quot;</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]],</span> <span class="n">step</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">metrics</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">trial</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">return</span> + <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_objective</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">OPTUNA</span><span class="p">:</span> + <span class="kn">import</span> <span class="nn">optuna</span> + + <span class="n">trial</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">objective</span><span class="p">,</span> <span class="n">step</span><span class="p">)</span> + <span class="k">if</span> <span class="n">trial</span><span class="o">.</span><span class="n">should_prune</span><span class="p">():</span> + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_train_end</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + <span class="k">raise</span> <span class="n">optuna</span><span class="o">.</span><span class="n">TrialPruned</span><span class="p">()</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">RAY</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">ray</span> <span class="kn">import</span> <span class="n">tune</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_tune_save_checkpoint</span><span class="p">()</span> + <span class="n">tune</span><span class="o">.</span><span class="n">report</span><span class="p">(</span><span class="n">objective</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">objective</span><span class="p">,</span> <span class="o">**</span><span class="n">metrics</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._tune_save_checkpoint"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._tune_save_checkpoint">[docs]</a> <span class="k">def</span> <span class="nf">_tune_save_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="kn">from</span> <span class="nn">ray</span> <span class="kn">import</span> <span class="n">tune</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_tune_checkpoints</span><span class="p">:</span> + <span class="k">return</span> + <span class="k">with</span> <span class="n">tune</span><span class="o">.</span><span class="n">checkpoint_dir</span><span class="p">(</span><span class="n">step</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="p">)</span> <span class="k">as</span> <span class="n">checkpoint_dir</span><span class="p">:</span> + <span class="n">output_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">PREFIX_CHECKPOINT_DIR</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">save_model</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">_internal_call</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">save_to_json</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">TRAINER_STATE_NAME</span><span class="p">))</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">))</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">SCHEDULER_NAME</span><span class="p">))</span></div> + +<div class="viewcode-block" id="RaftTrainer.call_model_init"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.call_model_init">[docs]</a> <span class="k">def</span> <span class="nf">call_model_init</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trial</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="n">model_init_argcount</span> <span class="o">=</span> <span class="n">number_of_arguments</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_init</span><span class="p">)</span> + <span class="k">if</span> <span class="n">model_init_argcount</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_init</span><span class="p">()</span> + <span class="k">elif</span> <span class="n">model_init_argcount</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_init</span><span class="p">(</span><span class="n">trial</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;model_init should have 0 or 1 argument.&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">model</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;model_init should not return None.&quot;</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">model</span></div> + +<div class="viewcode-block" id="RaftTrainer.torch_jit_model_eval"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.torch_jit_model_eval">[docs]</a> <span class="k">def</span> <span class="nf">torch_jit_model_eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">dataloader</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">training</span><span class="p">:</span> + <span class="k">if</span> <span class="n">dataloader</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;failed to use PyTorch jit mode due to current dataloader is none.&quot;</span><span class="p">)</span> + <span class="k">return</span> <span class="n">model</span> + <span class="n">example_batch</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">dataloader</span><span class="p">))</span> + <span class="n">example_batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_inputs</span><span class="p">(</span><span class="n">example_batch</span><span class="p">)</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">jit_model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> + <span class="k">with</span> <span class="n">ContextManagers</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">autocast_smart_context_manager</span><span class="p">(</span><span class="n">cache_enabled</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()]):</span> + <span class="k">if</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span><span class="o">.</span><span class="n">base_version</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="s2">&quot;1.14.0&quot;</span><span class="p">):</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">example_batch</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">jit_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">jit_model</span><span class="p">,</span> <span class="n">example_kwarg_inputs</span><span class="o">=</span><span class="n">example_batch</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">jit_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span> + <span class="n">jit_model</span><span class="p">,</span> + <span class="n">example_kwarg_inputs</span><span class="o">=</span><span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">example_batch</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">example_batch</span><span class="p">},</span> + <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">jit_inputs</span> <span class="o">=</span> <span class="p">[]</span> + <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">example_batch</span><span class="p">:</span> + <span class="n">example_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">example_batch</span><span class="p">[</span><span class="n">key</span><span class="p">])</span> + <span class="n">jit_inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">example_tensor</span><span class="p">)</span> + <span class="n">jit_inputs</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">jit_inputs</span><span class="p">)</span> + <span class="n">jit_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">jit_model</span><span class="p">,</span> <span class="n">jit_inputs</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="n">jit_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">freeze</span><span class="p">(</span><span class="n">jit_model</span><span class="p">)</span> + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> + <span class="n">jit_model</span><span class="p">(</span><span class="o">**</span><span class="n">example_batch</span><span class="p">)</span> + <span class="n">jit_model</span><span class="p">(</span><span class="o">**</span><span class="n">example_batch</span><span class="p">)</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">jit_model</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_cpu_amp</span> <span class="o">=</span> <span class="kc">False</span> + <span class="bp">self</span><span class="o">.</span><span class="n">use_cuda_amp</span> <span class="o">=</span> <span class="kc">False</span> + <span class="k">except</span> <span class="p">(</span><span class="ne">RuntimeError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">NameError</span><span class="p">,</span> <span class="ne">IndexError</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;failed to use PyTorch jit mode due to: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">model</span></div> + +<div class="viewcode-block" id="RaftTrainer.ipex_optimize_model"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.ipex_optimize_model">[docs]</a> <span class="k">def</span> <span class="nf">ipex_optimize_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">):</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">is_ipex_available</span><span class="p">():</span> + <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span> + <span class="s2">&quot;Using IPEX but IPEX is not installed or IPEX&#39;s version does not match current PyTorch, please refer&quot;</span> + <span class="s2">&quot; to https://github.com/intel/intel-extension-for-pytorch.&quot;</span> + <span class="p">)</span> + + <span class="kn">import</span> <span class="nn">intel_extension_for_pytorch</span> <span class="k">as</span> <span class="nn">ipex</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="n">training</span><span class="p">:</span> + <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> + <span class="n">dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span> <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_in_train</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">bf16_full_eval</span> <span class="k">else</span> <span class="n">dtype</span> + <span class="c1"># conv_bn_folding is disabled as it fails in symbolic tracing, resulting in ipex warnings</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="s2">&quot;O1&quot;</span><span class="p">,</span> <span class="n">conv_bn_folding</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_in_train</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">training</span><span class="p">:</span> + <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span> + <span class="n">model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">ipex</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span> + <span class="n">model</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">level</span><span class="o">=</span><span class="s2">&quot;O1&quot;</span> + <span class="p">)</span> + + <span class="k">return</span> <span class="n">model</span></div> + +<div class="viewcode-block" id="RaftTrainer._wrap_model"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._wrap_model">[docs]</a> <span class="k">def</span> <span class="nf">_wrap_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dataloader</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">torch_compile</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">torch_compile_backend</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">torch_compile_mode</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">use_ipex</span><span class="p">:</span> + <span class="n">dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cpu_amp</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span> + <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ipex_optimize_model</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">training</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="c1"># Wrapping the base model twice in a DistributedModel will raise an error.</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span><span class="p">,</span> <span class="n">smp</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">DistributedModel</span><span class="p">):</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> + <span class="k">return</span> <span class="n">smp</span><span class="o">.</span><span class="n">DistributedModel</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backward_passes_per_step</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span><span class="p">)</span> + + <span class="c1"># already initialized its own DDP and AMP</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span> + + <span class="c1"># train/eval could be run multiple-times - if already wrapped, don&#39;t re-wrap it again</span> + <span class="k">if</span> <span class="n">unwrap_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">model</span><span class="p">:</span> + <span class="k">return</span> <span class="n">model</span> + + <span class="c1"># Mixed precision training with apex (torch &lt; 1.6)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_apex</span> <span class="ow">and</span> <span class="n">training</span><span class="p">:</span> + <span class="n">model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">amp</span><span class="o">.</span><span class="n">initialize</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">opt_level</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fp16_opt_level</span><span class="p">)</span> + + <span class="c1"># Multi-gpu training (should be after apex fp16 initialization)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">n_gpu</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">DataParallel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">jit_mode_eval</span><span class="p">:</span> + <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">torch_jit_model_eval</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">dataloader</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">jit_compilation_time</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> + + <span class="c1"># Note: in torch.distributed mode, there&#39;s no point in wrapping the model</span> + <span class="c1"># inside a DistributedDataParallel as we&#39;ll be under `no_grad` anyways.</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">training</span><span class="p">:</span> + <span class="k">return</span> <span class="n">model</span> + + <span class="c1"># Distributed training (should be after apex fp16 initialization)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="c1"># Sharded DDP!</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="o">==</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">SIMPLE</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">ShardedDDP</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">mixed_precision</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fp16</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">bf16</span> + <span class="n">cpu_offload</span> <span class="o">=</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">OFFLOAD</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">sharded_ddp</span> + <span class="n">zero_3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="o">==</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">ZERO_DP_3</span> + <span class="c1"># XXX: Breaking the self.model convention but I see no way around it for now.</span> + <span class="k">if</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">AUTO_WRAP</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">sharded_ddp</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">auto_wrap</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span> <span class="o">=</span> <span class="n">FullyShardedDDP</span><span class="p">(</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">mixed_precision</span><span class="o">=</span><span class="n">mixed_precision</span><span class="p">,</span> + <span class="n">reshard_after_forward</span><span class="o">=</span><span class="n">zero_3</span><span class="p">,</span> + <span class="n">cpu_offload</span><span class="o">=</span><span class="n">cpu_offload</span><span class="p">,</span> + <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="c1"># Distributed training using PyTorch FSDP</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="p">[</span><span class="s2">&quot;xla&quot;</span><span class="p">]:</span> + <span class="c1"># PyTorch FSDP!</span> + <span class="kn">from</span> <span class="nn">torch.distributed.fsdp.fully_sharded_data_parallel</span> <span class="kn">import</span> <span class="n">CPUOffload</span><span class="p">,</span> <span class="n">MixedPrecision</span> + <span class="kn">from</span> <span class="nn">torch.distributed.fsdp.fully_sharded_data_parallel</span> <span class="kn">import</span> <span class="n">FullyShardedDataParallel</span> <span class="k">as</span> <span class="n">FSDP</span> + <span class="kn">from</span> <span class="nn">torch.distributed.fsdp.wrap</span> <span class="kn">import</span> <span class="n">size_based_auto_wrap_policy</span><span class="p">,</span> <span class="n">transformer_auto_wrap_policy</span> + + <span class="k">if</span> <span class="n">FSDPOption</span><span class="o">.</span><span class="n">OFFLOAD</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp</span><span class="p">:</span> + <span class="n">cpu_offload</span> <span class="o">=</span> <span class="n">CPUOffload</span><span class="p">(</span><span class="n">offload_params</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">cpu_offload</span> <span class="o">=</span> <span class="n">CPUOffload</span><span class="p">(</span><span class="n">offload_params</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + + <span class="n">auto_wrap_policy</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="k">if</span> <span class="n">FSDPOption</span><span class="o">.</span><span class="n">AUTO_WRAP</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="p">[</span><span class="s2">&quot;fsdp_min_num_params&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">auto_wrap_policy</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span> + <span class="n">size_based_auto_wrap_policy</span><span class="p">,</span> <span class="n">min_num_params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="p">[</span><span class="s2">&quot;fsdp_min_num_params&quot;</span><span class="p">]</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;fsdp_transformer_layer_cls_to_wrap&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">transformer_cls_to_wrap</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> + <span class="k">for</span> <span class="n">layer_class</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="p">[</span><span class="s2">&quot;fsdp_transformer_layer_cls_to_wrap&quot;</span><span class="p">]:</span> + <span class="n">transformer_cls</span> <span class="o">=</span> <span class="n">get_module_class_from_name</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">layer_class</span><span class="p">)</span> + <span class="k">if</span> <span class="n">transformer_cls</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Could not find the transformer layer class to wrap in the model.&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">transformer_cls_to_wrap</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">transformer_cls</span><span class="p">)</span> + <span class="n">auto_wrap_policy</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span> + <span class="n">transformer_auto_wrap_policy</span><span class="p">,</span> + <span class="c1"># Transformer layer class to wrap</span> + <span class="n">transformer_layer_cls</span><span class="o">=</span><span class="n">transformer_cls_to_wrap</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">mixed_precision_policy</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">dtype</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fp16</span><span class="p">:</span> + <span class="n">dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">bf16</span><span class="p">:</span> + <span class="n">dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span> + <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">mixed_precision_policy</span> <span class="o">=</span> <span class="n">MixedPrecision</span><span class="p">(</span><span class="n">param_dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">reduce_dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">,</span> <span class="n">buffer_dtype</span><span class="o">=</span><span class="n">dtype</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="o">!=</span> <span class="n">FSDP</span><span class="p">:</span> + <span class="c1"># XXX: Breaking the self.model convention but I see no way around it for now.</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span> <span class="o">=</span> <span class="n">FSDP</span><span class="p">(</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">sharding_strategy</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span><span class="p">,</span> + <span class="n">cpu_offload</span><span class="o">=</span><span class="n">cpu_offload</span><span class="p">,</span> + <span class="n">auto_wrap_policy</span><span class="o">=</span><span class="n">auto_wrap_policy</span><span class="p">,</span> + <span class="n">mixed_precision</span><span class="o">=</span><span class="n">mixed_precision_policy</span><span class="p">,</span> + <span class="n">device_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> + <span class="n">backward_prefetch</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">backward_prefetch</span><span class="p">,</span> + <span class="n">forward_prefetch</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">forword_prefetch</span><span class="p">,</span> + <span class="n">limit_all_gathers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">limit_all_gathers</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">try</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">torch_xla.distributed.fsdp</span> <span class="kn">import</span> <span class="n">XlaFullyShardedDataParallel</span> <span class="k">as</span> <span class="n">FSDP</span> + <span class="kn">from</span> <span class="nn">torch_xla.distributed.fsdp</span> <span class="kn">import</span> <span class="n">checkpoint_module</span> + <span class="kn">from</span> <span class="nn">torch_xla.distributed.fsdp.wrap</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">size_based_auto_wrap_policy</span><span class="p">,</span> + <span class="n">transformer_auto_wrap_policy</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="s2">&quot;Missing XLA FSDP related module; please make sure to use torch-xla &gt;= 2.0.&quot;</span><span class="p">)</span> + <span class="n">auto_wrap_policy</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">auto_wrapper_callable</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="p">[</span><span class="s2">&quot;fsdp_min_num_params&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">auto_wrap_policy</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span> + <span class="n">size_based_auto_wrap_policy</span><span class="p">,</span> <span class="n">min_num_params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="p">[</span><span class="s2">&quot;fsdp_min_num_params&quot;</span><span class="p">]</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;fsdp_transformer_layer_cls_to_wrap&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">transformer_cls_to_wrap</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span> + <span class="k">for</span> <span class="n">layer_class</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="p">[</span><span class="s2">&quot;fsdp_transformer_layer_cls_to_wrap&quot;</span><span class="p">]:</span> + <span class="n">transformer_cls</span> <span class="o">=</span> <span class="n">get_module_class_from_name</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">layer_class</span><span class="p">)</span> + <span class="k">if</span> <span class="n">transformer_cls</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Could not find the transformer layer class to wrap in the model.&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">transformer_cls_to_wrap</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">transformer_cls</span><span class="p">)</span> + <span class="n">auto_wrap_policy</span> <span class="o">=</span> <span class="n">functools</span><span class="o">.</span><span class="n">partial</span><span class="p">(</span> + <span class="n">transformer_auto_wrap_policy</span><span class="p">,</span> + <span class="c1"># Transformer layer class to wrap</span> + <span class="n">transformer_layer_cls</span><span class="o">=</span><span class="n">transformer_cls_to_wrap</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">fsdp_kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">xla_fsdp_config</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fsdp_config</span><span class="p">[</span><span class="s2">&quot;xla_fsdp_grad_ckpt&quot;</span><span class="p">]:</span> + <span class="c1"># Apply gradient checkpointing to auto-wrapped sub-modules if specified</span> + <span class="k">def</span> <span class="nf">auto_wrapper_callable</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> + <span class="k">return</span> <span class="n">FSDP</span><span class="p">(</span><span class="n">checkpoint_module</span><span class="p">(</span><span class="n">m</span><span class="p">),</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + + <span class="c1"># Wrap the base model with an outer FSDP wrapper</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span> <span class="o">=</span> <span class="n">FSDP</span><span class="p">(</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">auto_wrap_policy</span><span class="o">=</span><span class="n">auto_wrap_policy</span><span class="p">,</span> + <span class="n">auto_wrapper_callable</span><span class="o">=</span><span class="n">auto_wrapper_callable</span><span class="p">,</span> + <span class="o">**</span><span class="n">fsdp_kwargs</span><span class="p">,</span> + <span class="p">)</span> + + <span class="c1"># Patch `xm.optimizer_step` should not reduce gradients in this case,</span> + <span class="c1"># as FSDP does not need gradient reduction over sharded parameters.</span> + <span class="k">def</span> <span class="nf">patched_optimizer_step</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">barrier</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">optimizer_args</span><span class="o">=</span><span class="p">{}):</span> + <span class="n">loss</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="o">**</span><span class="n">optimizer_args</span><span class="p">)</span> + <span class="k">if</span> <span class="n">barrier</span><span class="p">:</span> + <span class="n">xm</span><span class="o">.</span><span class="n">mark_step</span><span class="p">()</span> + <span class="k">return</span> <span class="n">loss</span> + + <span class="n">xm</span><span class="o">.</span><span class="n">optimizer_step</span> <span class="o">=</span> <span class="n">patched_optimizer_step</span> + <span class="k">elif</span> <span class="n">is_sagemaker_dp_enabled</span><span class="p">():</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">DistributedDataParallel</span><span class="p">(</span> + <span class="n">model</span><span class="p">,</span> <span class="n">device_ids</span><span class="o">=</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getenv</span><span class="p">(</span><span class="s2">&quot;SMDATAPARALLEL_LOCAL_RANK&quot;</span><span class="p">))]</span> + <span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{}</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">ddp_find_unused_parameters</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;find_unused_parameters&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">ddp_find_unused_parameters</span> + <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">PreTrainedModel</span><span class="p">):</span> + <span class="c1"># find_unused_parameters breaks checkpointing as per</span> + <span class="c1"># https://github.com/huggingface/transformers/pull/4659#issuecomment-643356021</span> + <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;find_unused_parameters&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_gradient_checkpointing</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;find_unused_parameters&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">ddp_bucket_cap_mb</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;bucket_cap_mb&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">ddp_bucket_cap_mb</span> + <span class="k">if</span> <span class="n">is_torch_neuroncore_available</span><span class="p">():</span> + <span class="k">return</span> <span class="n">model</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">parallel</span><span class="o">.</span><span class="n">DistributedDataParallel</span><span class="p">(</span> + <span class="n">model</span><span class="p">,</span> + <span class="n">device_ids</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_rank</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">_n_gpu</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">output_device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">_n_gpu</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> + <span class="p">)</span> + + <span class="k">return</span> <span class="n">model</span></div> + +<div class="viewcode-block" id="RaftTrainer.train"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.train">[docs]</a> <span class="k">def</span> <span class="nf">train</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">resume_from_checkpoint</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">trial</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s2">&quot;optuna.Trial&quot;</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">ignore_keys_for_eval</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">is_first_time</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Main training entry point.</span> +<span class="sd"> Args:</span> +<span class="sd"> resume_from_checkpoint (`str` or `bool`, *optional*):</span> +<span class="sd"> If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a</span> +<span class="sd"> `bool` and equals `True`, load the last checkpoint in *args.output_dir* as saved by a previous instance</span> +<span class="sd"> of [`Trainer`]. If present, training will resume from the model/optimizer/scheduler states loaded here.</span> +<span class="sd"> trial (`optuna.Trial` or `Dict[str, Any]`, *optional*):</span> +<span class="sd"> The trial run or the hyperparameter dictionary for hyperparameter search.</span> +<span class="sd"> ignore_keys_for_eval (`List[str]`, *optional*)</span> +<span class="sd"> A list of keys in the output of your model (if it is a dictionary) that should be ignored when</span> +<span class="sd"> gathering predictions for evaluation during the training.</span> +<span class="sd"> kwargs:</span> +<span class="sd"> Additional keyword arguments used to hide deprecated arguments</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="n">resume_from_checkpoint</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span> + <span class="n">resume_from_checkpoint</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="c1"># memory metrics - must set up as early as possible</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_memory_tracker</span><span class="o">.</span><span class="n">start</span><span class="p">()</span> + + <span class="n">args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span> + + <span class="c1">#self.is_in_train = True</span> + + <span class="c1"># do_train is not a reliable argument, as it might not be set and .train() still called, so</span> + <span class="c1"># the following is a workaround:</span> + <span class="k">if</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">fp16_full_eval</span> <span class="ow">or</span> <span class="n">args</span><span class="o">.</span><span class="n">bf16_full_eval</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">do_train</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_move_model_to_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="k">if</span> <span class="s2">&quot;model_path&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span> + <span class="n">resume_from_checkpoint</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;model_path&quot;</span><span class="p">)</span> + <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span> + <span class="s2">&quot;`model_path` is deprecated and will be removed in a future version. Use `resume_from_checkpoint` &quot;</span> + <span class="s2">&quot;instead.&quot;</span><span class="p">,</span> + <span class="ne">FutureWarning</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;train() received got unexpected keyword arguments: </span><span class="si">{</span><span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span> + <span class="c1"># This might change the seed so needs to run first.</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_hp_search_setup</span><span class="p">(</span><span class="n">trial</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_train_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">train_batch_size</span> + + <span class="c1"># Model re-init</span> + <span class="n">model_reloaded</span> <span class="o">=</span> <span class="kc">False</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_init</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="c1"># Seed must be set before instantiating the model when using model_init.</span> + <span class="n">enable_full_determinism</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">full_determinism</span> <span class="k">else</span> <span class="n">set_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">call_model_init</span><span class="p">(</span><span class="n">trial</span><span class="p">)</span> + <span class="n">model_reloaded</span> <span class="o">=</span> <span class="kc">True</span> + <span class="c1"># Reinitializes optimizer and scheduler</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span> + + <span class="c1"># Load potential model checkpoint</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)</span> <span class="ow">and</span> <span class="n">resume_from_checkpoint</span><span class="p">:</span> + <span class="n">resume_from_checkpoint</span> <span class="o">=</span> <span class="n">get_last_checkpoint</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">)</span> + <span class="k">if</span> <span class="n">resume_from_checkpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No valid checkpoint found in output directory (</span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">resume_from_checkpoint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_load_from_checkpoint</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">)</span> + + <span class="c1"># If model was re-initialized, put it on the right device and update self.model_wrapped</span> + <span class="k">if</span> <span class="n">model_reloaded</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">place_model_on_device</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_move_model_to_device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> + <span class="k">if</span> <span class="n">is_first_time</span><span class="p">:</span> + <span class="n">inner_training_loop1</span> <span class="o">=</span> <span class="n">find_executable_batch_size</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_inner_training_loop</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_batch_size</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">auto_find_batch_size</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">inner_training_loop1</span><span class="p">(</span> + <span class="n">args</span><span class="o">=</span><span class="n">args</span><span class="p">,</span> + <span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> + <span class="n">trial</span><span class="o">=</span><span class="n">trial</span><span class="p">,</span> + <span class="n">ignore_keys_for_eval</span><span class="o">=</span><span class="n">ignore_keys_for_eval</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">inner_training_loop2</span> <span class="o">=</span> <span class="n">find_executable_batch_size</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_one_train</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_batch_size</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">auto_find_batch_size</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">inner_training_loop2</span><span class="p">(</span> + <span class="n">args</span><span class="o">=</span><span class="n">args</span><span class="p">,</span> + <span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> + <span class="n">trial</span><span class="o">=</span><span class="n">trial</span><span class="p">,</span> + <span class="n">ignore_keys_for_eval</span><span class="o">=</span><span class="n">ignore_keys_for_eval</span><span class="p">,</span> + <span class="p">)</span></div> + + +<div class="viewcode-block" id="RaftTrainer._one_train"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._one_train">[docs]</a> <span class="k">def</span> <span class="nf">_one_train</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">trial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignore_keys_for_eval</span><span class="o">=</span><span class="kc">None</span> + <span class="p">):</span> + <span class="c1">#print(self.lr_scheduler)</span> + <span class="c1">#print(dir(self.lr_scheduler))</span> + + + <span class="bp">self</span><span class="o">.</span><span class="n">state</span> <span class="o">=</span> <span class="n">TrainerState</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">is_hyper_param_search</span> <span class="o">=</span> <span class="n">trial</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="c1"># Get dataloader</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_train_batch_size</span> <span class="o">=</span> <span class="n">batch_size</span> + <span class="c1"># Data loader and number of training steps</span> + <span class="n">train_dataloader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_train_dataloader</span><span class="p">()</span> + <span class="c1">#print(&quot;AAAAAAA&quot;, len(train_dataloader))</span> + + <span class="n">total_train_batch_size</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">train_batch_size</span> <span class="o">*</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> <span class="o">*</span> <span class="n">args</span><span class="o">.</span><span class="n">world_size</span> + + <span class="n">len_dataloader</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="n">has_length</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">):</span> + <span class="n">len_dataloader</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> + <span class="n">num_update_steps_per_epoch</span> <span class="o">=</span> <span class="n">len_dataloader</span> <span class="o">//</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> + <span class="n">num_update_steps_per_epoch</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">num_update_steps_per_epoch</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> + <span class="n">num_examples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_examples</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">max_steps</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> + <span class="n">num_train_epochs</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">//</span> <span class="n">num_update_steps_per_epoch</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span> + <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">%</span> <span class="n">num_update_steps_per_epoch</span> <span class="o">&gt;</span> <span class="mi">0</span> + <span class="p">)</span> + <span class="c1"># May be slightly incorrect if the last batch in the training dataloader has a smaller size but it&#39;s</span> + <span class="c1"># the best we can do.</span> + <span class="n">num_train_samples</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">*</span> <span class="n">total_train_batch_size</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">max_steps</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">num_train_epochs</span> <span class="o">*</span> <span class="n">num_update_steps_per_epoch</span><span class="p">)</span> + <span class="n">num_train_epochs</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">num_train_epochs</span><span class="p">)</span> + <span class="n">num_train_samples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_examples</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> <span class="o">*</span> <span class="n">args</span><span class="o">.</span><span class="n">num_train_epochs</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Rely on max_steps when dataloader does not have a working size</span> + <span class="n">max_steps</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> + <span class="c1"># Setting a very large number of epochs so we go as many times as necessary over the iterator.</span> + <span class="n">num_train_epochs</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">maxsize</span> + <span class="n">num_update_steps_per_epoch</span> <span class="o">=</span> <span class="n">max_steps</span> + <span class="n">num_examples</span> <span class="o">=</span> <span class="n">total_train_batch_size</span> <span class="o">*</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> + <span class="n">num_train_samples</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">*</span> <span class="n">total_train_batch_size</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;args.max_steps must be set to a positive value if dataloader does not have a length, was&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">max_steps</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="p">)</span> + <span class="c1">###########</span> + <span class="c1">#num_train_epochs = 5</span> + + <span class="c1"># Train!</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;***** Running training *****&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Num examples = </span><span class="si">{</span><span class="n">num_examples</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Num Epochs = </span><span class="si">{</span><span class="n">num_train_epochs</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Instantaneous batch size per device = </span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">per_device_train_batch_size</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Total train batch size (w. parallel, distributed &amp; accumulation) = </span><span class="si">{</span><span class="n">total_train_batch_size</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Gradient Accumulation steps = </span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Total optimization steps = </span><span class="si">{</span><span class="n">max_steps</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot; Number of trainable parameters = </span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span><span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="n">p</span><span class="w"> </span><span class="ow">in</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span><span class="w"> </span><span class="k">if</span><span class="w"> </span><span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">epoch</span> <span class="o">=</span> <span class="mi">0</span> + <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + <span class="n">epochs_trained</span> <span class="o">=</span> <span class="mi">0</span> + <span class="n">steps_trained_in_current_epoch</span> <span class="o">=</span> <span class="mi">0</span> + <span class="n">steps_trained_progress_bar</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="c1"># Update the references</span> + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">train_dataloader</span> <span class="o">=</span> <span class="n">train_dataloader</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_trial</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="c1"># use self._trial because the SigOpt/Optuna hpo only call `_hp_search_setup(trial)` instead of passing trial</span> + <span class="c1"># parameter to Train when using DDP.</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">trial_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_name</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_trial</span><span class="p">)</span> + <span class="k">if</span> <span class="n">trial</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">assignments</span> <span class="o">=</span> <span class="n">trial</span><span class="o">.</span><span class="n">assignments</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">SIGOPT</span> <span class="k">else</span> <span class="n">trial</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">trial_params</span> <span class="o">=</span> <span class="n">hp_params</span><span class="p">(</span><span class="n">assignments</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">trial_params</span> <span class="o">=</span> <span class="kc">None</span> + <span class="c1"># This should be the same if the state has been saved but in case the training arguments changed, it&#39;s safer</span> + <span class="c1"># to set this after the load.</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">=</span> <span class="n">max_steps</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">num_train_epochs</span> <span class="o">=</span> <span class="n">num_train_epochs</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">is_local_process_zero</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_local_process_zero</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">is_world_process_zero</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_world_process_zero</span><span class="p">()</span> + + <span class="c1"># tr_loss is a tensor to avoid synchronization of TPUs through .item()</span> + <span class="n">tr_loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="c1"># _total_loss_scalar is updated everytime .item() has to be called on tr_loss and stores the sum of all losses</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_total_loss_scalar</span> <span class="o">=</span> <span class="mf">0.0</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_globalstep_last_logged</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span> + <span class="c1">#model.zero_grad()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_train_begin</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + + <span class="c1"># Skip the first epochs_trained epochs to get the random state of the dataloader at the right point.</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">ignore_data_skip</span><span class="p">:</span> + <span class="c1">#print(&quot;I skip!&quot;) called</span> + <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs_trained</span><span class="p">):</span> + <span class="n">is_random_sampler</span> <span class="o">=</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">,</span> <span class="s2">&quot;sampler&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span> + <span class="n">train_dataloader</span><span class="o">.</span><span class="n">sampler</span><span class="p">,</span> <span class="n">RandomSampler</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">is_torch_less_than_1_11</span> <span class="ow">or</span> <span class="ow">not</span> <span class="n">is_random_sampler</span><span class="p">:</span> + <span class="c1"># We just need to begin an iteration to create the randomization of the sampler.</span> + <span class="c1"># That was before PyTorch 1.11 however...</span> + <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">train_dataloader</span><span class="p">:</span> + <span class="k">break</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># Otherwise we need to call the whooooole sampler cause there is some random operation added</span> + <span class="c1"># AT THE VERY END!</span> + <span class="n">_</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">train_dataloader</span><span class="o">.</span><span class="n">sampler</span><span class="p">)</span> + + <span class="c1">###############</span> + <span class="c1">#num_train_epochs = 10</span> + <span class="bp">self</span><span class="o">.</span><span class="n">is_in_train</span> <span class="o">=</span> <span class="kc">True</span> + <span class="c1">#print(&quot;The number of epoches: &quot;, num_train_epochs)</span> + <span class="c1">############# </span> + <span class="n">total_batched_samples</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs_trained</span><span class="p">,</span> <span class="n">num_train_epochs</span><span class="p">):</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">,</span> <span class="n">DataLoader</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">train_dataloader</span><span class="o">.</span><span class="n">sampler</span><span class="p">,</span> <span class="n">DistributedSampler</span><span class="p">):</span> + <span class="n">train_dataloader</span><span class="o">.</span><span class="n">sampler</span><span class="o">.</span><span class="n">set_epoch</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span> + <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">,</span> <span class="s2">&quot;dataset&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">train_dataloader</span><span class="o">.</span><span class="n">dataset</span><span class="p">,</span> <span class="n">IterableDatasetShard</span><span class="p">):</span> + <span class="n">train_dataloader</span><span class="o">.</span><span class="n">dataset</span><span class="o">.</span><span class="n">set_epoch</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">parallel_loader</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">ParallelLoader</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">,</span> <span class="p">[</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">])</span><span class="o">.</span><span class="n">per_device_loader</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">parallel_loader</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">train_dataloader</span> + + <span class="c1"># Reset the past mems state at the beginning of each epoch if necessary.</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_past</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="n">steps_in_epoch</span> <span class="o">=</span> <span class="p">(</span> + <span class="nb">len</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">)</span> + <span class="k">if</span> <span class="n">len_dataloader</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="k">else</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">*</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_epoch_begin</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">epoch</span> <span class="o">==</span> <span class="n">epochs_trained</span> <span class="ow">and</span> <span class="n">resume_from_checkpoint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">steps_trained_in_current_epoch</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_load_rng_state</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">)</span> + + <span class="n">rng_to_sync</span> <span class="o">=</span> <span class="kc">False</span> + <span class="n">steps_skipped</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">if</span> <span class="n">skip_first_batches</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">steps_trained_in_current_epoch</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">epoch_iterator</span> <span class="o">=</span> <span class="n">skip_first_batches</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">,</span> <span class="n">steps_trained_in_current_epoch</span><span class="p">)</span> + <span class="n">steps_skipped</span> <span class="o">=</span> <span class="n">steps_trained_in_current_epoch</span> + <span class="n">steps_trained_in_current_epoch</span> <span class="o">=</span> <span class="mi">0</span> + <span class="n">rng_to_sync</span> <span class="o">=</span> <span class="kc">True</span> + + <span class="c1">#print(&quot;The number of one epoch: &quot;, len(epoch_iterator))</span> + <span class="n">step</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> + <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">epoch_iterator</span><span class="p">):</span> + <span class="n">total_batched_samples</span> <span class="o">+=</span> <span class="mi">1</span> + <span class="k">if</span> <span class="n">rng_to_sync</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_load_rng_state</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">)</span> + <span class="n">rng_to_sync</span> <span class="o">=</span> <span class="kc">False</span> + + <span class="c1"># Skip past any already trained steps if resuming training</span> + <span class="k">if</span> <span class="n">steps_trained_in_current_epoch</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">steps_trained_in_current_epoch</span> <span class="o">-=</span> <span class="mi">1</span> + <span class="k">if</span> <span class="n">steps_trained_progress_bar</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">steps_trained_progress_bar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> + <span class="k">if</span> <span class="n">steps_trained_in_current_epoch</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_load_rng_state</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">)</span> + <span class="k">continue</span> + <span class="k">elif</span> <span class="n">steps_trained_progress_bar</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">steps_trained_progress_bar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span> + <span class="n">steps_trained_progress_bar</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="k">if</span> <span class="n">step</span> <span class="o">%</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_step_begin</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + + <span class="k">if</span> <span class="p">(</span> + <span class="p">(</span><span class="n">total_batched_samples</span> <span class="o">%</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> + <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span> + <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">_no_sync_in_gradient_accumulation</span> + <span class="p">):</span> + <span class="c1"># Avoid unnecessary DDP synchronization since there will be no backward pass on this example.</span> + <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span><span class="o">.</span><span class="n">no_sync</span><span class="p">():</span> + <span class="n">tr_loss_step</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training_step</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> + <span class="c1">#with model.no_sync():</span> + <span class="c1">#tr_loss_step = self.training_step(model, inputs)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">tr_loss_step</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training_step</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> + + <span class="k">if</span> <span class="p">(</span> + <span class="n">args</span><span class="o">.</span><span class="n">logging_nan_inf_filter</span> + <span class="ow">and</span> <span class="ow">not</span> <span class="n">is_torch_tpu_available</span><span class="p">()</span> + <span class="ow">and</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">tr_loss_step</span><span class="p">)</span> <span class="ow">or</span> <span class="n">torch</span><span class="o">.</span><span class="n">isinf</span><span class="p">(</span><span class="n">tr_loss_step</span><span class="p">))</span> + <span class="p">):</span> + <span class="c1"># if loss is nan or inf simply add the average of previous logged losses</span> + <span class="n">tr_loss</span> <span class="o">+=</span> <span class="n">tr_loss</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_globalstep_last_logged</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">tr_loss</span> <span class="o">+=</span> <span class="n">tr_loss_step</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">current_flos</span> <span class="o">+=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">floating_point_ops</span><span class="p">(</span><span class="n">inputs</span><span class="p">))</span> + + <span class="c1"># Optimizer step for deepspeed must be called on every step regardless of the value of gradient_accumulation_steps</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="o">.</span><span class="n">step</span><span class="p">()</span> + + <span class="k">if</span> <span class="n">total_batched_samples</span> <span class="o">%</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="p">(</span> + <span class="c1"># last step in epoch but step is always smaller than gradient_accumulation_steps</span> + <span class="n">steps_in_epoch</span> <span class="o">&lt;=</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> + <span class="ow">and</span> <span class="p">(</span><span class="n">step</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">steps_in_epoch</span> + <span class="p">):</span> + <span class="c1"># Gradient clipping</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">max_grad_norm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">max_grad_norm</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># deepspeed does its own clipping</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span><span class="p">:</span> + <span class="c1"># Reduce gradients first for XLA</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">gradients</span> <span class="o">=</span> <span class="n">xm</span><span class="o">.</span><span class="n">_fetch_gradients</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> + <span class="n">xm</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="n">gradients</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">xm</span><span class="o">.</span><span class="n">xrt_world_size</span><span class="p">())</span> + <span class="c1"># AMP: gradients need unscaling</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">unscale_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">fp16</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">clip_master_grads</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">max_grad_norm</span><span class="p">)</span> + <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">&quot;clip_grad_norm&quot;</span><span class="p">):</span> + <span class="c1"># Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">clip_grad_norm</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">max_grad_norm</span><span class="p">)</span> + <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;clip_grad_norm_&quot;</span><span class="p">):</span> + <span class="c1"># Some models (like FullyShardedDDP) have a specific way to do gradient clipping</span> + <span class="n">model</span><span class="o">.</span><span class="n">clip_grad_norm_</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">max_grad_norm</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># Revert to normal clipping otherwise, handling Apex or full precision</span> + <span class="n">nn</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">clip_grad_norm_</span><span class="p">(</span> + <span class="n">amp</span><span class="o">.</span><span class="n">master_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_apex</span> <span class="k">else</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> + <span class="n">args</span><span class="o">.</span><span class="n">max_grad_norm</span><span class="p">,</span> + <span class="p">)</span> + + <span class="c1"># Optimizer step</span> + <span class="n">optimizer_was_run</span> <span class="o">=</span> <span class="kc">True</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="k">pass</span> <span class="c1"># called outside the loop</span> + <span class="k">elif</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">update</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">xm</span><span class="o">.</span><span class="n">optimizer_step</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span><span class="p">:</span> + <span class="n">scale_before</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">get_scale</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">update</span><span class="p">()</span> + <span class="n">scale_after</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">get_scale</span><span class="p">()</span> + <span class="n">optimizer_was_run</span> <span class="o">=</span> <span class="n">scale_before</span> <span class="o">&lt;=</span> <span class="n">scale_after</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span> + + <span class="k">if</span> <span class="n">optimizer_was_run</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span> <span class="o">+=</span> <span class="mi">1</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">epoch</span> <span class="o">=</span> <span class="n">epoch</span> <span class="o">+</span> <span class="p">(</span><span class="n">step</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">steps_skipped</span><span class="p">)</span> <span class="o">/</span> <span class="n">steps_in_epoch</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_step_end</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">_maybe_log_save_evaluate</span><span class="p">(</span><span class="n">tr_loss</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span><span class="p">,</span> <span class="n">trial</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">ignore_keys_for_eval</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_substep_end</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">should_epoch_stop</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">should_training_stop</span><span class="p">:</span> + <span class="k">break</span> + <span class="k">if</span> <span class="n">step</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;There seems to be not a single sample in your epoch_iterator, stopping training at step&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="si">}</span><span class="s2">! This is expected if you&#39;re using an IterableDataset and set&quot;</span> + <span class="sa">f</span><span class="s2">&quot; num_steps (</span><span class="si">{</span><span class="n">max_steps</span><span class="si">}</span><span class="s2">) higher than the number of available samples.&quot;</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">should_training_stop</span> <span class="o">=</span> <span class="kc">True</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_epoch_end</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_maybe_log_save_evaluate</span><span class="p">(</span><span class="n">tr_loss</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span><span class="p">,</span> <span class="n">trial</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">ignore_keys_for_eval</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">DebugOption</span><span class="o">.</span><span class="n">TPU_METRICS_DEBUG</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">debug</span><span class="p">:</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="c1"># tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)</span> + <span class="n">xm</span><span class="o">.</span><span class="n">master_print</span><span class="p">(</span><span class="n">met</span><span class="o">.</span><span class="n">metrics_report</span><span class="p">())</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;You enabled PyTorch/XLA debug metrics but you don&#39;t have a TPU &quot;</span> + <span class="s2">&quot;configured. Check your training configuration if this is unexpected.&quot;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">should_training_stop</span><span class="p">:</span> + <span class="k">break</span> + + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_past&quot;</span><span class="p">):</span> + <span class="c1"># Clean the state at the end of training</span> + <span class="nb">delattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_past&quot;</span><span class="p">)</span> + + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Training completed. Do not forget to share your model on huggingface.co/models =)</span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">load_best_model_at_end</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="c1"># Wait for everyone to get here so we are sur the model has been saved by process 0.</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">xm</span><span class="o">.</span><span class="n">rendezvous</span><span class="p">(</span><span class="s2">&quot;load_best_model_at_end&quot;</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="n">dist</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span> + <span class="k">elif</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="n">smp</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">_load_best_model</span><span class="p">()</span> + + <span class="c1"># add remaining tr_loss</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_total_loss_scalar</span> <span class="o">+=</span> <span class="n">tr_loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> + <span class="n">train_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_total_loss_scalar</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span> + + <span class="n">metrics</span> <span class="o">=</span> <span class="n">speed_metrics</span><span class="p">(</span><span class="s2">&quot;train&quot;</span><span class="p">,</span> <span class="n">start_time</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="n">num_train_samples</span><span class="p">,</span> <span class="n">num_steps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">max_steps</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">store_flos</span><span class="p">()</span> + <span class="n">metrics</span><span class="p">[</span><span class="s2">&quot;total_flos&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">total_flos</span> + <span class="n">metrics</span><span class="p">[</span><span class="s2">&quot;train_loss&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">train_loss</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">is_in_train</span> <span class="o">=</span> <span class="kc">False</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">_memory_tracker</span><span class="o">.</span><span class="n">stop_and_update_metrics</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span> + + <span class="n">run_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_output_dir</span><span class="p">(</span><span class="n">trial</span><span class="p">)</span> + <span class="n">checkpoints_sorted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sorted_checkpoints</span><span class="p">(</span><span class="n">use_mtime</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">output_dir</span><span class="o">=</span><span class="n">run_dir</span><span class="p">)</span> + + <span class="c1"># Delete the last checkpoint when save_total_limit=1 if it&#39;s different from the best checkpoint and process allowed to save.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">save_total_limit</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="k">for</span> <span class="n">checkpoint</span> <span class="ow">in</span> <span class="n">checkpoints_sorted</span><span class="p">:</span> + <span class="k">if</span> <span class="n">checkpoint</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Deleting older checkpoint [</span><span class="si">{</span><span class="n">checkpoint</span><span class="si">}</span><span class="s2">] due to args.save_total_limit&quot;</span><span class="p">)</span> + <span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_train_end</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">TrainOutput</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="p">,</span> <span class="n">train_loss</span><span class="p">,</span> <span class="n">metrics</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._inner_training_loop"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._inner_training_loop">[docs]</a> <span class="k">def</span> <span class="nf">_inner_training_loop</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">trial</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ignore_keys_for_eval</span><span class="o">=</span><span class="kc">None</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&#39;&#39;&#39;</span> +<span class="sd"> 0 This function serves to train one time</span> +<span class="sd"> 1 Update the self.train_dataset before calling this function</span> +<span class="sd"> &#39;&#39;&#39;</span> + <span class="c1"># 1 Get dataloader</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_train_batch_size</span> <span class="o">=</span> <span class="n">batch_size</span> + <span class="c1"># Data loader and number of training steps</span> + <span class="n">train_dataloader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_train_dataloader</span><span class="p">()</span> + <span class="n">total_train_batch_size</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">train_batch_size</span> <span class="o">*</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> <span class="o">*</span> <span class="n">args</span><span class="o">.</span><span class="n">world_size</span> + + <span class="n">len_dataloader</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="n">has_length</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">):</span> + <span class="n">len_dataloader</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> + <span class="n">num_update_steps_per_epoch</span> <span class="o">=</span> <span class="n">len_dataloader</span> <span class="o">//</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> + <span class="n">num_update_steps_per_epoch</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">num_update_steps_per_epoch</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> + <span class="n">num_examples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_examples</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">max_steps</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> + <span class="n">num_train_epochs</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">//</span> <span class="n">num_update_steps_per_epoch</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span> + <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">%</span> <span class="n">num_update_steps_per_epoch</span> <span class="o">&gt;</span> <span class="mi">0</span> + <span class="p">)</span> + <span class="c1"># May be slightly incorrect if the last batch in the training dataloader has a smaller size but it&#39;s</span> + <span class="c1"># the best we can do.</span> + <span class="n">num_train_samples</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">*</span> <span class="n">total_train_batch_size</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">max_steps</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">num_train_epochs</span> <span class="o">*</span> <span class="n">num_update_steps_per_epoch</span><span class="p">)</span> + <span class="n">num_train_epochs</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">num_train_epochs</span><span class="p">)</span> + <span class="n">num_train_samples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_examples</span><span class="p">(</span><span class="n">train_dataloader</span><span class="p">)</span> <span class="o">*</span> <span class="n">args</span><span class="o">.</span><span class="n">num_train_epochs</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Rely on max_steps when dataloader does not have a working size</span> + <span class="n">max_steps</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> + <span class="c1"># Setting a very large number of epochs so we go as many times as necessary over the iterator.</span> + <span class="n">num_train_epochs</span> <span class="o">=</span> <span class="n">sys</span><span class="o">.</span><span class="n">maxsize</span> + <span class="n">num_update_steps_per_epoch</span> <span class="o">=</span> <span class="n">max_steps</span> + <span class="n">num_examples</span> <span class="o">=</span> <span class="n">total_train_batch_size</span> <span class="o">*</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> + <span class="n">num_train_samples</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">max_steps</span> <span class="o">*</span> <span class="n">total_train_batch_size</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;args.max_steps must be set to a positive value if dataloader does not have a length, was&quot;</span> + <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">max_steps</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">DebugOption</span><span class="o">.</span><span class="n">UNDERFLOW_OVERFLOW</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">debug</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">n_gpu</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> + <span class="c1"># nn.DataParallel(model) replicates the model, creating new variables and module</span> + <span class="c1"># references registered here no longer work on other gpus, breaking the module</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;Currently --debug underflow_overflow is not supported under DP. Please use DDP&quot;</span> + <span class="s2">&quot; (torch.distributed.launch).&quot;</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">debug_overflow</span> <span class="o">=</span> <span class="n">DebugUnderflowOverflow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span> <span class="c1"># noqa</span> + + <span class="n">delay_optimizer_creation</span> <span class="o">=</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="o">!=</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">SIMPLE</span> + <span class="ow">or</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> + <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="n">deepspeed_engine</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">deepspeed_init</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">num_training_steps</span><span class="o">=</span><span class="n">max_steps</span><span class="p">,</span> <span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="n">resume_from_checkpoint</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">deepspeed_engine</span><span class="o">.</span><span class="n">module</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="o">=</span> <span class="n">deepspeed_engine</span> + <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span> <span class="o">=</span> <span class="n">deepspeed_engine</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer</span> + <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">lr_scheduler</span> + <span class="c1">#print(&quot;I just create a optimizer here!&quot;) # called</span> + <span class="k">elif</span> <span class="ow">not</span> <span class="n">delay_optimizer_creation</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">create_optimizer_and_scheduler</span><span class="p">(</span><span class="n">num_training_steps</span><span class="o">=</span><span class="n">max_steps</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">state</span> <span class="o">=</span> <span class="n">TrainerState</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">is_hyper_param_search</span> <span class="o">=</span> <span class="n">trial</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + + <span class="c1"># Activate gradient checkpointing if needed</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">gradient_checkpointing</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">gradient_checkpointing_enable</span><span class="p">()</span> + + <span class="c1">#model = self._wrap_model(self.model_wrapped)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_wrap_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span><span class="p">)</span> + + + <span class="c1">#if is_sagemaker_mp_enabled() and resume_from_checkpoint is not None:</span> + <span class="c1"># self._load_from_checkpoint(resume_from_checkpoint, model)</span> + + <span class="c1"># for the rest of this function `model` is the outside model, whether it was wrapped or not</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmp_model</span> + + <span class="k">if</span> <span class="n">delay_optimizer_creation</span><span class="p">:</span> + <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;I create here!&quot;</span><span class="p">)</span> <span class="c1"># not called</span> + <span class="bp">self</span><span class="o">.</span><span class="n">create_optimizer_and_scheduler</span><span class="p">(</span><span class="n">num_training_steps</span><span class="o">=</span><span class="n">max_steps</span><span class="p">)</span> + + <span class="k">return</span> <span class="kc">True</span></div> + <span class="c1"># Check if saved optimizer or scheduler states exist</span> + <span class="c1">#self._load_optimizer_and_scheduler(resume_from_checkpoint)</span> + + <span class="c1"># important: at this point:</span> + <span class="c1"># self.model is the Transformers Model</span> + <span class="c1"># self.model_wrapped is DDP(Transformers Model), Deepspeed(Transformers Model), etc.</span> + + + +<div class="viewcode-block" id="RaftTrainer._get_output_dir"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_output_dir">[docs]</a> <span class="k">def</span> <span class="nf">_get_output_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">trial</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">trial</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">OPTUNA</span><span class="p">:</span> + <span class="n">run_id</span> <span class="o">=</span> <span class="n">trial</span><span class="o">.</span><span class="n">number</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">RAY</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">ray</span> <span class="kn">import</span> <span class="n">tune</span> + + <span class="n">run_id</span> <span class="o">=</span> <span class="n">tune</span><span class="o">.</span><span class="n">get_trial_id</span><span class="p">()</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">SIGOPT</span><span class="p">:</span> + <span class="n">run_id</span> <span class="o">=</span> <span class="n">trial</span><span class="o">.</span><span class="n">id</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">WANDB</span><span class="p">:</span> + <span class="kn">import</span> <span class="nn">wandb</span> + + <span class="n">run_id</span> <span class="o">=</span> <span class="n">wandb</span><span class="o">.</span><span class="n">run</span><span class="o">.</span><span class="n">id</span> + <span class="n">run_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_name</span><span class="p">(</span><span class="n">trial</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="sa">f</span><span class="s2">&quot;run-</span><span class="si">{</span><span class="n">run_id</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="n">run_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">run_name</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">run_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span> + <span class="k">return</span> <span class="n">run_dir</span></div> + +<div class="viewcode-block" id="RaftTrainer._load_from_checkpoint"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_from_checkpoint">[docs]</a> <span class="k">def</span> <span class="nf">_load_from_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="k">if</span> <span class="n">model</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">))</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span> + <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">WEIGHTS_INDEX_NAME</span><span class="p">)</span> + <span class="p">):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Can&#39;t find a valid checkpoint at </span><span class="si">{</span><span class="n">resume_from_checkpoint</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Loading model from </span><span class="si">{</span><span class="n">resume_from_checkpoint</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">CONFIG_NAME</span><span class="p">)):</span> + <span class="n">config</span> <span class="o">=</span> <span class="n">PretrainedConfig</span><span class="o">.</span><span class="n">from_json_file</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">CONFIG_NAME</span><span class="p">))</span> + <span class="n">checkpoint_version</span> <span class="o">=</span> <span class="n">config</span><span class="o">.</span><span class="n">transformers_version</span> + <span class="k">if</span> <span class="n">checkpoint_version</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">checkpoint_version</span> <span class="o">!=</span> <span class="n">__version__</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;You are resuming training from a checkpoint trained with </span><span class="si">{</span><span class="n">checkpoint_version</span><span class="si">}</span><span class="s2"> of &quot;</span> + <span class="sa">f</span><span class="s2">&quot;Transformers but your current version is </span><span class="si">{</span><span class="n">__version__</span><span class="si">}</span><span class="s2">. This is not recommended and could &quot;</span> + <span class="s2">&quot;yield to errors or unwanted behaviors.&quot;</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">)):</span> + <span class="c1"># If the model is on the GPU, it still works!</span> + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="s2">&quot;user_content.pt&quot;</span><span class="p">)):</span> + <span class="c1"># If the &#39;user_content.pt&#39; file exists, load with the new smp api.</span> + <span class="c1"># Checkpoint must have been saved with the new smp api.</span> + <span class="n">smp</span><span class="o">.</span><span class="n">resume_from_checkpoint</span><span class="p">(</span> + <span class="n">path</span><span class="o">=</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">tag</span><span class="o">=</span><span class="n">WEIGHTS_NAME</span><span class="p">,</span> <span class="n">partial</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">load_optimizer</span><span class="o">=</span><span class="kc">False</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># If the &#39;user_content.pt&#39; file does NOT exist, load with the old smp api.</span> + <span class="c1"># Checkpoint must have been saved with the old smp api.</span> + <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="s2">&quot;fp16&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">fp16</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;Enabling FP16 and loading from smp &lt; 1.10 checkpoint together is not suppported.&quot;</span> + <span class="p">)</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">),</span> <span class="n">map_location</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span> + <span class="c1"># Required for smp to not auto-translate state_dict from hf to smp (is already smp).</span> + <span class="n">state_dict</span><span class="p">[</span><span class="s2">&quot;_smp_is_partial&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span> + <span class="n">load_result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="c1"># release memory</span> + <span class="k">del</span> <span class="n">state_dict</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># We load the model state dict on the CPU to avoid an OOM error.</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">),</span> <span class="n">map_location</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span> + <span class="c1"># workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963</span> + <span class="c1"># which takes *args instead of **kwargs</span> + <span class="n">load_result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> + <span class="c1"># release memory</span> + <span class="k">del</span> <span class="n">state_dict</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_issue_warnings_after_load</span><span class="p">(</span><span class="n">load_result</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># We load the sharded checkpoint</span> + <span class="n">load_result</span> <span class="o">=</span> <span class="n">load_sharded_checkpoint</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">resume_from_checkpoint</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="n">is_sagemaker_mp_enabled</span><span class="p">())</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_issue_warnings_after_load</span><span class="p">(</span><span class="n">load_result</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._load_best_model"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_best_model">[docs]</a> <span class="k">def</span> <span class="nf">_load_best_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Loading best model from </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="si">}</span><span class="s2"> (score: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_metric</span><span class="si">}</span><span class="s2">).&quot;</span><span class="p">)</span> + <span class="n">best_model_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">)</span> + <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">best_model_path</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="c1"># this removes the pre-hooks from the previous engine</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span><span class="o">.</span><span class="n">destroy</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="c1"># temp hack until Deepspeed fixes the problem with resume from an existing engine that did some stepping</span> + <span class="n">deepspeed_engine</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">deepspeed_init</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">num_training_steps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">max_steps</span><span class="p">,</span> + <span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="p">,</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">deepspeed_engine</span><span class="o">.</span><span class="n">module</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="o">=</span> <span class="n">deepspeed_engine</span> + <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span> <span class="o">=</span> <span class="n">deepspeed_engine</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer</span> + <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">lr_scheduler</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="p">,</span> <span class="s2">&quot;user_content.pt&quot;</span><span class="p">)):</span> + <span class="c1"># If the &#39;user_content.pt&#39; file exists, load with the new smp api.</span> + <span class="c1"># Checkpoint must have been saved with the new smp api.</span> + <span class="n">smp</span><span class="o">.</span><span class="n">resume_from_checkpoint</span><span class="p">(</span> + <span class="n">path</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="p">,</span> + <span class="n">tag</span><span class="o">=</span><span class="n">WEIGHTS_NAME</span><span class="p">,</span> + <span class="n">partial</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">load_optimizer</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># If the &#39;user_content.pt&#39; file does NOT exist, load with the old smp api.</span> + <span class="c1"># Checkpoint must have been saved with the old smp api.</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">best_model_path</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span> + <span class="n">state_dict</span><span class="p">[</span><span class="s2">&quot;_smp_is_partial&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span> + <span class="n">load_result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># We load the model state dict on the CPU to avoid an OOM error.</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">best_model_path</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span> + <span class="c1"># If the model is on the GPU, it still works!</span> + <span class="c1"># workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963</span> + <span class="c1"># which takes *args instead of **kwargs</span> + <span class="n">load_result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_issue_warnings_after_load</span><span class="p">(</span><span class="n">load_result</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="p">,</span> <span class="n">WEIGHTS_INDEX_NAME</span><span class="p">)):</span> + <span class="n">load_result</span> <span class="o">=</span> <span class="n">load_sharded_checkpoint</span><span class="p">(</span> + <span class="n">model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> + <span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_issue_warnings_after_load</span><span class="p">(</span><span class="n">load_result</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Could not locate the best model at </span><span class="si">{</span><span class="n">best_model_path</span><span class="si">}</span><span class="s2">, if you are running a distributed training &quot;</span> + <span class="s2">&quot;on multiple nodes, you should activate `--save_on_each_node`.&quot;</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._issue_warnings_after_load"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._issue_warnings_after_load">[docs]</a> <span class="k">def</span> <span class="nf">_issue_warnings_after_load</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">load_result</span><span class="p">):</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">load_result</span><span class="o">.</span><span class="n">missing_keys</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">_keys_to_ignore_on_save</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">set</span><span class="p">(</span><span class="n">load_result</span><span class="o">.</span><span class="n">missing_keys</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">_keys_to_ignore_on_save</span> + <span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">tie_weights</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;There were missing keys in the checkpoint model loaded: </span><span class="si">{</span><span class="n">load_result</span><span class="o">.</span><span class="n">missing_keys</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">load_result</span><span class="o">.</span><span class="n">unexpected_keys</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;There were unexpected keys in the checkpoint model loaded: </span><span class="si">{</span><span class="n">load_result</span><span class="o">.</span><span class="n">unexpected_keys</span><span class="si">}</span><span class="s2">.&quot;</span> + <span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._maybe_log_save_evaluate"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._maybe_log_save_evaluate">[docs]</a> <span class="k">def</span> <span class="nf">_maybe_log_save_evaluate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tr_loss</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">trial</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">ignore_keys_for_eval</span><span class="p">):</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">should_log</span><span class="p">:</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">xm</span><span class="o">.</span><span class="n">mark_step</span><span class="p">()</span> + + <span class="n">logs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span> + + <span class="c1"># all_gather + mean() to get average loss over all processes</span> + <span class="n">tr_loss_scalar</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nested_gather</span><span class="p">(</span><span class="n">tr_loss</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> + + <span class="c1"># reset tr_loss to zero</span> + <span class="n">tr_loss</span> <span class="o">-=</span> <span class="n">tr_loss</span> + + <span class="n">logs</span><span class="p">[</span><span class="s2">&quot;loss&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">tr_loss_scalar</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_globalstep_last_logged</span><span class="p">),</span> <span class="mi">4</span><span class="p">)</span> + <span class="n">logs</span><span class="p">[</span><span class="s2">&quot;learning_rate&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_learning_rate</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">_total_loss_scalar</span> <span class="o">+=</span> <span class="n">tr_loss_scalar</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_globalstep_last_logged</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span> + <span class="bp">self</span><span class="o">.</span><span class="n">store_flos</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">logs</span><span class="p">)</span> + + <span class="n">metrics</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">should_evaluate</span><span class="p">:</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">eval_dataset</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="k">for</span> <span class="n">eval_dataset_name</span><span class="p">,</span> <span class="n">eval_dataset</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">eval_dataset</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span> + <span class="n">eval_dataset</span><span class="o">=</span><span class="n">eval_dataset</span><span class="p">,</span> + <span class="n">ignore_keys</span><span class="o">=</span><span class="n">ignore_keys_for_eval</span><span class="p">,</span> + <span class="n">metric_key_prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;eval_</span><span class="si">{</span><span class="n">eval_dataset_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">ignore_keys</span><span class="o">=</span><span class="n">ignore_keys_for_eval</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_report_to_hp_search</span><span class="p">(</span><span class="n">trial</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="p">,</span> <span class="n">metrics</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_save_checkpoint</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">trial</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="n">metrics</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._load_rng_state"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_rng_state">[docs]</a> <span class="k">def</span> <span class="nf">_load_rng_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">):</span> + <span class="c1"># Load RNG states from `checkpoint`</span> + <span class="k">if</span> <span class="n">checkpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">return</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">process_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span> + <span class="n">rng_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;rng_state_</span><span class="si">{</span><span class="n">process_index</span><span class="si">}</span><span class="s2">.pth&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">rng_file</span><span class="p">):</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Didn&#39;t find an RNG file for process </span><span class="si">{</span><span class="n">process_index</span><span class="si">}</span><span class="s2">, if you are resuming a training that &quot;</span> + <span class="s2">&quot;wasn&#39;t launched in a distributed fashion, reproducibility is not guaranteed.&quot;</span> + <span class="p">)</span> + <span class="k">return</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">rng_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="s2">&quot;rng_state.pth&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">rng_file</span><span class="p">):</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span> + <span class="s2">&quot;Didn&#39;t find an RNG file, if you are resuming a training that was launched in a distributed &quot;</span> + <span class="s2">&quot;fashion, reproducibility is not guaranteed.&quot;</span> + <span class="p">)</span> + <span class="k">return</span> + + <span class="n">checkpoint_rng_state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">rng_file</span><span class="p">)</span> + <span class="n">random</span><span class="o">.</span><span class="n">setstate</span><span class="p">(</span><span class="n">checkpoint_rng_state</span><span class="p">[</span><span class="s2">&quot;python&quot;</span><span class="p">])</span> + <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">checkpoint_rng_state</span><span class="p">[</span><span class="s2">&quot;numpy&quot;</span><span class="p">])</span> + <span class="n">torch</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">checkpoint_rng_state</span><span class="p">[</span><span class="s2">&quot;cpu&quot;</span><span class="p">])</span> + <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">checkpoint_rng_state</span><span class="p">[</span><span class="s2">&quot;cuda&quot;</span><span class="p">])</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_rng_state_all</span><span class="p">(</span><span class="n">checkpoint_rng_state</span><span class="p">[</span><span class="s2">&quot;cuda&quot;</span><span class="p">])</span> + <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Didn&#39;t manage to set back the RNG states of the GPU because of the following error:</span><span class="se">\n</span><span class="s2"> </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">This won&#39;t yield the same results as if the training had not been interrupted.&quot;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">xm</span><span class="o">.</span><span class="n">set_rng_state</span><span class="p">(</span><span class="n">checkpoint_rng_state</span><span class="p">[</span><span class="s2">&quot;xla&quot;</span><span class="p">])</span></div> + +<div class="viewcode-block" id="RaftTrainer._save_checkpoint"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_checkpoint">[docs]</a> <span class="k">def</span> <span class="nf">_save_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">trial</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="c1"># In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we</span> + <span class="c1"># want to save except FullyShardedDDP.</span> + <span class="c1"># assert unwrap_model(model) is self.model, &quot;internal model should be a reference to self.model&quot;</span> + + <span class="c1"># Save model checkpoint</span> + <span class="c1">#checkpoint_folder = f&quot;{PREFIX_CHECKPOINT_DIR}-{self.state.global_step}&quot;</span> + <span class="n">checkpoint_folder</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">PREFIX_CHECKPOINT_DIR</span><span class="si">}</span><span class="s2">-</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">save_counter</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="c1">##########</span> + <span class="bp">self</span><span class="o">.</span><span class="n">save_counter</span> <span class="o">+=</span> <span class="mi">1</span> + <span class="c1">##########</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">trial</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">store_flos</span><span class="p">()</span> + + <span class="n">run_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_output_dir</span><span class="p">(</span><span class="n">trial</span><span class="o">=</span><span class="n">trial</span><span class="p">)</span> + <span class="n">output_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">run_dir</span><span class="p">,</span> <span class="n">checkpoint_folder</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">save_model</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">_internal_call</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># under zero3 model file itself doesn&#39;t get saved since it&#39;s bogus! Unless deepspeed</span> + <span class="c1"># config `stage3_gather_16bit_weights_on_model_save` is True</span> + <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="o">.</span><span class="n">save_checkpoint</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span> + + <span class="c1"># Save optimizer and scheduler</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sharded_ddp</span> <span class="o">==</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">SIMPLE</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">consolidate_state_dict</span><span class="p">()</span> + + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">xm</span><span class="o">.</span><span class="n">rendezvous</span><span class="p">(</span><span class="s2">&quot;saving_optimizer_states&quot;</span><span class="p">)</span> + <span class="n">xm</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">))</span> + <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">(</span><span class="n">record</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">caught_warnings</span><span class="p">:</span> + <span class="n">xm</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">SCHEDULER_NAME</span><span class="p">))</span> + <span class="n">reissue_pt_warnings</span><span class="p">(</span><span class="n">caught_warnings</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="n">opt_state_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">local_state_dict</span><span class="p">(</span><span class="n">gather_if_shard</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="n">smp</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span> + <span class="k">if</span> <span class="n">smp</span><span class="o">.</span><span class="n">rdp_rank</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">smp</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">cfg</span><span class="o">.</span><span class="n">shard_optimizer_state</span><span class="p">:</span> + <span class="n">smp</span><span class="o">.</span><span class="n">save</span><span class="p">(</span> + <span class="n">opt_state_dict</span><span class="p">,</span> + <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">),</span> + <span class="n">partial</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">v3</span><span class="o">=</span><span class="n">smp</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">cfg</span><span class="o">.</span><span class="n">shard_optimizer_state</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">(</span><span class="n">record</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">caught_warnings</span><span class="p">:</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">SCHEDULER_NAME</span><span class="p">))</span> + <span class="n">reissue_pt_warnings</span><span class="p">(</span><span class="n">caught_warnings</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span><span class="p">:</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">SCALER_NAME</span><span class="p">))</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># deepspeed.save_checkpoint above saves model/optim/sched</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">))</span> + <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">(</span><span class="n">record</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">caught_warnings</span><span class="p">:</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">SCHEDULER_NAME</span><span class="p">))</span> + <span class="n">reissue_pt_warnings</span><span class="p">(</span><span class="n">caught_warnings</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span><span class="p">:</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">SCALER_NAME</span><span class="p">))</span> + + <span class="c1"># Determine the new best metric / best model checkpoint</span> + <span class="k">if</span> <span class="n">metrics</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">metric_for_best_model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">metric_to_check</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">metric_for_best_model</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">metric_to_check</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;eval_&quot;</span><span class="p">):</span> + <span class="n">metric_to_check</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;eval_</span><span class="si">{</span><span class="n">metric_to_check</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="n">metric_value</span> <span class="o">=</span> <span class="n">metrics</span><span class="p">[</span><span class="n">metric_to_check</span><span class="p">]</span> + + <span class="n">operator</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">greater</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">greater_is_better</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">less</span> + <span class="k">if</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_metric</span> <span class="ow">is</span> <span class="kc">None</span> + <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span> <span class="ow">is</span> <span class="kc">None</span> + <span class="ow">or</span> <span class="n">operator</span><span class="p">(</span><span class="n">metric_value</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_metric</span><span class="p">)</span> + <span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_metric</span> <span class="o">=</span> <span class="n">metric_value</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span> <span class="o">=</span> <span class="n">output_dir</span> + + <span class="c1"># Save the Trainer state</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">save_to_json</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">TRAINER_STATE_NAME</span><span class="p">))</span> + + <span class="c1"># Save RNG state in non-distributed training</span> + <span class="n">rng_states</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;python&quot;</span><span class="p">:</span> <span class="n">random</span><span class="o">.</span><span class="n">getstate</span><span class="p">(),</span> + <span class="s2">&quot;numpy&quot;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">get_state</span><span class="p">(),</span> + <span class="s2">&quot;cpu&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">(),</span> + <span class="p">}</span> + <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="c1"># In non distributed, we save the global CUDA RNG state (will take care of DataParallel)</span> + <span class="n">rng_states</span><span class="p">[</span><span class="s2">&quot;cuda&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">get_rng_state_all</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">rng_states</span><span class="p">[</span><span class="s2">&quot;cuda&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">()</span> + + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">rng_states</span><span class="p">[</span><span class="s2">&quot;xla&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">xm</span><span class="o">.</span><span class="n">get_rng_state</span><span class="p">()</span> + + <span class="c1"># A process can arrive here before the process 0 has a chance to save the model, in which case output_dir may</span> + <span class="c1"># not yet exist.</span> + <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">rng_states</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s2">&quot;rng_state.pth&quot;</span><span class="p">))</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">rng_states</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;rng_state_</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span><span class="si">}</span><span class="s2">.pth&quot;</span><span class="p">))</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">push_to_hub</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_push_from_checkpoint</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span> + + <span class="c1"># Maybe delete some older checkpoints.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_rotate_checkpoints</span><span class="p">(</span><span class="n">use_mtime</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">output_dir</span><span class="o">=</span><span class="n">run_dir</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._load_optimizer_and_scheduler"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_optimizer_and_scheduler">[docs]</a> <span class="k">def</span> <span class="nf">_load_optimizer_and_scheduler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">checkpoint</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;If optimizer and scheduler states exist, load them.&quot;&quot;&quot;</span> + <span class="k">if</span> <span class="n">checkpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">return</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># deepspeed loads optimizer/lr_scheduler together with the model in deepspeed_init</span> + <span class="k">return</span> + + <span class="n">checkpoint_file_exists</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;_*&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> + <span class="k">else</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">))</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">checkpoint_file_exists</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">SCHEDULER_NAME</span><span class="p">)):</span> + <span class="c1"># Load in optimizer and scheduler states</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="c1"># On TPU we have to take some extra precautions to properly load the states on the right device.</span> + <span class="n">optimizer_state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">),</span> <span class="n">map_location</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span> + <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">(</span><span class="n">record</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">caught_warnings</span><span class="p">:</span> + <span class="n">lr_scheduler_state</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">SCHEDULER_NAME</span><span class="p">),</span> <span class="n">map_location</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span> + <span class="n">reissue_pt_warnings</span><span class="p">(</span><span class="n">caught_warnings</span><span class="p">)</span> + + <span class="n">xm</span><span class="o">.</span><span class="n">send_cpu_data_to_device</span><span class="p">(</span><span class="n">optimizer_state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="n">xm</span><span class="o">.</span><span class="n">send_cpu_data_to_device</span><span class="p">(</span><span class="n">lr_scheduler_state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">optimizer_state</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">lr_scheduler_state</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">map_location</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span> <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">()</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">device</span> + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="s2">&quot;user_content.pt&quot;</span><span class="p">)):</span> + <span class="c1"># Optimizer checkpoint was saved with smp &gt;= 1.10</span> + <span class="k">def</span> <span class="nf">opt_load_hook</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">opt</span><span class="p">):</span> + <span class="n">opt</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">smp</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">),</span> <span class="n">partial</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span> + + <span class="k">else</span><span class="p">:</span> + <span class="c1"># Optimizer checkpoint was saved with smp &lt; 1.10</span> + <span class="k">def</span> <span class="nf">opt_load_hook</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">opt</span><span class="p">):</span> + <span class="k">if</span> <span class="n">IS_SAGEMAKER_MP_POST_1_10</span><span class="p">:</span> + <span class="n">opt</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span> + <span class="n">smp</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">),</span> <span class="n">partial</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">back_compat</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">opt</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">smp</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">),</span> <span class="n">partial</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span><span class="o">.</span><span class="n">register_post_step_hook</span><span class="p">(</span><span class="n">opt_load_hook</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span> + <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">OPTIMIZER_NAME</span><span class="p">),</span> <span class="n">map_location</span><span class="o">=</span><span class="n">map_location</span><span class="p">)</span> + <span class="p">)</span> + <span class="k">with</span> <span class="n">warnings</span><span class="o">.</span><span class="n">catch_warnings</span><span class="p">(</span><span class="n">record</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">caught_warnings</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">SCHEDULER_NAME</span><span class="p">)))</span> + <span class="n">reissue_pt_warnings</span><span class="p">(</span><span class="n">caught_warnings</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">SCALER_NAME</span><span class="p">)):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">SCALER_NAME</span><span class="p">)))</span></div> + +<div class="viewcode-block" id="RaftTrainer.hyperparameter_search"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.hyperparameter_search">[docs]</a> <span class="k">def</span> <span class="nf">hyperparameter_search</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">hp_space</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="s2">&quot;optuna.Trial&quot;</span><span class="p">],</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">compute_objective</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]],</span> <span class="nb">float</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">n_trials</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span> + <span class="n">direction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;minimize&quot;</span><span class="p">,</span> + <span class="n">backend</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="s2">&quot;str&quot;</span><span class="p">,</span> <span class="n">HPSearchBackend</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">hp_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="s2">&quot;optuna.Trial&quot;</span><span class="p">],</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">BestRun</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined</span> +<span class="sd"> by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,</span> +<span class="sd"> the sum of all metrics otherwise.</span> +<span class="sd"> &lt;Tip warning={true}&gt;</span> +<span class="sd"> To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to</span> +<span class="sd"> reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to</span> +<span class="sd"> subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`] for custom</span> +<span class="sd"> optimizer/scheduler.</span> +<span class="sd"> &lt;/Tip&gt;</span> +<span class="sd"> Args:</span> +<span class="sd"> hp_space (`Callable[[&quot;optuna.Trial&quot;], Dict[str, float]]`, *optional*):</span> +<span class="sd"> A function that defines the hyperparameter search space. Will default to</span> +<span class="sd"> [`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or</span> +<span class="sd"> [`~trainer_utils.default_hp_space_sigopt`] depending on your backend.</span> +<span class="sd"> compute_objective (`Callable[[Dict[str, float]], float]`, *optional*):</span> +<span class="sd"> A function computing the objective to minimize or maximize from the metrics returned by the `evaluate`</span> +<span class="sd"> method. Will default to [`~trainer_utils.default_compute_objective`].</span> +<span class="sd"> n_trials (`int`, *optional*, defaults to 100):</span> +<span class="sd"> The number of trial runs to test.</span> +<span class="sd"> direction (`str`, *optional*, defaults to `&quot;minimize&quot;`):</span> +<span class="sd"> Whether to optimize greater or lower objects. Can be `&quot;minimize&quot;` or `&quot;maximize&quot;`, you should pick</span> +<span class="sd"> `&quot;minimize&quot;` when optimizing the validation loss, `&quot;maximize&quot;` when optimizing one or several metrics.</span> +<span class="sd"> backend (`str` or [`~training_utils.HPSearchBackend`], *optional*):</span> +<span class="sd"> The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending</span> +<span class="sd"> on which one is installed. If all are installed, will default to optuna.</span> +<span class="sd"> hp_name (`Callable[[&quot;optuna.Trial&quot;], str]]`, *optional*):</span> +<span class="sd"> A function that defines the trial/run name. Will default to None.</span> +<span class="sd"> kwargs (`Dict[str, Any]`, *optional*):</span> +<span class="sd"> Additional keyword arguments passed along to `optuna.create_study` or `ray.tune.run`. For more</span> +<span class="sd"> information see:</span> +<span class="sd"> - the documentation of</span> +<span class="sd"> [optuna.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html)</span> +<span class="sd"> - the documentation of [tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run)</span> +<span class="sd"> - the documentation of [sigopt](https://app.sigopt.com/docs/endpoints/experiments/create)</span> +<span class="sd"> Returns:</span> +<span class="sd"> [`trainer_utils.BestRun`]: All the information about the best run. Experiment summary can be found in</span> +<span class="sd"> `run_summary` attribute for Ray backend.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="n">backend</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">backend</span> <span class="o">=</span> <span class="n">default_hp_search_backend</span><span class="p">()</span> + <span class="k">if</span> <span class="n">backend</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> + <span class="s2">&quot;At least one of optuna or ray should be installed. &quot;</span> + <span class="s2">&quot;To install optuna run `pip install optuna`. &quot;</span> + <span class="s2">&quot;To install ray run `pip install ray[tune]`. &quot;</span> + <span class="s2">&quot;To install sigopt run `pip install sigopt`.&quot;</span> + <span class="p">)</span> + <span class="n">backend</span> <span class="o">=</span> <span class="n">HPSearchBackend</span><span class="p">(</span><span class="n">backend</span><span class="p">)</span> + <span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">OPTUNA</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;You picked the optuna backend, but it is not installed. Use `pip install optuna`.&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">RAY</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> + <span class="s2">&quot;You picked the Ray Tune backend, but it is not installed. Use `pip install &#39;ray[tune]&#39;`.&quot;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">SIGOPT</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;You picked the sigopt backend, but it is not installed. Use `pip install sigopt`.&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="n">backend</span> <span class="o">==</span> <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">WANDB</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;You picked the wandb backend, but it is not installed. Use `pip install wandb`.&quot;</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">=</span> <span class="n">backend</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_init</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span> + <span class="s2">&quot;To use hyperparameter search, you need to pass your model through a model_init function.&quot;</span> + <span class="p">)</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">backend_dict</span> <span class="o">=</span> <span class="p">{</span> + <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">OPTUNA</span><span class="p">:</span> <span class="n">run_hp_search_optuna</span><span class="p">,</span> + <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">RAY</span><span class="p">:</span> <span class="n">run_hp_search_ray</span><span class="p">,</span> + <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">SIGOPT</span><span class="p">:</span> <span class="n">run_hp_search_sigopt</span><span class="p">,</span> + <span class="n">HPSearchBackend</span><span class="o">.</span><span class="n">WANDB</span><span class="p">:</span> <span class="n">run_hp_search_wandb</span><span class="p">,</span> + <span class="p">}</span> + <span class="n">backend_run</span> <span class="o">=</span> <span class="n">backend_dict</span><span class="p">[</span><span class="n">backend</span><span class="p">]</span> + <span class="k">except</span> <span class="ne">NameError</span><span class="p">:</span> + <span class="n">ALL_HYPERPARAMETER_SEARCH_BACKENDS</span> + <span class="n">backend_obj</span> <span class="o">=</span> <span class="n">ALL_HYPERPARAMETER_SEARCH_BACKENDS</span><span class="p">[</span><span class="n">backend</span><span class="p">]()</span> + <span class="n">backend_run</span> <span class="o">=</span> <span class="n">backend_obj</span><span class="o">.</span><span class="n">run</span> + <span class="k">try</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">transformers.trainer_utils</span> <span class="kn">import</span> <span class="n">default_hp_space</span> + <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span> + <span class="n">default_hp_space</span> <span class="o">=</span> <span class="n">backend_obj</span><span class="o">.</span><span class="n">default_hp_space</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">hp_space</span> <span class="o">=</span> <span class="n">default_hp_space</span><span class="p">[</span><span class="n">backend</span><span class="p">]</span> <span class="k">if</span> <span class="n">hp_space</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">hp_space</span> + <span class="bp">self</span><span class="o">.</span><span class="n">hp_name</span> <span class="o">=</span> <span class="n">hp_name</span> + <span class="bp">self</span><span class="o">.</span><span class="n">compute_objective</span> <span class="o">=</span> <span class="n">default_compute_objective</span> <span class="k">if</span> <span class="n">compute_objective</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">compute_objective</span> + + + <span class="n">best_run</span> <span class="o">=</span> <span class="n">backend_run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_trials</span><span class="p">,</span> <span class="n">direction</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">hp_search_backend</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">return</span> <span class="n">best_run</span></div> + +<div class="viewcode-block" id="RaftTrainer.log"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.log">[docs]</a> <span class="k">def</span> <span class="nf">log</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">logs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Log `logs` on the various objects watching training.</span> +<span class="sd"> Subclass and override this method to inject custom behavior.</span> +<span class="sd"> Args:</span> +<span class="sd"> logs (`Dict[str, float]`):</span> +<span class="sd"> The values to log.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">epoch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logs</span><span class="p">[</span><span class="s2">&quot;epoch&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">epoch</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> + + <span class="n">output</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">logs</span><span class="p">,</span> <span class="o">**</span><span class="p">{</span><span class="s2">&quot;step&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="p">}}</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">log_history</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_log</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">,</span> <span class="n">logs</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._prepare_input"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_input">[docs]</a> <span class="k">def</span> <span class="nf">_prepare_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">Mapping</span><span class="p">):</span> + <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)({</span><span class="n">k</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_input</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">items</span><span class="p">()})</span> + <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span> + <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)(</span><span class="bp">self</span><span class="o">.</span><span class="n">_prepare_input</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">data</span><span class="p">)</span> + <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span> + <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">}</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span> <span class="ow">and</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">is_floating_point</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="ow">or</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_complex</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span> + <span class="c1"># NLP models inputs are int/uint and those get adjusted to the right dtype of the</span> + <span class="c1"># embedding. Other models such as wav2vec2&#39;s inputs are already float and thus</span> + <span class="c1"># may need special handling to match the dtypes of the model</span> + <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s2">&quot;dtype&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hf_deepspeed_config</span><span class="o">.</span><span class="n">dtype</span><span class="p">()})</span> + <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">return</span> <span class="n">data</span></div> + +<div class="viewcode-block" id="RaftTrainer._prepare_inputs"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_inputs">[docs]</a> <span class="k">def</span> <span class="nf">_prepare_inputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Any</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and</span> +<span class="sd"> handling potential state.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_input</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;The batch received was empty, your model won&#39;t be able to train on it. Double-check that your &quot;</span> + <span class="sa">f</span><span class="s2">&quot;training dataset contains keys expected by the model: </span><span class="si">{</span><span class="s1">&#39;,&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_signature_columns</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span> + <span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_past</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;mems&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_past</span> + + <span class="k">return</span> <span class="n">inputs</span></div> + +<div class="viewcode-block" id="RaftTrainer.compute_loss_context_manager"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss_context_manager">[docs]</a> <span class="k">def</span> <span class="nf">compute_loss_context_manager</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> A helper wrapper to group together context managers.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">autocast_smart_context_manager</span><span class="p">()</span></div> + +<div class="viewcode-block" id="RaftTrainer.autocast_smart_context_manager"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.autocast_smart_context_manager">[docs]</a> <span class="k">def</span> <span class="nf">autocast_smart_context_manager</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cache_enabled</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired</span> +<span class="sd"> arguments, depending on the situation.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cuda_amp</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cpu_amp</span><span class="p">:</span> + <span class="k">if</span> <span class="n">is_torch_greater_or_equal_than_1_10</span><span class="p">:</span> + <span class="n">ctx_manager</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">torch</span><span class="o">.</span><span class="n">cpu</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">cache_enabled</span><span class="o">=</span><span class="n">cache_enabled</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">amp_dtype</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cpu_amp</span> + <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">autocast</span><span class="p">(</span><span class="n">cache_enabled</span><span class="o">=</span><span class="n">cache_enabled</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">amp_dtype</span><span class="p">)</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">ctx_manager</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">autocast</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">ctx_manager</span> <span class="o">=</span> <span class="n">contextlib</span><span class="o">.</span><span class="n">nullcontext</span><span class="p">()</span> <span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span> <span class="k">else</span> <span class="n">contextlib</span><span class="o">.</span><span class="n">suppress</span><span class="p">()</span> + + <span class="k">return</span> <span class="n">ctx_manager</span></div> + +<div class="viewcode-block" id="RaftTrainer.training_step"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.training_step">[docs]</a> <span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Any</span><span class="p">]])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform a training step on a batch of inputs.</span> +<span class="sd"> Subclass and override to inject custom behavior.</span> +<span class="sd"> Args:</span> +<span class="sd"> model (`nn.Module`):</span> +<span class="sd"> The model to train.</span> +<span class="sd"> inputs (`Dict[str, Union[torch.Tensor, Any]]`):</span> +<span class="sd"> The inputs and targets of the model.</span> +<span class="sd"> The dictionary will be unpacked before being fed to the model. Most models expect the targets under the</span> +<span class="sd"> argument `labels`. Check your model&#39;s documentation for all accepted arguments.</span> +<span class="sd"> Return:</span> +<span class="sd"> `torch.Tensor`: The tensor with training loss on this batch.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_inputs</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="n">loss_mb</span> <span class="o">=</span> <span class="n">smp_forward_backward</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span><span class="p">)</span> + <span class="k">return</span> <span class="n">loss_mb</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_loss_context_manager</span><span class="p">():</span> + <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_loss</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">n_gpu</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="c1"># mean() to average on multi-gpu parallel training</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># deepspeed handles loss scaling by gradient_accumulation_steps in its `backward`</span> + <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">gradient_accumulation_steps</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">do_grad_scaling</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">scaler</span><span class="o">.</span><span class="n">scale</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_apex</span><span class="p">:</span> + <span class="k">with</span> <span class="n">amp</span><span class="o">.</span><span class="n">scale_loss</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">)</span> <span class="k">as</span> <span class="n">scaled_loss</span><span class="p">:</span> + <span class="n">scaled_loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># loss gets scaled under gradient_accumulation_steps in deepspeed</span> + <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> + + <span class="k">return</span> <span class="n">loss</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span></div> + +<div class="viewcode-block" id="RaftTrainer.compute_loss"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss">[docs]</a> <span class="k">def</span> <span class="nf">compute_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">return_outputs</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> How the loss is computed by Trainer. By default, all models return the loss in the first element.</span> +<span class="sd"> Subclass and override for custom behavior.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_smoother</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="s2">&quot;labels&quot;</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;labels&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">)</span> + <span class="c1"># Save past state if it exists</span> + <span class="c1"># TODO: this needs to be fixed and made cleaner later.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_past</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">past_index</span><span class="p">]</span> + + <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="n">unwrap_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">_get_name</span><span class="p">()</span> <span class="ow">in</span> <span class="n">MODEL_FOR_CAUSAL_LM_MAPPING_NAMES</span><span class="o">.</span><span class="n">values</span><span class="p">():</span> + <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_smoother</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">shift_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_smoother</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="ow">and</span> <span class="s2">&quot;loss&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;The model did not return a loss from the inputs, only the following keys: &quot;</span> + <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="s1">&#39;,&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s2">. For reference, the inputs it received are </span><span class="si">{</span><span class="s1">&#39;,&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">inputs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s2">.&quot;</span> + <span class="p">)</span> + <span class="c1"># We don&#39;t use .loss here since the model may return tuples instead of ModelOutput.</span> + <span class="n">loss</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="s2">&quot;loss&quot;</span><span class="p">]</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">)</span> <span class="k">else</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + + <span class="k">return</span> <span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">outputs</span><span class="p">)</span> <span class="k">if</span> <span class="n">return_outputs</span> <span class="k">else</span> <span class="n">loss</span></div> + +<div class="viewcode-block" id="RaftTrainer.is_local_process_zero"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_local_process_zero">[docs]</a> <span class="k">def</span> <span class="nf">is_local_process_zero</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several</span> +<span class="sd"> machines) main process.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_process_index</span> <span class="o">==</span> <span class="mi">0</span></div> + +<div class="viewcode-block" id="RaftTrainer.is_world_process_zero"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_world_process_zero">[docs]</a> <span class="k">def</span> <span class="nf">is_world_process_zero</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Whether or not this process is the global main process (when training in a distributed fashion on several</span> +<span class="sd"> machines, this is only going to be `True` for one process).</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># Special case for SageMaker ModelParallel since there process_index is dp_process_index, not the global</span> + <span class="c1"># process index.</span> + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="k">return</span> <span class="n">smp</span><span class="o">.</span><span class="n">rank</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">process_index</span> <span class="o">==</span> <span class="mi">0</span></div> + +<div class="viewcode-block" id="RaftTrainer.save_model"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.save_model">[docs]</a> <span class="k">def</span> <span class="nf">save_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">_internal_call</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Will save the model, so you can reload it using `from_pretrained()`.</span> +<span class="sd"> Will only save from the main process.</span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="k">if</span> <span class="n">output_dir</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">output_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span> + + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_save_tpu</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="c1"># Calling the state_dict needs to be done on the wrapped model and on all processes.</span> + <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_save</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">state_dict</span><span class="o">=</span><span class="n">state_dict</span><span class="p">)</span> + <span class="k">if</span> <span class="n">IS_SAGEMAKER_MP_POST_1_10</span><span class="p">:</span> + <span class="c1"># &#39;user_content.pt&#39; indicates model state_dict saved with smp &gt;= 1.10</span> + <span class="n">Path</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s2">&quot;user_content.pt&quot;</span><span class="p">))</span><span class="o">.</span><span class="n">touch</span><span class="p">()</span> + <span class="k">elif</span> <span class="p">(</span> + <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">ZERO_DP_2</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">sharded_ddp</span> + <span class="ow">or</span> <span class="n">ShardedDDPOption</span><span class="o">.</span><span class="n">ZERO_DP_3</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">sharded_ddp</span> + <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">fsdp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="p">):</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_save</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">state_dict</span><span class="o">=</span><span class="n">state_dict</span><span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># this takes care of everything as long as we aren&#39;t under zero3</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_save</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">is_deepspeed_zero3_enabled</span><span class="p">():</span> + <span class="c1"># It&#39;s too complicated to try to override different places where the weights dump gets</span> + <span class="c1"># saved, so since under zero3 the file is bogus, simply delete it. The user should</span> + <span class="c1"># either user deepspeed checkpoint to resume or to recover full weights use</span> + <span class="c1"># zero_to_fp32.py stored in the checkpoint.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="n">file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">)</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">file</span><span class="p">):</span> + <span class="c1"># logger.info(f&quot;deepspeed zero3: removing {file}, see zero_to_fp32.py to recover weights&quot;)</span> + <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">file</span><span class="p">)</span> + + <span class="c1"># now save the real model if stage3_gather_16bit_weights_on_model_save=True</span> + <span class="c1"># if false it will not be saved.</span> + <span class="c1"># This must be called on all ranks</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="o">.</span><span class="n">save_16bit_model</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">):</span> + <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span> + <span class="s2">&quot;deepspeed.save_16bit_model didn&#39;t save the model, since&quot;</span> + <span class="s2">&quot; stage3_gather_16bit_weights_on_model_save=false. Saving the full checkpoint instead, use&quot;</span> + <span class="s2">&quot; zero_to_fp32.py to recover weights&quot;</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="o">.</span><span class="n">save_checkpoint</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span> + + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_save</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span> + + <span class="c1"># Push to the Hub when `save_model` is called by the user.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">push_to_hub</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">_internal_call</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">push_to_hub</span><span class="p">(</span><span class="n">commit_message</span><span class="o">=</span><span class="s2">&quot;Model save&quot;</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._save_tpu"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_tpu">[docs]</a> <span class="k">def</span> <span class="nf">_save_tpu</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span> + <span class="n">output_dir</span> <span class="o">=</span> <span class="n">output_dir</span> <span class="k">if</span> <span class="n">output_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saving model checkpoint to </span><span class="si">{</span><span class="n">output_dir</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">xm</span><span class="o">.</span><span class="n">is_master_ordinal</span><span class="p">():</span> + <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">TRAINING_ARGS_NAME</span><span class="p">))</span> + + <span class="c1"># Save a trained model and configuration using `save_pretrained()`.</span> + <span class="c1"># They can then be reloaded using `from_pretrained()`</span> + <span class="n">xm</span><span class="o">.</span><span class="n">rendezvous</span><span class="p">(</span><span class="s2">&quot;saving_checkpoint&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">PreTrainedModel</span><span class="p">):</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">unwrap_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">),</span> <span class="n">PreTrainedModel</span><span class="p">):</span> + <span class="n">unwrap_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span> + <span class="n">output_dir</span><span class="p">,</span> + <span class="n">is_main_process</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">,</span> + <span class="n">state_dict</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> + <span class="n">save_function</span><span class="o">=</span><span class="n">xm</span><span class="o">.</span><span class="n">save</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Trainer.model is not a `PreTrainedModel`, only saving its state dict.&quot;</span><span class="p">)</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span> + <span class="n">xm</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">))</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">is_main_process</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">,</span> <span class="n">save_function</span><span class="o">=</span><span class="n">xm</span><span class="o">.</span><span class="n">save</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._save"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save">[docs]</a> <span class="k">def</span> <span class="nf">_save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">state_dict</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="c1"># If we are executing this function, we are the process zero, so we don&#39;t check for that.</span> + <span class="n">output_dir</span> <span class="o">=</span> <span class="n">output_dir</span> <span class="k">if</span> <span class="n">output_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span> + <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saving model checkpoint to </span><span class="si">{</span><span class="n">output_dir</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="c1"># Save a trained model and configuration using `save_pretrained()`.</span> + <span class="c1"># They can then be reloaded using `from_pretrained()`</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">PreTrainedModel</span><span class="p">):</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">unwrap_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">),</span> <span class="n">PreTrainedModel</span><span class="p">):</span> + <span class="k">if</span> <span class="n">state_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span> + <span class="n">unwrap_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">state_dict</span><span class="o">=</span><span class="n">state_dict</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Trainer.model is not a `PreTrainedModel`, only saving its state dict.&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="n">state_dict</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">state_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">state_dict</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">))</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">state_dict</span><span class="o">=</span><span class="n">state_dict</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span> + + <span class="c1"># Good practice: save your training arguments together with the trained model</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">TRAINING_ARGS_NAME</span><span class="p">))</span></div> + +<div class="viewcode-block" id="RaftTrainer.store_flos"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.store_flos">[docs]</a> <span class="k">def</span> <span class="nf">store_flos</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="c1"># Storing the number of floating-point operations that went into the model</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">total_flos</span> <span class="o">+=</span> <span class="p">(</span> + <span class="n">distributed_broadcast_scalars</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">current_flos</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">current_flos</span> <span class="o">=</span> <span class="mi">0</span> + <span class="k">else</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">total_flos</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_flos</span> + <span class="bp">self</span><span class="o">.</span><span class="n">current_flos</span> <span class="o">=</span> <span class="mi">0</span></div> + +<div class="viewcode-block" id="RaftTrainer._sorted_checkpoints"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._sorted_checkpoints">[docs]</a> <span class="k">def</span> <span class="nf">_sorted_checkpoints</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">output_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">checkpoint_prefix</span><span class="o">=</span><span class="n">PREFIX_CHECKPOINT_DIR</span><span class="p">,</span> <span class="n">use_mtime</span><span class="o">=</span><span class="kc">False</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span> + <span class="n">ordering_and_checkpoint_path</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="n">glob_checkpoints</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">Path</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">checkpoint_prefix</span><span class="si">}</span><span class="s2">-*&quot;</span><span class="p">)</span> <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">x</span><span class="p">)]</span> + + <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">glob_checkpoints</span><span class="p">:</span> + <span class="k">if</span> <span class="n">use_mtime</span><span class="p">:</span> + <span class="n">ordering_and_checkpoint_path</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">getmtime</span><span class="p">(</span><span class="n">path</span><span class="p">),</span> <span class="n">path</span><span class="p">))</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">regex_match</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;.*</span><span class="si">{</span><span class="n">checkpoint_prefix</span><span class="si">}</span><span class="s2">-([0-9]+)&quot;</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> + <span class="k">if</span> <span class="n">regex_match</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">regex_match</span><span class="o">.</span><span class="n">groups</span><span class="p">()</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">ordering_and_checkpoint_path</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="nb">int</span><span class="p">(</span><span class="n">regex_match</span><span class="o">.</span><span class="n">groups</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">path</span><span class="p">))</span> + + <span class="n">checkpoints_sorted</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">ordering_and_checkpoint_path</span><span class="p">)</span> + <span class="n">checkpoints_sorted</span> <span class="o">=</span> <span class="p">[</span><span class="n">checkpoint</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">checkpoint</span> <span class="ow">in</span> <span class="n">checkpoints_sorted</span><span class="p">]</span> + <span class="c1"># Make sure we don&#39;t delete the best model.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">best_model_index</span> <span class="o">=</span> <span class="n">checkpoints_sorted</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span><span class="p">)))</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">best_model_index</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">checkpoints_sorted</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="p">):</span> + <span class="n">checkpoints_sorted</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">checkpoints_sorted</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">checkpoints_sorted</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="n">checkpoints_sorted</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> + <span class="k">return</span> <span class="n">checkpoints_sorted</span></div> + +<div class="viewcode-block" id="RaftTrainer._rotate_checkpoints"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._rotate_checkpoints">[docs]</a> <span class="k">def</span> <span class="nf">_rotate_checkpoints</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">use_mtime</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">output_dir</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">save_total_limit</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">save_total_limit</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">return</span> + + <span class="c1"># Check if we should delete older checkpoint(s)</span> + <span class="n">checkpoints_sorted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sorted_checkpoints</span><span class="p">(</span><span class="n">use_mtime</span><span class="o">=</span><span class="n">use_mtime</span><span class="p">,</span> <span class="n">output_dir</span><span class="o">=</span><span class="n">output_dir</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">checkpoints_sorted</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">save_total_limit</span><span class="p">:</span> + <span class="k">return</span> + + <span class="c1"># If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which</span> + <span class="c1"># we don&#39;t do to allow resuming.</span> + <span class="n">save_total_limit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">save_total_limit</span> + <span class="k">if</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">save_total_limit</span> <span class="o">==</span> <span class="mi">1</span> + <span class="ow">and</span> <span class="n">checkpoints_sorted</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">best_model_checkpoint</span> + <span class="p">):</span> + <span class="n">save_total_limit</span> <span class="o">=</span> <span class="mi">2</span> + + <span class="n">number_of_checkpoints_to_delete</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">checkpoints_sorted</span><span class="p">)</span> <span class="o">-</span> <span class="n">save_total_limit</span><span class="p">)</span> + <span class="n">checkpoints_to_be_deleted</span> <span class="o">=</span> <span class="n">checkpoints_sorted</span><span class="p">[:</span><span class="n">number_of_checkpoints_to_delete</span><span class="p">]</span> + <span class="k">for</span> <span class="n">checkpoint</span> <span class="ow">in</span> <span class="n">checkpoints_to_be_deleted</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Deleting older checkpoint [</span><span class="si">{</span><span class="n">checkpoint</span><span class="si">}</span><span class="s2">] due to args.save_total_limit&quot;</span><span class="p">)</span> + <span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.evaluate"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluate">[docs]</a> <span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">eval_dataset</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dataset</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">ignore_keys</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">metric_key_prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;eval&quot;</span><span class="p">,</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Run evaluation and returns metrics.</span> +<span class="sd"> The calling script will be responsible for providing a method to compute metrics, as they are task-dependent</span> +<span class="sd"> (pass it to the init `compute_metrics` argument).</span> +<span class="sd"> You can also subclass and override this method to inject custom behavior.</span> +<span class="sd"> Args:</span> +<span class="sd"> eval_dataset (`Dataset`, *optional*):</span> +<span class="sd"> Pass a dataset if you wish to override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns</span> +<span class="sd"> not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__`</span> +<span class="sd"> method.</span> +<span class="sd"> ignore_keys (`Lst[str]`, *optional*):</span> +<span class="sd"> A list of keys in the output of your model (if it is a dictionary) that should be ignored when</span> +<span class="sd"> gathering predictions.</span> +<span class="sd"> metric_key_prefix (`str`, *optional*, defaults to `&quot;eval&quot;`):</span> +<span class="sd"> An optional prefix to be used as the metrics key prefix. For example the metrics &quot;bleu&quot; will be named</span> +<span class="sd"> &quot;eval_bleu&quot; if the prefix is &quot;eval&quot; (default)</span> +<span class="sd"> Returns:</span> +<span class="sd"> A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The</span> +<span class="sd"> dictionary also contains the epoch number which comes from the training state.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># memory metrics - must set up as early as possible</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_memory_tracker</span><span class="o">.</span><span class="n">start</span><span class="p">()</span> + + <span class="n">eval_dataloader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_eval_dataloader</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span> + <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + + <span class="n">eval_loop</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prediction_loop</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">use_legacy_prediction_loop</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_loop</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">eval_loop</span><span class="p">(</span> + <span class="n">eval_dataloader</span><span class="p">,</span> + <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Evaluation&quot;</span><span class="p">,</span> + <span class="c1"># No point gathering the predictions if there are no metrics, otherwise we defer to</span> + <span class="c1"># self.args.prediction_loss_only</span> + <span class="n">prediction_loss_only</span><span class="o">=</span><span class="kc">True</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">ignore_keys</span><span class="o">=</span><span class="n">ignore_keys</span><span class="p">,</span> + <span class="n">metric_key_prefix</span><span class="o">=</span><span class="n">metric_key_prefix</span><span class="p">,</span> + <span class="p">)</span> + + <span class="n">total_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> + <span class="k">if</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_jit_compilation_time&quot;</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span> + <span class="n">start_time</span> <span class="o">+=</span> <span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_jit_compilation_time&quot;</span><span class="p">]</span> + <span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">update</span><span class="p">(</span> + <span class="n">speed_metrics</span><span class="p">(</span> + <span class="n">metric_key_prefix</span><span class="p">,</span> + <span class="n">start_time</span><span class="p">,</span> + <span class="n">num_samples</span><span class="o">=</span><span class="n">output</span><span class="o">.</span><span class="n">num_samples</span><span class="p">,</span> + <span class="n">num_steps</span><span class="o">=</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">num_samples</span> <span class="o">/</span> <span class="n">total_batch_size</span><span class="p">),</span> + <span class="p">)</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">DebugOption</span><span class="o">.</span><span class="n">TPU_METRICS_DEBUG</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">debug</span><span class="p">:</span> + <span class="c1"># tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)</span> + <span class="n">xm</span><span class="o">.</span><span class="n">master_print</span><span class="p">(</span><span class="n">met</span><span class="o">.</span><span class="n">metrics_report</span><span class="p">())</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_evaluate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">,</span> <span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">_memory_tracker</span><span class="o">.</span><span class="n">stop_and_update_metrics</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">metrics</span></div> + +<div class="viewcode-block" id="RaftTrainer.predict"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">test_dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">ignore_keys</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">metric_key_prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;test&quot;</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">PredictionOutput</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Run prediction and returns predictions and potential metrics.</span> +<span class="sd"> Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method</span> +<span class="sd"> will also return metrics, like in `evaluate()`.</span> +<span class="sd"> Args:</span> +<span class="sd"> test_dataset (`Dataset`):</span> +<span class="sd"> Dataset to run the predictions on. If it is an `datasets.Dataset`, columns not accepted by the</span> +<span class="sd"> `model.forward()` method are automatically removed. Has to implement the method `__len__`</span> +<span class="sd"> ignore_keys (`Lst[str]`, *optional*):</span> +<span class="sd"> A list of keys in the output of your model (if it is a dictionary) that should be ignored when</span> +<span class="sd"> gathering predictions.</span> +<span class="sd"> metric_key_prefix (`str`, *optional*, defaults to `&quot;test&quot;`):</span> +<span class="sd"> An optional prefix to be used as the metrics key prefix. For example the metrics &quot;bleu&quot; will be named</span> +<span class="sd"> &quot;test_bleu&quot; if the prefix is &quot;test&quot; (default)</span> +<span class="sd"> &lt;Tip&gt;</span> +<span class="sd"> If your predictions or labels have different sequence length (for instance because you&#39;re doing dynamic padding</span> +<span class="sd"> in a token classification task) the predictions will be padded (on the right) to allow for concatenation into</span> +<span class="sd"> one array. The padding index is -100.</span> +<span class="sd"> &lt;/Tip&gt;</span> +<span class="sd"> Returns: *NamedTuple* A namedtuple with the following keys:</span> +<span class="sd"> - predictions (`np.ndarray`): The predictions on `test_dataset`.</span> +<span class="sd"> - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some).</span> +<span class="sd"> - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained</span> +<span class="sd"> labels).</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># memory metrics - must set up as early as possible</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_memory_tracker</span><span class="o">.</span><span class="n">start</span><span class="p">()</span> + + <span class="n">test_dataloader</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_test_dataloader</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">)</span> + <span class="n">start_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> + + <span class="n">eval_loop</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prediction_loop</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">use_legacy_prediction_loop</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_loop</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">eval_loop</span><span class="p">(</span> + <span class="n">test_dataloader</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Prediction&quot;</span><span class="p">,</span> <span class="n">ignore_keys</span><span class="o">=</span><span class="n">ignore_keys</span><span class="p">,</span> <span class="n">metric_key_prefix</span><span class="o">=</span><span class="n">metric_key_prefix</span> + <span class="p">)</span> + <span class="n">total_batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span> + <span class="k">if</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_jit_compilation_time&quot;</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">:</span> + <span class="n">start_time</span> <span class="o">+=</span> <span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_jit_compilation_time&quot;</span><span class="p">]</span> + <span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="o">.</span><span class="n">update</span><span class="p">(</span> + <span class="n">speed_metrics</span><span class="p">(</span> + <span class="n">metric_key_prefix</span><span class="p">,</span> + <span class="n">start_time</span><span class="p">,</span> + <span class="n">num_samples</span><span class="o">=</span><span class="n">output</span><span class="o">.</span><span class="n">num_samples</span><span class="p">,</span> + <span class="n">num_steps</span><span class="o">=</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">num_samples</span> <span class="o">/</span> <span class="n">total_batch_size</span><span class="p">),</span> + <span class="p">)</span> + <span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">,</span> <span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_memory_tracker</span><span class="o">.</span><span class="n">stop_and_update_metrics</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">PredictionOutput</span><span class="p">(</span><span class="n">predictions</span><span class="o">=</span><span class="n">output</span><span class="o">.</span><span class="n">predictions</span><span class="p">,</span> <span class="n">label_ids</span><span class="o">=</span><span class="n">output</span><span class="o">.</span><span class="n">label_ids</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="n">output</span><span class="o">.</span><span class="n">metrics</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.evaluation_loop"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluation_loop">[docs]</a> <span class="k">def</span> <span class="nf">evaluation_loop</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">dataloader</span><span class="p">:</span> <span class="n">DataLoader</span><span class="p">,</span> + <span class="n">description</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> + <span class="n">prediction_loss_only</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">ignore_keys</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">metric_key_prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;eval&quot;</span><span class="p">,</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">EvalLoopOutput</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.</span> +<span class="sd"> Works both with or without labels.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span> + + <span class="n">prediction_loss_only</span> <span class="o">=</span> <span class="n">prediction_loss_only</span> <span class="k">if</span> <span class="n">prediction_loss_only</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">args</span><span class="o">.</span><span class="n">prediction_loss_only</span> + + <span class="c1"># if eval is called w/o train init deepspeed here</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># XXX: eval doesn&#39;t have `resume_from_checkpoint` arg but we should be able to do eval</span> + <span class="c1"># from the checkpoint eventually</span> + <span class="n">deepspeed_engine</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">deepspeed_init</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">num_training_steps</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">inference</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">deepspeed_engine</span><span class="o">.</span><span class="n">module</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="o">=</span> <span class="n">deepspeed_engine</span> + <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span> <span class="o">=</span> <span class="n">deepspeed_engine</span> + + <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_wrap_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader</span><span class="o">=</span><span class="n">dataloader</span><span class="p">)</span> + + <span class="c1"># if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn&#39;t called</span> + <span class="c1"># while ``train`` is running, cast it to the right dtype first and then put on device</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_in_train</span><span class="p">:</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">fp16_full_eval</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">bf16_full_eval</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="n">batch_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span> + + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;***** Running </span><span class="si">{</span><span class="n">description</span><span class="si">}</span><span class="s2"> *****&quot;</span><span class="p">)</span> + <span class="k">if</span> <span class="n">has_length</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Num examples = </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">num_examples</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot; Num examples: Unknown&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Batch size = </span><span class="si">{</span><span class="n">batch_size</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">eval_dataloader</span> <span class="o">=</span> <span class="n">dataloader</span> + <span class="c1"># Do this before wrapping.</span> + <span class="n">eval_dataset</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">dataloader</span><span class="p">,</span> <span class="s2">&quot;dataset&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">dataloader</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">ParallelLoader</span><span class="p">(</span><span class="n">dataloader</span><span class="p">,</span> <span class="p">[</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">])</span><span class="o">.</span><span class="n">per_device_loader</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_past</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="c1"># Initialize containers</span> + <span class="c1"># losses/preds/labels on GPU/TPU (accumulated for eval_accumulation_steps)</span> + <span class="n">losses_host</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">preds_host</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">labels_host</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">inputs_host</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="c1"># losses/preds/labels on CPU (final containers)</span> + <span class="n">all_losses</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">all_preds</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">all_labels</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">all_inputs</span> <span class="o">=</span> <span class="kc">None</span> + <span class="c1"># Will be useful when we have an iterable dataset so don&#39;t know its length.</span> + + <span class="n">observed_num_examples</span> <span class="o">=</span> <span class="mi">0</span> + <span class="c1"># Main evaluation loop</span> + <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span> + <span class="c1"># Update the observed num examples</span> + <span class="n">observed_batch_size</span> <span class="o">=</span> <span class="n">find_batch_size</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> + <span class="k">if</span> <span class="n">observed_batch_size</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">observed_num_examples</span> <span class="o">+=</span> <span class="n">observed_batch_size</span> + <span class="c1"># For batch samplers, batch_size is not known by the dataloader in advance.</span> + <span class="k">if</span> <span class="n">batch_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">batch_size</span> <span class="o">=</span> <span class="n">observed_batch_size</span> + + <span class="c1"># Prediction step</span> + <span class="n">loss</span><span class="p">,</span> <span class="n">logits</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prediction_step</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">prediction_loss_only</span><span class="p">,</span> <span class="n">ignore_keys</span><span class="o">=</span><span class="n">ignore_keys</span><span class="p">)</span> + <span class="n">inputs_decode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_input</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">])</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">include_inputs_for_metrics</span> <span class="k">else</span> <span class="kc">None</span> + + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">xm</span><span class="o">.</span><span class="n">mark_step</span><span class="p">()</span> + + <span class="c1"># Update containers on host</span> + <span class="k">if</span> <span class="n">loss</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">losses</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nested_gather</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">batch_size</span><span class="p">))</span> + <span class="n">losses_host</span> <span class="o">=</span> <span class="n">losses</span> <span class="k">if</span> <span class="n">losses_host</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">losses_host</span><span class="p">,</span> <span class="n">losses</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> + <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_across_processes</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span> + <span class="n">labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nested_gather</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span> + <span class="n">labels_host</span> <span class="o">=</span> <span class="n">labels</span> <span class="k">if</span> <span class="n">labels_host</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">labels_host</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="k">if</span> <span class="n">inputs_decode</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">inputs_decode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_across_processes</span><span class="p">(</span><span class="n">inputs_decode</span><span class="p">)</span> + <span class="n">inputs_decode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nested_gather</span><span class="p">(</span><span class="n">inputs_decode</span><span class="p">)</span> + <span class="n">inputs_host</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">inputs_decode</span> + <span class="k">if</span> <span class="n">inputs_host</span> <span class="ow">is</span> <span class="kc">None</span> + <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">inputs_host</span><span class="p">,</span> <span class="n">inputs_decode</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">logits</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_across_processes</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span> + <span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nested_gather</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocess_logits_for_metrics</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocess_logits_for_metrics</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span> + <span class="n">preds_host</span> <span class="o">=</span> <span class="n">logits</span> <span class="k">if</span> <span class="n">preds_host</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">preds_host</span><span class="p">,</span> <span class="n">logits</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_prediction_step</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + + <span class="c1"># Gather all tensors and put them back on the CPU if we have done enough accumulation steps.</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">eval_accumulation_steps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="n">step</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">args</span><span class="o">.</span><span class="n">eval_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="k">if</span> <span class="n">losses_host</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">losses</span> <span class="o">=</span> <span class="n">nested_numpify</span><span class="p">(</span><span class="n">losses_host</span><span class="p">)</span> + <span class="n">all_losses</span> <span class="o">=</span> <span class="n">losses</span> <span class="k">if</span> <span class="n">all_losses</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">all_losses</span><span class="p">,</span> <span class="n">losses</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> + <span class="k">if</span> <span class="n">preds_host</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logits</span> <span class="o">=</span> <span class="n">nested_numpify</span><span class="p">(</span><span class="n">preds_host</span><span class="p">)</span> + <span class="n">all_preds</span> <span class="o">=</span> <span class="n">logits</span> <span class="k">if</span> <span class="n">all_preds</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">all_preds</span><span class="p">,</span> <span class="n">logits</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="k">if</span> <span class="n">inputs_host</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">inputs_decode</span> <span class="o">=</span> <span class="n">nested_numpify</span><span class="p">(</span><span class="n">inputs_host</span><span class="p">)</span> + <span class="n">all_inputs</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">inputs_decode</span> + <span class="k">if</span> <span class="n">all_inputs</span> <span class="ow">is</span> <span class="kc">None</span> + <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">all_inputs</span><span class="p">,</span> <span class="n">inputs_decode</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">labels_host</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="n">nested_numpify</span><span class="p">(</span><span class="n">labels_host</span><span class="p">)</span> + <span class="n">all_labels</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">labels</span> <span class="k">if</span> <span class="n">all_labels</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">all_labels</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="p">)</span> + + <span class="c1"># Set back to None to begin a new accumulation</span> + <span class="n">losses_host</span><span class="p">,</span> <span class="n">preds_host</span><span class="p">,</span> <span class="n">inputs_host</span><span class="p">,</span> <span class="n">labels_host</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span> + + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_past&quot;</span><span class="p">):</span> + <span class="c1"># Clean the state at the end of the evaluation loop</span> + <span class="nb">delattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_past&quot;</span><span class="p">)</span> + + <span class="c1"># Gather all remaining tensors and put them back on the CPU</span> + <span class="k">if</span> <span class="n">losses_host</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">losses</span> <span class="o">=</span> <span class="n">nested_numpify</span><span class="p">(</span><span class="n">losses_host</span><span class="p">)</span> + <span class="n">all_losses</span> <span class="o">=</span> <span class="n">losses</span> <span class="k">if</span> <span class="n">all_losses</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">all_losses</span><span class="p">,</span> <span class="n">losses</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> + <span class="k">if</span> <span class="n">preds_host</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">logits</span> <span class="o">=</span> <span class="n">nested_numpify</span><span class="p">(</span><span class="n">preds_host</span><span class="p">)</span> + <span class="n">all_preds</span> <span class="o">=</span> <span class="n">logits</span> <span class="k">if</span> <span class="n">all_preds</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">all_preds</span><span class="p">,</span> <span class="n">logits</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="k">if</span> <span class="n">inputs_host</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">inputs_decode</span> <span class="o">=</span> <span class="n">nested_numpify</span><span class="p">(</span><span class="n">inputs_host</span><span class="p">)</span> + <span class="n">all_inputs</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">inputs_decode</span> <span class="k">if</span> <span class="n">all_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">all_inputs</span><span class="p">,</span> <span class="n">inputs_decode</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="p">)</span> + <span class="k">if</span> <span class="n">labels_host</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="n">nested_numpify</span><span class="p">(</span><span class="n">labels_host</span><span class="p">)</span> + <span class="n">all_labels</span> <span class="o">=</span> <span class="n">labels</span> <span class="k">if</span> <span class="n">all_labels</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">all_labels</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + + <span class="c1"># Number of samples</span> + <span class="k">if</span> <span class="n">has_length</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">):</span> + <span class="n">num_samples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span> + <span class="c1"># The instance check is weird and does not actually check for the type, but whether the dataset has the right</span> + <span class="c1"># methods. Therefore we need to make sure it also has the attribute.</span> + <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">,</span> <span class="n">IterableDatasetShard</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">,</span> <span class="s2">&quot;num_examples&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">num_samples</span> <span class="o">=</span> <span class="n">eval_dataset</span><span class="o">.</span><span class="n">num_examples</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">has_length</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span> + <span class="n">num_samples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_examples</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> <span class="c1"># both len(dataloader.dataset) and len(dataloader) fail</span> + <span class="n">num_samples</span> <span class="o">=</span> <span class="n">observed_num_examples</span> + <span class="k">if</span> <span class="n">num_samples</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">observed_num_examples</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">num_samples</span> <span class="o">=</span> <span class="n">observed_num_examples</span> + + <span class="c1"># Number of losses has been rounded to a multiple of batch_size and in a distributed training, the number of</span> + <span class="c1"># samplers has been rounded to a multiple of batch_size, so we truncate.</span> + <span class="k">if</span> <span class="n">all_losses</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">all_losses</span> <span class="o">=</span> <span class="n">all_losses</span><span class="p">[:</span><span class="n">num_samples</span><span class="p">]</span> + <span class="k">if</span> <span class="n">all_preds</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">all_preds</span> <span class="o">=</span> <span class="n">nested_truncate</span><span class="p">(</span><span class="n">all_preds</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span> + <span class="k">if</span> <span class="n">all_labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">all_labels</span> <span class="o">=</span> <span class="n">nested_truncate</span><span class="p">(</span><span class="n">all_labels</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span> + <span class="k">if</span> <span class="n">all_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">all_inputs</span> <span class="o">=</span> <span class="n">nested_truncate</span><span class="p">(</span><span class="n">all_inputs</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">)</span> + + <span class="c1"># Metrics!</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">all_preds</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">all_labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">include_inputs_for_metrics</span><span class="p">:</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span><span class="p">(</span> + <span class="n">EvalPrediction</span><span class="p">(</span><span class="n">predictions</span><span class="o">=</span><span class="n">all_preds</span><span class="p">,</span> <span class="n">label_ids</span><span class="o">=</span><span class="n">all_labels</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">all_inputs</span><span class="p">)</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span><span class="p">(</span><span class="n">EvalPrediction</span><span class="p">(</span><span class="n">predictions</span><span class="o">=</span><span class="n">all_preds</span><span class="p">,</span> <span class="n">label_ids</span><span class="o">=</span><span class="n">all_labels</span><span class="p">))</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="p">{}</span> + + <span class="c1"># To be JSON-serializable, we need to remove numpy types or zero-d tensors</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="n">denumpify_detensorize</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">all_losses</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_loss&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_losses</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> + <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;jit_compilation_time&quot;</span><span class="p">):</span> + <span class="n">metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_jit_compilation_time&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">jit_compilation_time</span> + + <span class="c1"># Prefix all keys with metric_key_prefix + &#39;_&#39;</span> + <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">):</span> + <span class="n">metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">EvalLoopOutput</span><span class="p">(</span><span class="n">predictions</span><span class="o">=</span><span class="n">all_preds</span><span class="p">,</span> <span class="n">label_ids</span><span class="o">=</span><span class="n">all_labels</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="n">metrics</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="n">num_samples</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._nested_gather"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._nested_gather">[docs]</a> <span class="k">def</span> <span class="nf">_nested_gather</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before</span> +<span class="sd"> concatenating them to `gathered`</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="n">tensors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">return</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;nested_gather&quot;</span> + <span class="n">tensors</span> <span class="o">=</span> <span class="n">nested_xla_mesh_reduce</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="n">tensors</span> <span class="o">=</span> <span class="n">smp_gather</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="n">tensors</span> <span class="o">=</span> <span class="n">distributed_concat</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span> + <span class="k">return</span> <span class="n">tensors</span></div> + + <span class="c1"># Copied from Accelerate.</span> +<div class="viewcode-block" id="RaftTrainer._pad_across_processes"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._pad_across_processes">[docs]</a> <span class="k">def</span> <span class="nf">_pad_across_processes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tensor</span><span class="p">,</span> <span class="n">pad_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so</span> +<span class="sd"> they can safely be gathered.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">)):</span> + <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">tensor</span><span class="p">)(</span><span class="bp">self</span><span class="o">.</span><span class="n">_pad_across_processes</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">pad_index</span><span class="o">=</span><span class="n">pad_index</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensor</span><span class="p">)</span> + <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="k">return</span> <span class="nb">type</span><span class="p">(</span><span class="n">tensor</span><span class="p">)({</span><span class="n">k</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_pad_across_processes</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">pad_index</span><span class="o">=</span><span class="n">pad_index</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">tensor</span><span class="o">.</span><span class="n">items</span><span class="p">()})</span> + <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span> + <span class="sa">f</span><span class="s2">&quot;Can&#39;t pad the values of type </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span><span class="si">}</span><span class="s2">, only of nested list/tuple/dicts of tensors.&quot;</span> + <span class="p">)</span> + + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span> + <span class="k">return</span> <span class="n">tensor</span> + <span class="c1"># Gather all sizes</span> + <span class="n">size</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">tensor</span><span class="o">.</span><span class="n">device</span><span class="p">)[</span><span class="kc">None</span><span class="p">]</span> + <span class="n">sizes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_nested_gather</span><span class="p">(</span><span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> + + <span class="n">max_size</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">sizes</span><span class="p">)</span> + <span class="c1"># When extracting XLA graphs for compilation, max_size is 0,</span> + <span class="c1"># so use inequality to avoid errors.</span> + <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">max_size</span><span class="p">:</span> + <span class="k">return</span> <span class="n">tensor</span> + + <span class="c1"># Then pad to the maximum size</span> + <span class="n">old_size</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">shape</span> + <span class="n">new_size</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">old_size</span><span class="p">)</span> + <span class="n">new_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">max_size</span> + <span class="n">new_tensor</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">new_zeros</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">new_size</span><span class="p">))</span> <span class="o">+</span> <span class="n">pad_index</span> + <span class="n">new_tensor</span><span class="p">[:,</span> <span class="p">:</span> <span class="n">old_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="n">tensor</span> + <span class="k">return</span> <span class="n">new_tensor</span></div> + +<div class="viewcode-block" id="RaftTrainer.prediction_step"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_step">[docs]</a> <span class="k">def</span> <span class="nf">prediction_step</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">model</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> + <span class="n">inputs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Any</span><span class="p">]],</span> + <span class="n">prediction_loss_only</span><span class="p">:</span> <span class="nb">bool</span><span class="p">,</span> + <span class="n">ignore_keys</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Perform an evaluation step on `model` using `inputs`.</span> +<span class="sd"> Subclass and override to inject custom behavior.</span> +<span class="sd"> Args:</span> +<span class="sd"> model (`nn.Module`):</span> +<span class="sd"> The model to evaluate.</span> +<span class="sd"> inputs (`Dict[str, Union[torch.Tensor, Any]]`):</span> +<span class="sd"> The inputs and targets of the model.</span> +<span class="sd"> The dictionary will be unpacked before being fed to the model. Most models expect the targets under the</span> +<span class="sd"> argument `labels`. Check your model&#39;s documentation for all accepted arguments.</span> +<span class="sd"> prediction_loss_only (`bool`):</span> +<span class="sd"> Whether or not to return the loss only.</span> +<span class="sd"> ignore_keys (`Lst[str]`, *optional*):</span> +<span class="sd"> A list of keys in the output of your model (if it is a dictionary) that should be ignored when</span> +<span class="sd"> gathering predictions.</span> +<span class="sd"> Return:</span> +<span class="sd"> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss,</span> +<span class="sd"> logits and labels (each being optional).</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">has_labels</span> <span class="o">=</span> <span class="kc">False</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="nb">all</span><span class="p">(</span><span class="n">inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">)</span> + <span class="c1"># For CLIP-like models capable of returning loss values.</span> + <span class="c1"># If `return_loss` is not specified or being `None` in `inputs`, we check if the default value of `return_loss`</span> + <span class="c1"># is `True` in `model.forward`.</span> + <span class="n">return_loss</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;return_loss&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> + <span class="k">if</span> <span class="n">return_loss</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">return_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">can_return_loss</span> + <span class="n">loss_without_labels</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">return_loss</span> <span class="k">else</span> <span class="kc">False</span> + + <span class="n">inputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_inputs</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> + <span class="k">if</span> <span class="n">ignore_keys</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;config&quot;</span><span class="p">):</span> + <span class="n">ignore_keys</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">config</span><span class="p">,</span> <span class="s2">&quot;keys_to_ignore_at_inference&quot;</span><span class="p">,</span> <span class="p">[])</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">ignore_keys</span> <span class="o">=</span> <span class="p">[]</span> + + <span class="c1"># labels may be popped when computing the loss (label smoothing for instance) so we grab them first.</span> + <span class="k">if</span> <span class="n">has_labels</span> <span class="ow">or</span> <span class="n">loss_without_labels</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="n">nested_detach</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">inputs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_names</span><span class="p">))</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="n">labels</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">labels</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span> + <span class="k">if</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="n">raw_outputs</span> <span class="o">=</span> <span class="n">smp_forward_only</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> + <span class="k">if</span> <span class="n">has_labels</span> <span class="ow">or</span> <span class="n">loss_without_labels</span><span class="p">:</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">raw_outputs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">loss_mb</span> <span class="o">=</span> <span class="n">raw_outputs</span><span class="p">[</span><span class="s2">&quot;loss&quot;</span><span class="p">]</span> + <span class="n">logits_mb</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">raw_outputs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ignore_keys</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;loss&quot;</span><span class="p">])</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">loss_mb</span> <span class="o">=</span> <span class="n">raw_outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + <span class="n">logits_mb</span> <span class="o">=</span> <span class="n">raw_outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> + + <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_mb</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span> + <span class="n">logits</span> <span class="o">=</span> <span class="n">smp_nested_concat</span><span class="p">(</span><span class="n">logits_mb</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">loss</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">raw_outputs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">logits_mb</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">raw_outputs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ignore_keys</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logits_mb</span> <span class="o">=</span> <span class="n">raw_outputs</span> + <span class="n">logits</span> <span class="o">=</span> <span class="n">smp_nested_concat</span><span class="p">(</span><span class="n">logits_mb</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">has_labels</span> <span class="ow">or</span> <span class="n">loss_without_labels</span><span class="p">:</span> + <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_loss_context_manager</span><span class="p">():</span> + <span class="n">loss</span><span class="p">,</span> <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_loss</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">return_outputs</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span> + + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">logits</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">outputs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ignore_keys</span> <span class="o">+</span> <span class="p">[</span><span class="s2">&quot;loss&quot;</span><span class="p">])</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logits</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">loss</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_loss_context_manager</span><span class="p">():</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span> + <span class="n">logits</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">outputs</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">ignore_keys</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">logits</span> <span class="o">=</span> <span class="n">outputs</span> + <span class="c1"># TODO: this needs to be fixed and made cleaner later.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_past</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> + + <span class="k">if</span> <span class="n">prediction_loss_only</span><span class="p">:</span> + <span class="k">return</span> <span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> + + <span class="n">logits</span> <span class="o">=</span> <span class="n">nested_detach</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">logits</span> <span class="o">=</span> <span class="n">logits</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + + <span class="k">return</span> <span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.floating_point_ops"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.floating_point_ops">[docs]</a> <span class="k">def</span> <span class="nf">floating_point_ops</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point</span> +<span class="sd"> operations for every backward + forward pass. If using another model, either implement such a method in the</span> +<span class="sd"> model or subclass and override this method.</span> +<span class="sd"> Args:</span> +<span class="sd"> inputs (`Dict[str, Union[torch.Tensor, Any]]`):</span> +<span class="sd"> The inputs and targets of the model.</span> +<span class="sd"> Returns:</span> +<span class="sd"> `int`: The number of floating-point operations.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;floating_point_ops&quot;</span><span class="p">):</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">floating_point_ops</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">return</span> <span class="mi">0</span></div> + +<div class="viewcode-block" id="RaftTrainer.init_git_repo"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.init_git_repo">[docs]</a> <span class="k">def</span> <span class="nf">init_git_repo</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">at_init</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Initializes a git repo in `self.args.hub_model_id`.</span> +<span class="sd"> Args:</span> +<span class="sd"> at_init (`bool`, *optional*, defaults to `False`):</span> +<span class="sd"> Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is</span> +<span class="sd"> `True` and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped</span> +<span class="sd"> out.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_world_process_zero</span><span class="p">():</span> + <span class="k">return</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_model_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">repo_name</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">)</span><span class="o">.</span><span class="n">absolute</span><span class="p">()</span><span class="o">.</span><span class="n">name</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">repo_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_model_id</span> + <span class="k">if</span> <span class="s2">&quot;/&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">repo_name</span><span class="p">:</span> + <span class="n">repo_name</span> <span class="o">=</span> <span class="n">get_full_repo_name</span><span class="p">(</span><span class="n">repo_name</span><span class="p">,</span> <span class="n">token</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_token</span><span class="p">)</span> + + <span class="c1"># Make sure the repo exists.</span> + <span class="n">create_repo</span><span class="p">(</span><span class="n">repo_name</span><span class="p">,</span> <span class="n">token</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_token</span><span class="p">,</span> <span class="n">private</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_private_repo</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="k">try</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">repo</span> <span class="o">=</span> <span class="n">Repository</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">clone_from</span><span class="o">=</span><span class="n">repo_name</span><span class="p">,</span> <span class="n">token</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_token</span><span class="p">)</span> + <span class="k">except</span> <span class="ne">EnvironmentError</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">overwrite_output_dir</span> <span class="ow">and</span> <span class="n">at_init</span><span class="p">:</span> + <span class="c1"># Try again after wiping output_dir</span> + <span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">repo</span> <span class="o">=</span> <span class="n">Repository</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">clone_from</span><span class="o">=</span><span class="n">repo_name</span><span class="p">,</span> <span class="n">token</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_token</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">git_pull</span><span class="p">()</span> + + <span class="c1"># By default, ignore the checkpoint folders</span> + <span class="k">if</span> <span class="p">(</span> + <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="s2">&quot;.gitignore&quot;</span><span class="p">))</span> + <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_strategy</span> <span class="o">!=</span> <span class="n">HubStrategy</span><span class="o">.</span><span class="n">ALL_CHECKPOINTS</span> + <span class="p">):</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="s2">&quot;.gitignore&quot;</span><span class="p">),</span> <span class="s2">&quot;w&quot;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">writer</span><span class="p">:</span> + <span class="n">writer</span><span class="o">.</span><span class="n">writelines</span><span class="p">([</span><span class="s2">&quot;checkpoint-*/&quot;</span><span class="p">])</span> + + <span class="c1"># Add &quot;*.sagemaker&quot; to .gitignore if using SageMaker</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;SM_TRAINING_ENV&quot;</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_add_sm_patterns_to_gitignore</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">push_in_progress</span> <span class="o">=</span> <span class="kc">None</span></div> + +<div class="viewcode-block" id="RaftTrainer.create_model_card"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_model_card">[docs]</a> <span class="k">def</span> <span class="nf">create_model_card</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">language</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">license</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">tags</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">model_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">finetuned_from</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">tasks</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">dataset_tags</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">dataset</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">dataset_args</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Creates a draft of a model card using the information available to the `Trainer`.</span> +<span class="sd"> Args:</span> +<span class="sd"> language (`str`, *optional*):</span> +<span class="sd"> The language of the model (if applicable)</span> +<span class="sd"> license (`str`, *optional*):</span> +<span class="sd"> The license of the model. Will default to the license of the pretrained model used, if the original</span> +<span class="sd"> model given to the `Trainer` comes from a repo on the Hub.</span> +<span class="sd"> tags (`str` or `List[str]`, *optional*):</span> +<span class="sd"> Some tags to be included in the metadata of the model card.</span> +<span class="sd"> model_name (`str`, *optional*):</span> +<span class="sd"> The name of the model.</span> +<span class="sd"> finetuned_from (`str`, *optional*):</span> +<span class="sd"> The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo</span> +<span class="sd"> of the original model given to the `Trainer` (if it comes from the Hub).</span> +<span class="sd"> tasks (`str` or `List[str]`, *optional*):</span> +<span class="sd"> One or several task identifiers, to be included in the metadata of the model card.</span> +<span class="sd"> dataset_tags (`str` or `List[str]`, *optional*):</span> +<span class="sd"> One or several dataset tags, to be included in the metadata of the model card.</span> +<span class="sd"> dataset (`str` or `List[str]`, *optional*):</span> +<span class="sd"> One or several dataset identifiers, to be included in the metadata of the model card.</span> +<span class="sd"> dataset_args (`str` or `List[str]`, *optional*):</span> +<span class="sd"> One or several dataset arguments, to be included in the metadata of the model card.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_world_process_zero</span><span class="p">():</span> + <span class="k">return</span> + + <span class="n">training_summary</span> <span class="o">=</span> <span class="n">TrainingSummary</span><span class="o">.</span><span class="n">from_trainer</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">language</span><span class="o">=</span><span class="n">language</span><span class="p">,</span> + <span class="n">license</span><span class="o">=</span><span class="n">license</span><span class="p">,</span> + <span class="n">tags</span><span class="o">=</span><span class="n">tags</span><span class="p">,</span> + <span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span> + <span class="n">finetuned_from</span><span class="o">=</span><span class="n">finetuned_from</span><span class="p">,</span> + <span class="n">tasks</span><span class="o">=</span><span class="n">tasks</span><span class="p">,</span> + <span class="n">dataset_tags</span><span class="o">=</span><span class="n">dataset_tags</span><span class="p">,</span> + <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span> + <span class="n">dataset_args</span><span class="o">=</span><span class="n">dataset_args</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">model_card</span> <span class="o">=</span> <span class="n">training_summary</span><span class="o">.</span><span class="n">to_model_card</span><span class="p">()</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="s2">&quot;README.md&quot;</span><span class="p">),</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> + <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">model_card</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._push_from_checkpoint"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._push_from_checkpoint">[docs]</a> <span class="k">def</span> <span class="nf">_push_from_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">checkpoint_folder</span><span class="p">):</span> + <span class="c1"># Only push from one node.</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_world_process_zero</span><span class="p">()</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_strategy</span> <span class="o">==</span> <span class="n">HubStrategy</span><span class="o">.</span><span class="n">END</span><span class="p">:</span> + <span class="k">return</span> + <span class="c1"># If we haven&#39;t finished the last push, we don&#39;t do this one.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">push_in_progress</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">push_in_progress</span><span class="o">.</span><span class="n">is_done</span><span class="p">:</span> + <span class="k">return</span> + + <span class="n">output_dir</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span> + <span class="c1"># To avoid a new synchronization of all model weights, we just copy the file from the checkpoint folder</span> + <span class="n">modeling_files</span> <span class="o">=</span> <span class="p">[</span><span class="n">CONFIG_NAME</span><span class="p">,</span> <span class="n">WEIGHTS_NAME</span><span class="p">]</span> + <span class="k">for</span> <span class="n">modeling_file</span> <span class="ow">in</span> <span class="n">modeling_files</span><span class="p">:</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint_folder</span><span class="p">,</span> <span class="n">modeling_file</span><span class="p">)):</span> + <span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpoint_folder</span><span class="p">,</span> <span class="n">modeling_file</span><span class="p">),</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">modeling_file</span><span class="p">))</span> + <span class="c1"># Saving the tokenizer is fast and we don&#39;t know how many files it may have spawned, so we resave it to be sure.</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">save_pretrained</span><span class="p">(</span><span class="n">output_dir</span><span class="p">)</span> + <span class="c1"># Same for the training arguments</span> + <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">TRAINING_ARGS_NAME</span><span class="p">))</span> + + <span class="k">try</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_strategy</span> <span class="o">==</span> <span class="n">HubStrategy</span><span class="o">.</span><span class="n">CHECKPOINT</span><span class="p">:</span> + <span class="c1"># Temporarily move the checkpoint just saved for the push</span> + <span class="n">tmp_checkpoint</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">output_dir</span><span class="p">,</span> <span class="s2">&quot;last-checkpoint&quot;</span><span class="p">)</span> + <span class="c1"># We have to remove the &quot;last-checkpoint&quot; dir if it exists, otherwise the checkpoint is moved as a</span> + <span class="c1"># subfolder.</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">tmp_checkpoint</span><span class="p">):</span> + <span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="n">tmp_checkpoint</span><span class="p">)</span> + <span class="n">shutil</span><span class="o">.</span><span class="n">move</span><span class="p">(</span><span class="n">checkpoint_folder</span><span class="p">,</span> <span class="n">tmp_checkpoint</span><span class="p">)</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">save_strategy</span> <span class="o">==</span> <span class="n">IntervalStrategy</span><span class="o">.</span><span class="n">STEPS</span><span class="p">:</span> + <span class="n">commit_message</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Training in progress, step </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">global_step</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">commit_message</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Training in progress, epoch </span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">epoch</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span> + <span class="n">_</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">push_in_progress</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">push_to_hub</span><span class="p">(</span> + <span class="n">commit_message</span><span class="o">=</span><span class="n">commit_message</span><span class="p">,</span> <span class="n">blocking</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">auto_lfs_prune</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span> + <span class="k">finally</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_strategy</span> <span class="o">==</span> <span class="n">HubStrategy</span><span class="o">.</span><span class="n">CHECKPOINT</span><span class="p">:</span> + <span class="c1"># Move back the checkpoint to its place</span> + <span class="n">shutil</span><span class="o">.</span><span class="n">move</span><span class="p">(</span><span class="n">tmp_checkpoint</span><span class="p">,</span> <span class="n">checkpoint_folder</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer.push_to_hub"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.push_to_hub">[docs]</a> <span class="k">def</span> <span class="nf">push_to_hub</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">commit_message</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;End of training&quot;</span><span class="p">,</span> <span class="n">blocking</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*.</span> +<span class="sd"> Parameters:</span> +<span class="sd"> commit_message (`str`, *optional*, defaults to `&quot;End of training&quot;`):</span> +<span class="sd"> Message to commit while pushing.</span> +<span class="sd"> blocking (`bool`, *optional*, defaults to `True`):</span> +<span class="sd"> Whether the function should return only when the `git push` has finished.</span> +<span class="sd"> kwargs:</span> +<span class="sd"> Additional keyword arguments passed along to [`~Trainer.create_model_card`].</span> +<span class="sd"> Returns:</span> +<span class="sd"> The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of</span> +<span class="sd"> the commit and an object to track the progress of the commit if `blocking=True`</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># If a user calls manually `push_to_hub` with `self.args.push_to_hub = False`, we try to create the repo but</span> + <span class="c1"># it might fail.</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;repo&quot;</span><span class="p">):</span> + <span class="bp">self</span><span class="o">.</span><span class="n">init_git_repo</span><span class="p">()</span> + + <span class="n">model_name</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;model_name&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> + <span class="k">if</span> <span class="n">model_name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_model_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">model_name</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">)</span><span class="o">.</span><span class="n">name</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">model_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">hub_model_id</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> + + <span class="c1"># Needs to be executed on all processes for TPU training, but will only save on the processed determined by</span> + <span class="c1"># self.args.should_save.</span> + <span class="bp">self</span><span class="o">.</span><span class="n">save_model</span><span class="p">(</span><span class="n">_internal_call</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + + <span class="c1"># Only push from one node.</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_world_process_zero</span><span class="p">():</span> + <span class="k">return</span> + + <span class="c1"># Cancel any async push in progress if blocking=True. The commits will all be pushed together.</span> + <span class="k">if</span> <span class="n">blocking</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">push_in_progress</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">push_in_progress</span><span class="o">.</span><span class="n">is_done</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">push_in_progress</span><span class="o">.</span><span class="n">_process</span><span class="o">.</span><span class="n">kill</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">push_in_progress</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="n">git_head_commit_url</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">push_to_hub</span><span class="p">(</span> + <span class="n">commit_message</span><span class="o">=</span><span class="n">commit_message</span><span class="p">,</span> <span class="n">blocking</span><span class="o">=</span><span class="n">blocking</span><span class="p">,</span> <span class="n">auto_lfs_prune</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span> + <span class="c1"># push separately the model card to be independant from the rest of the model</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">should_save</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">create_model_card</span><span class="p">(</span><span class="n">model_name</span><span class="o">=</span><span class="n">model_name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> + <span class="k">try</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">push_to_hub</span><span class="p">(</span> + <span class="n">commit_message</span><span class="o">=</span><span class="s2">&quot;update model card README.md&quot;</span><span class="p">,</span> <span class="n">blocking</span><span class="o">=</span><span class="n">blocking</span><span class="p">,</span> <span class="n">auto_lfs_prune</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span> + <span class="k">except</span> <span class="ne">EnvironmentError</span> <span class="k">as</span> <span class="n">exc</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error pushing update to the model card. Please read logs and retry.</span><span class="se">\n</span><span class="s2">$</span><span class="si">{</span><span class="n">exc</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">git_head_commit_url</span></div> + + <span class="c1">#</span> + <span class="c1"># Deprecated code</span> + <span class="c1">#</span> + +<div class="viewcode-block" id="RaftTrainer.prediction_loop"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_loop">[docs]</a> <span class="k">def</span> <span class="nf">prediction_loop</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">dataloader</span><span class="p">:</span> <span class="n">DataLoader</span><span class="p">,</span> + <span class="n">description</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> + <span class="n">prediction_loss_only</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">ignore_keys</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">metric_key_prefix</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;eval&quot;</span><span class="p">,</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">EvalLoopOutput</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.</span> +<span class="sd"> Works both with or without labels.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="n">has_length</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dataloader must implement a working __len__&quot;</span><span class="p">)</span> + + <span class="n">prediction_loss_only</span> <span class="o">=</span> <span class="n">prediction_loss_only</span> <span class="k">if</span> <span class="n">prediction_loss_only</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">args</span><span class="o">.</span><span class="n">prediction_loss_only</span> + + <span class="c1"># if eval is called w/o train init deepspeed here</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">deepspeed</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span><span class="p">:</span> + <span class="c1"># XXX: eval doesn&#39;t have `resume_from_checkpoint` arg but we should be able to do eval</span> + <span class="c1"># from the checkpoint eventually</span> + <span class="n">deepspeed_engine</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">deepspeed_init</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_training_steps</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">resume_from_checkpoint</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">deepspeed_engine</span><span class="o">.</span><span class="n">module</span> + <span class="bp">self</span><span class="o">.</span><span class="n">model_wrapped</span> <span class="o">=</span> <span class="n">deepspeed_engine</span> + <span class="bp">self</span><span class="o">.</span><span class="n">deepspeed</span> <span class="o">=</span> <span class="n">deepspeed_engine</span> + <span class="c1"># XXX: we don&#39;t need optim/sched for inference, but this needs to be sorted out, since</span> + <span class="c1"># for example the Z3-optimizer is a must for zero3 to work even for inference - what we</span> + <span class="c1"># don&#39;t need is the deepspeed basic optimizer which is self.optimizer.optimizer</span> + <span class="n">deepspeed_engine</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">deepspeed_engine</span><span class="o">.</span><span class="n">lr_scheduler</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_wrap_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dataloader</span><span class="o">=</span><span class="n">dataloader</span><span class="p">)</span> + + <span class="c1"># if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn&#39;t called</span> + <span class="c1"># while ``train`` is running, cast it to the right dtype first and then put on device</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_in_train</span><span class="p">:</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">fp16_full_eval</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">args</span><span class="o">.</span><span class="n">bf16_full_eval</span><span class="p">:</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="n">batch_size</span> <span class="o">=</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">batch_size</span> + <span class="n">num_examples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_examples</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;***** Running </span><span class="si">{</span><span class="n">description</span><span class="si">}</span><span class="s2"> *****&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Num examples = </span><span class="si">{</span><span class="n">num_examples</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot; Batch size = </span><span class="si">{</span><span class="n">batch_size</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">losses_host</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">preds_host</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">labels_host</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">inputs_host</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="n">world_size</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">)</span> + + <span class="n">eval_losses_gatherer</span> <span class="o">=</span> <span class="n">DistributedTensorGatherer</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">make_multiple_of</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">prediction_loss_only</span><span class="p">:</span> + <span class="c1"># The actual number of eval_sample can be greater than num_examples in distributed settings (when we pass</span> + <span class="c1"># a batch size to the sampler)</span> + <span class="n">make_multiple_of</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">dataloader</span><span class="p">,</span> <span class="s2">&quot;sampler&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataloader</span><span class="o">.</span><span class="n">sampler</span><span class="p">,</span> <span class="n">SequentialDistributedSampler</span><span class="p">):</span> + <span class="n">make_multiple_of</span> <span class="o">=</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">sampler</span><span class="o">.</span><span class="n">batch_size</span> + <span class="n">preds_gatherer</span> <span class="o">=</span> <span class="n">DistributedTensorGatherer</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">make_multiple_of</span><span class="o">=</span><span class="n">make_multiple_of</span><span class="p">)</span> + <span class="n">labels_gatherer</span> <span class="o">=</span> <span class="n">DistributedTensorGatherer</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">make_multiple_of</span><span class="o">=</span><span class="n">make_multiple_of</span><span class="p">)</span> + <span class="n">inputs_gatherer</span> <span class="o">=</span> <span class="n">DistributedTensorGatherer</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="n">num_examples</span><span class="p">,</span> <span class="n">make_multiple_of</span><span class="o">=</span><span class="n">make_multiple_of</span><span class="p">)</span> + + <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span> + + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">dataloader</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">ParallelLoader</span><span class="p">(</span><span class="n">dataloader</span><span class="p">,</span> <span class="p">[</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">])</span><span class="o">.</span><span class="n">per_device_loader</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">_past</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">eval_dataloader</span> <span class="o">=</span> <span class="n">dataloader</span> + + <span class="k">for</span> <span class="n">step</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dataloader</span><span class="p">):</span> + <span class="n">loss</span><span class="p">,</span> <span class="n">logits</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prediction_step</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">prediction_loss_only</span><span class="p">,</span> <span class="n">ignore_keys</span><span class="o">=</span><span class="n">ignore_keys</span><span class="p">)</span> + <span class="n">inputs_decode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_input</span><span class="p">(</span><span class="n">inputs</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">])</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">include_inputs_for_metrics</span> <span class="k">else</span> <span class="kc">None</span> + + <span class="k">if</span> <span class="n">loss</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">losses</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span> + <span class="n">losses_host</span> <span class="o">=</span> <span class="n">losses</span> <span class="k">if</span> <span class="n">losses_host</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">losses_host</span><span class="p">,</span> <span class="n">losses</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> + <span class="k">if</span> <span class="n">logits</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">preds_host</span> <span class="o">=</span> <span class="n">logits</span> <span class="k">if</span> <span class="n">preds_host</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">preds_host</span><span class="p">,</span> <span class="n">logits</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="k">if</span> <span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">labels_host</span> <span class="o">=</span> <span class="n">labels</span> <span class="k">if</span> <span class="n">labels_host</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">labels_host</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="k">if</span> <span class="n">inputs_decode</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">inputs_host</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">inputs_decode</span> + <span class="k">if</span> <span class="n">inputs_host</span> <span class="ow">is</span> <span class="kc">None</span> + <span class="k">else</span> <span class="n">nested_concat</span><span class="p">(</span><span class="n">inputs_host</span><span class="p">,</span> <span class="n">inputs_decode</span><span class="p">,</span> <span class="n">padding_index</span><span class="o">=-</span><span class="mi">100</span><span class="p">)</span> + <span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">control</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">callback_handler</span><span class="o">.</span><span class="n">on_prediction_step</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">state</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">control</span><span class="p">)</span> + + <span class="c1"># Gather all tensors and put them back on the CPU if we have done enough accumulation steps.</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">eval_accumulation_steps</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="p">(</span><span class="n">step</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">args</span><span class="o">.</span><span class="n">eval_accumulation_steps</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">eval_losses_gatherer</span><span class="o">.</span><span class="n">add_arrays</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_gather_and_numpify</span><span class="p">(</span><span class="n">losses_host</span><span class="p">,</span> <span class="s2">&quot;eval_losses&quot;</span><span class="p">))</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">prediction_loss_only</span><span class="p">:</span> + <span class="n">preds_gatherer</span><span class="o">.</span><span class="n">add_arrays</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_gather_and_numpify</span><span class="p">(</span><span class="n">preds_host</span><span class="p">,</span> <span class="s2">&quot;eval_preds&quot;</span><span class="p">))</span> + <span class="n">labels_gatherer</span><span class="o">.</span><span class="n">add_arrays</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_gather_and_numpify</span><span class="p">(</span><span class="n">labels_host</span><span class="p">,</span> <span class="s2">&quot;eval_label_ids&quot;</span><span class="p">))</span> + <span class="n">inputs_gatherer</span><span class="o">.</span><span class="n">add_arrays</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_gather_and_numpify</span><span class="p">(</span><span class="n">inputs_host</span><span class="p">,</span> <span class="s2">&quot;eval_inputs_ids&quot;</span><span class="p">))</span> + + <span class="c1"># Set back to None to begin a new accumulation</span> + <span class="n">losses_host</span><span class="p">,</span> <span class="n">preds_host</span><span class="p">,</span> <span class="n">labels_host</span><span class="p">,</span> <span class="n">inputs_host</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span> + + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">past_index</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_past&quot;</span><span class="p">):</span> + <span class="c1"># Clean the state at the end of the evaluation loop</span> + <span class="nb">delattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_past&quot;</span><span class="p">)</span> + + <span class="c1"># Gather all remaining tensors and put them back on the CPU</span> + <span class="n">eval_losses_gatherer</span><span class="o">.</span><span class="n">add_arrays</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_gather_and_numpify</span><span class="p">(</span><span class="n">losses_host</span><span class="p">,</span> <span class="s2">&quot;eval_losses&quot;</span><span class="p">))</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">prediction_loss_only</span><span class="p">:</span> + <span class="n">preds_gatherer</span><span class="o">.</span><span class="n">add_arrays</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_gather_and_numpify</span><span class="p">(</span><span class="n">preds_host</span><span class="p">,</span> <span class="s2">&quot;eval_preds&quot;</span><span class="p">))</span> + <span class="n">labels_gatherer</span><span class="o">.</span><span class="n">add_arrays</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_gather_and_numpify</span><span class="p">(</span><span class="n">labels_host</span><span class="p">,</span> <span class="s2">&quot;eval_label_ids&quot;</span><span class="p">))</span> + <span class="n">inputs_gatherer</span><span class="o">.</span><span class="n">add_arrays</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_gather_and_numpify</span><span class="p">(</span><span class="n">inputs_host</span><span class="p">,</span> <span class="s2">&quot;eval_inputs_ids&quot;</span><span class="p">))</span> + + <span class="n">eval_loss</span> <span class="o">=</span> <span class="n">eval_losses_gatherer</span><span class="o">.</span><span class="n">finalize</span><span class="p">()</span> + <span class="n">preds</span> <span class="o">=</span> <span class="n">preds_gatherer</span><span class="o">.</span><span class="n">finalize</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">prediction_loss_only</span> <span class="k">else</span> <span class="kc">None</span> + <span class="n">label_ids</span> <span class="o">=</span> <span class="n">labels_gatherer</span><span class="o">.</span><span class="n">finalize</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">prediction_loss_only</span> <span class="k">else</span> <span class="kc">None</span> + <span class="n">inputs_ids</span> <span class="o">=</span> <span class="n">inputs_gatherer</span><span class="o">.</span><span class="n">finalize</span><span class="p">()</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">prediction_loss_only</span> <span class="k">else</span> <span class="kc">None</span> + + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">preds</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">label_ids</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">include_inputs_for_metrics</span><span class="p">:</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span><span class="p">(</span> + <span class="n">EvalPrediction</span><span class="p">(</span><span class="n">predictions</span><span class="o">=</span><span class="n">preds</span><span class="p">,</span> <span class="n">label_ids</span><span class="o">=</span><span class="n">label_ids</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs_ids</span><span class="p">)</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span><span class="p">(</span><span class="n">EvalPrediction</span><span class="p">(</span><span class="n">predictions</span><span class="o">=</span><span class="n">preds</span><span class="p">,</span> <span class="n">label_ids</span><span class="o">=</span><span class="n">label_ids</span><span class="p">))</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="p">{}</span> + + <span class="c1"># To be JSON-serializable, we need to remove numpy types or zero-d tensors</span> + <span class="n">metrics</span> <span class="o">=</span> <span class="n">denumpify_detensorize</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">eval_loss</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_loss&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">eval_loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> + + <span class="c1"># Prefix all keys with metric_key_prefix + &#39;_&#39;</span> + <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">):</span> + <span class="n">metrics</span><span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">metric_key_prefix</span><span class="si">}</span><span class="s2">_</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">EvalLoopOutput</span><span class="p">(</span><span class="n">predictions</span><span class="o">=</span><span class="n">preds</span><span class="p">,</span> <span class="n">label_ids</span><span class="o">=</span><span class="n">label_ids</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="n">metrics</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="n">num_examples</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._gather_and_numpify"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._gather_and_numpify">[docs]</a> <span class="k">def</span> <span class="nf">_gather_and_numpify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tensors</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before</span> +<span class="sd"> concatenating them to `gathered`</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="k">if</span> <span class="n">tensors</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">return</span> + <span class="k">if</span> <span class="n">is_torch_tpu_available</span><span class="p">():</span> + <span class="n">tensors</span> <span class="o">=</span> <span class="n">nested_xla_mesh_reduce</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">is_sagemaker_mp_enabled</span><span class="p">():</span> + <span class="n">tensors</span> <span class="o">=</span> <span class="n">smp_gather</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span> + <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> + <span class="n">tensors</span> <span class="o">=</span> <span class="n">distributed_concat</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">nested_numpify</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span></div> + +<div class="viewcode-block" id="RaftTrainer._add_sm_patterns_to_gitignore"><a class="viewcode-back" href="../../../../autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._add_sm_patterns_to_gitignore">[docs]</a> <span class="k">def</span> <span class="nf">_add_sm_patterns_to_gitignore</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;Add SageMaker Checkpointing patterns to .gitignore file.&quot;&quot;&quot;</span> + <span class="c1"># Make sure we only do this on the main process</span> + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_world_process_zero</span><span class="p">():</span> + <span class="k">return</span> + + <span class="n">patterns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;*.sagemaker-uploading&quot;</span><span class="p">,</span> <span class="s2">&quot;*.sagemaker-uploaded&quot;</span><span class="p">]</span> + + <span class="c1"># Get current .gitignore content</span> + <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">local_dir</span><span class="p">,</span> <span class="s2">&quot;.gitignore&quot;</span><span class="p">)):</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">local_dir</span><span class="p">,</span> <span class="s2">&quot;.gitignore&quot;</span><span class="p">),</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> + <span class="n">current_content</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">current_content</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> + + <span class="c1"># Add the patterns to .gitignore</span> + <span class="n">content</span> <span class="o">=</span> <span class="n">current_content</span> + <span class="k">for</span> <span class="n">pattern</span> <span class="ow">in</span> <span class="n">patterns</span><span class="p">:</span> + <span class="k">if</span> <span class="n">pattern</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">content</span><span class="p">:</span> + <span class="k">if</span> <span class="n">content</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">):</span> + <span class="n">content</span> <span class="o">+=</span> <span class="n">pattern</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">content</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">{</span><span class="n">pattern</span><span class="si">}</span><span class="s2">&quot;</span> + + <span class="c1"># Write the .gitignore file if it has changed</span> + <span class="k">if</span> <span class="n">content</span> <span class="o">!=</span> <span class="n">current_content</span><span class="p">:</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">local_dir</span><span class="p">,</span> <span class="s2">&quot;.gitignore&quot;</span><span class="p">),</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> + <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Writing .gitignore file. Content: </span><span class="si">{</span><span class="n">content</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">content</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">git_add</span><span class="p">(</span><span class="s2">&quot;.gitignore&quot;</span><span class="p">)</span> + + <span class="c1"># avoid race condition with git status</span> + <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span> + + <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">is_repo_clean</span><span class="p">():</span> + <span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">git_commit</span><span class="p">(</span><span class="s2">&quot;Add *.sagemaker patterns to .gitignore.&quot;</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">repo</span><span class="o">.</span><span class="n">git_push</span><span class="p">()</span></div></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script 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class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.utils.constants</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.utils.constants</h1><div class="highlight"><pre> +<span></span><span class="ch">#!/usr/bin/env python</span> +<span class="c1"># coding=utf-8</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="sd">Commonly used constants.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<div class="viewcode-block" id="TEXT_ONLY_DATASET_DESCRIPTION"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT_ONLY_DATASET_DESCRIPTION">[docs]</a><span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span> <span class="o">=</span> <span class="p">(</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="sd">&quot;text_only&quot;: a dataset with only raw text instances, with following format:</span> + +<span class="sd"> {</span> +<span class="sd"> &quot;type&quot;: &quot;text_only&quot;,</span> +<span class="sd"> &quot;instances&quot;: [</span> +<span class="sd"> { &quot;text&quot;: &quot;TEXT_1&quot; },</span> +<span class="sd"> { &quot;text&quot;: &quot;TEXT_2&quot; },</span> +<span class="sd"> ...</span> +<span class="sd"> ]</span> +<span class="sd"> }</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="TEXT_ONLY_DATASET_DETAILS"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT_ONLY_DATASET_DETAILS">[docs]</a><span class="n">TEXT_ONLY_DATASET_DETAILS</span> <span class="o">=</span> <span class="p">(</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> For example,</span> + +<span class="sd"> ```python</span> +<span class="sd"> from lmflow.datasets import Dataset</span> + +<span class="sd"> data_dict = {</span> +<span class="sd"> &quot;type&quot;: &quot;text_only&quot;,</span> +<span class="sd"> &quot;instances&quot;: [</span> +<span class="sd"> { &quot;text&quot;: &quot;Human: Hello. Bot: Hi!&quot; },</span> +<span class="sd"> { &quot;text&quot;: &quot;Human: How are you today? Bot: Fine, thank you!&quot; },</span> +<span class="sd"> ]</span> +<span class="sd"> }</span> +<span class="sd"> dataset = Dataset.create_from_dict(data_dict)</span> +<span class="sd"> ```</span> + +<span class="sd"> You may also save the corresponding format to json,</span> +<span class="sd"> ```python</span> +<span class="sd"> import json</span> +<span class="sd"> from lmflow.args import DatasetArguments</span> +<span class="sd"> from lmflow.datasets import Dataset</span> + +<span class="sd"> data_dict = {</span> +<span class="sd"> &quot;type&quot;: &quot;text_only&quot;,</span> +<span class="sd"> &quot;instances&quot;: [</span> +<span class="sd"> { &quot;text&quot;: &quot;Human: Hello. Bot: Hi!&quot; },</span> +<span class="sd"> { &quot;text&quot;: &quot;Human: How are you today? Bot: Fine, thank you!&quot; },</span> +<span class="sd"> ]</span> +<span class="sd"> }</span> +<span class="sd"> with open(&quot;data.json&quot;, &quot;w&quot;) as fout:</span> +<span class="sd"> json.dump(data_dict, fout)</span> + +<span class="sd"> data_args = DatasetArgument(dataset_path=&quot;data.json&quot;)</span> +<span class="sd"> dataset = Dataset(data_args)</span> +<span class="sd"> new_data_dict = dataset.to_dict()</span> +<span class="sd"> # `new_data_dict` Should have the same content as `data_dict`</span> +<span class="sd"> ```</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="TEXT2TEXT_DATASET_DESCRIPTION"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT2TEXT_DATASET_DESCRIPTION">[docs]</a><span class="n">TEXT2TEXT_DATASET_DESCRIPTION</span> <span class="o">=</span> <span class="p">(</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="sd">&quot;text2text&quot;: a dataset with input &amp; output instances, with following format:</span> + +<span class="sd"> {</span> +<span class="sd"> &quot;type&quot;: &quot;text2text&quot;,</span> +<span class="sd"> &quot;instances&quot;: [</span> +<span class="sd"> { &quot;input&quot;: &quot;INPUT_1&quot;, &quot;output&quot;: &quot;OUTPUT_1&quot; },</span> +<span class="sd"> { &quot;input&quot;: &quot;INPUT_2&quot;, &quot;output&quot;: &quot;OUTPUT_2&quot; },</span> +<span class="sd"> ...</span> +<span class="sd"> ]</span> +<span class="sd"> }</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="TEXT2TEXT_DATASET_DETAILS"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT2TEXT_DATASET_DETAILS">[docs]</a><span class="n">TEXT2TEXT_DATASET_DETAILS</span> <span class="o">=</span> <span class="p">(</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> For example,</span> + +<span class="sd"> ```python</span> +<span class="sd"> from lmflow.datasets import Dataset</span> + +<span class="sd"> data_dict = {</span> +<span class="sd"> &quot;type&quot;: &quot;text2text&quot;,</span> +<span class="sd"> &quot;instances&quot;: [</span> +<span class="sd"> {</span> +<span class="sd"> &quot;input&quot;: &quot;Human: Hello.&quot;,</span> +<span class="sd"> &quot;output&quot;: &quot;Bot: Hi!&quot;,</span> +<span class="sd"> },</span> +<span class="sd"> {</span> +<span class="sd"> &quot;input&quot;: &quot;Human: How are you today?&quot;,</span> +<span class="sd"> &quot;output&quot;: &quot;Bot: Fine, thank you! And you?&quot;,</span> +<span class="sd"> }</span> +<span class="sd"> ]</span> +<span class="sd"> }</span> +<span class="sd"> dataset = Dataset.create_from_dict(data_dict)</span> +<span class="sd"> ```</span> + +<span class="sd"> You may also save the corresponding format to json,</span> +<span class="sd"> ```python</span> +<span class="sd"> import json</span> +<span class="sd"> from lmflow.args import DatasetArguments</span> +<span class="sd"> from lmflow.datasets import Dataset</span> + +<span class="sd"> data_dict = {</span> +<span class="sd"> &quot;type&quot;: &quot;text2text&quot;,</span> +<span class="sd"> &quot;instances&quot;: [</span> +<span class="sd"> {</span> +<span class="sd"> &quot;input&quot;: &quot;Human: Hello.&quot;,</span> +<span class="sd"> &quot;output&quot;: &quot;Bot: Hi!&quot;,</span> +<span class="sd"> },</span> +<span class="sd"> {</span> +<span class="sd"> &quot;input&quot;: &quot;Human: How are you today?&quot;,</span> +<span class="sd"> &quot;output&quot;: &quot;Bot: Fine, thank you! And you?&quot;,</span> +<span class="sd"> }</span> +<span class="sd"> ]</span> +<span class="sd"> }</span> +<span class="sd"> with open(&quot;data.json&quot;, &quot;w&quot;) as fout:</span> +<span class="sd"> json.dump(data_dict, fout)</span> + +<span class="sd"> data_args = DatasetArgument(dataset_path=&quot;data.json&quot;)</span> +<span class="sd"> dataset = Dataset(data_args)</span> +<span class="sd"> new_data_dict = dataset.to_dict()</span> +<span class="sd"> # `new_data_dict` Should have the same content as `data_dict`</span> +<span class="sd"> ```</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="FLOAT_ONLY_DATASET_DESCRIPTION"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.FLOAT_ONLY_DATASET_DESCRIPTION">[docs]</a><span class="n">FLOAT_ONLY_DATASET_DESCRIPTION</span> <span class="o">=</span> <span class="p">(</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="sd">&quot;float_only&quot;: a dataset with only float instances, with following format:</span> + +<span class="sd"> {</span> +<span class="sd"> &quot;type&quot;: &quot;float_only&quot;,</span> +<span class="sd"> &quot;instances&quot;: [</span> +<span class="sd"> { &quot;value&quot;: &quot;FLOAT_1&quot; },</span> +<span class="sd"> { &quot;value&quot;: &quot;FLOAT_2&quot; },</span> +<span class="sd"> ...</span> +<span class="sd"> ]</span> +<span class="sd"> }</span> +<span class="sd">&quot;&quot;&quot;</span> +<span class="p">)</span><span class="o">.</span><span class="n">lstrip</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="TEXT_ONLY_DATASET_LONG_DESCRITION"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT_ONLY_DATASET_LONG_DESCRITION">[docs]</a><span class="n">TEXT_ONLY_DATASET_LONG_DESCRITION</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span> <span class="o">+</span> <span class="n">TEXT_ONLY_DATASET_DETAILS</span> +<span class="p">)</span></div> + +<div class="viewcode-block" id="TEXT2TEXT_DATASET_LONG_DESCRITION"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT2TEXT_DATASET_LONG_DESCRITION">[docs]</a><span class="n">TEXT2TEXT_DATASET_LONG_DESCRITION</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">TEXT2TEXT_DATASET_DESCRIPTION</span> <span class="o">+</span> <span class="n">TEXT2TEXT_DATASET_DETAILS</span> +<span class="p">)</span></div> + + +<div class="viewcode-block" id="DATASET_DESCRIPTION_MAP"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.DATASET_DESCRIPTION_MAP">[docs]</a><span class="n">DATASET_DESCRIPTION_MAP</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;text_only&quot;</span><span class="p">:</span> <span class="n">TEXT_ONLY_DATASET_DESCRIPTION</span><span class="p">,</span> + <span class="s2">&quot;text2text&quot;</span><span class="p">:</span> <span class="n">TEXT2TEXT_DATASET_DESCRIPTION</span><span class="p">,</span> + <span class="s2">&quot;float_only&quot;</span><span class="p">:</span> <span class="n">FLOAT_ONLY_DATASET_DESCRIPTION</span><span class="p">,</span> +<span class="p">}</span></div> + +<div class="viewcode-block" id="INSTANCE_FIELDS_MAP"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.INSTANCE_FIELDS_MAP">[docs]</a><span class="n">INSTANCE_FIELDS_MAP</span> <span class="o">=</span> <span class="p">{</span> + <span class="s2">&quot;text_only&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;text&quot;</span><span class="p">],</span> + <span class="s2">&quot;text2text&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">,</span> <span class="s2">&quot;output&quot;</span><span class="p">],</span> + <span class="s2">&quot;float_only&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;value&quot;</span><span class="p">],</span> + <span class="s2">&quot;image_text&quot;</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;images&quot;</span><span class="p">,</span> <span class="s2">&quot;text&quot;</span><span class="p">],</span> +<span class="p">}</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div class="footer-item"> + <p class="copyright"> + + © Copyright LMFlow 2023. + <br/> + + </p> +</div> + + <div class="footer-item"> + <p class="sphinx-version"> + Created using <a href="https://sphinx-doc.org/">Sphinx</a> 5.3.0. + <br/> + </p> +</div> + + </div> + + + <div 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class="header-article-items header-article__inner"> + + <div class="header-article-items__start"> + + <div class="header-article-item"> + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.utils.data_utils</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.utils.data_utils</h1><div class="highlight"><pre> +<span></span><span class="sd">&quot;&quot;&quot;The program includes several functions: setting a random seed, </span> +<span class="sd">loading data from a JSON file, batching data, and extracting answers from generated text.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">import</span> <span class="nn">random</span> +<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">json</span> +<span class="kn">import</span> <span class="nn">re</span> +<div class="viewcode-block" id="set_random_seed"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.set_random_seed">[docs]</a><span class="k">def</span> <span class="nf">set_random_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Set the random seed for `random`, `numpy`, `torch`, `torch.cuda`.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> seed : int</span> +<span class="sd"> The default seed.</span> +<span class="sd"> </span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span> + <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span> + <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span> + <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span> + <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">manual_seed_all</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span></div> + +<div class="viewcode-block" id="load_data"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.load_data">[docs]</a><span class="k">def</span> <span class="nf">load_data</span><span class="p">(</span><span class="n">file_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Load data with file name.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> file_name : str.</span> +<span class="sd"> The dataset file name.</span> +<span class="sd"> </span> +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> inputs : list.</span> +<span class="sd"> The input texts of the dataset.</span> +<span class="sd"> outputs : list.</span> +<span class="sd"> The output texts file datasets. </span> +<span class="sd"> len : int.</span> +<span class="sd"> The length of the dataset.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">outputs</span> <span class="o">=</span> <span class="p">[]</span> + <span class="nb">type</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> + <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_name</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> + <span class="n">json_data</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> + <span class="nb">type</span> <span class="o">=</span> <span class="n">json_data</span><span class="p">[</span><span class="s2">&quot;type&quot;</span><span class="p">]</span> + <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">json_data</span><span class="p">[</span><span class="s2">&quot;instances&quot;</span><span class="p">]:</span> + <span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="s2">&quot;input&quot;</span><span class="p">])</span> + <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="s2">&quot;output&quot;</span><span class="p">])</span> + + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;load dataset </span><span class="si">{</span><span class="n">file_name</span><span class="si">}</span><span class="s2"> success.</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span> + <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Type : </span><span class="si">{</span><span class="nb">type</span><span class="si">}</span><span class="s2">, datasize : </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span></div> + +<div class="viewcode-block" id="batchlize"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.batchlize">[docs]</a><span class="k">def</span> <span class="nf">batchlize</span><span class="p">(</span><span class="n">examples</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">random_shuffle</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Convert examples to a dataloader.</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> examples : list.</span> +<span class="sd"> Data list.</span> +<span class="sd"> batch_size : int.</span> + +<span class="sd"> random_shuffle : bool</span> +<span class="sd"> If true, the dataloader shuffle the training data.</span> +<span class="sd"> </span> +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> dataloader:</span> +<span class="sd"> Dataloader with batch generator.</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">size</span> <span class="o">=</span> <span class="mi">0</span> + <span class="n">dataloader</span> <span class="o">=</span> <span class="p">[]</span> + <span class="n">length</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">examples</span><span class="p">)</span> + <span class="k">if</span> <span class="p">(</span><span class="n">random_shuffle</span><span class="p">):</span> + <span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">examples</span><span class="p">)</span> + <span class="k">while</span> <span class="n">size</span> <span class="o">&lt;</span> <span class="n">length</span><span class="p">:</span> + <span class="k">if</span> <span class="n">length</span> <span class="o">-</span> <span class="n">size</span> <span class="o">&gt;</span> <span class="n">batch_size</span><span class="p">:</span> + <span class="n">dataloader</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">examples</span><span class="p">[</span><span class="n">size</span> <span class="p">:</span> <span class="n">size</span><span class="o">+</span><span class="n">batch_size</span><span class="p">])</span> + <span class="n">size</span> <span class="o">+=</span> <span class="n">batch_size</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">dataloader</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">examples</span><span class="p">[</span><span class="n">size</span> <span class="p">:</span> <span class="n">size</span><span class="o">+</span><span class="p">(</span><span class="n">length</span><span class="o">-</span><span class="n">size</span><span class="p">)])</span> + <span class="n">size</span> <span class="o">+=</span> <span class="p">(</span><span class="n">length</span> <span class="o">-</span> <span class="n">size</span><span class="p">)</span> + <span class="k">return</span> <span class="n">dataloader</span></div> + + + +<div class="viewcode-block" id="answer_extraction"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.answer_extraction">[docs]</a><span class="k">def</span> <span class="nf">answer_extraction</span><span class="p">(</span><span class="n">response</span><span class="p">,</span> <span class="n">answer_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="c1">#use this funtion to extract answers from generated text</span> + +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> Use this funtion to extract answers from generated text</span> + +<span class="sd"> Parameters</span> +<span class="sd"> ------------</span> +<span class="sd"> args : </span> +<span class="sd"> Arguments.</span> +<span class="sd"> response : str</span> +<span class="sd"> plain string response.</span> + + +<span class="sd"> Returns</span> +<span class="sd"> ------------</span> +<span class="sd"> answer:</span> +<span class="sd"> Decoded answer (such as A, B, C, D, E for mutiple-choice QA).</span> +<span class="sd"> &quot;&quot;&quot;</span> + + <span class="c1"># temp = response[&quot;generated_text&quot;]</span> + <span class="n">temp</span> <span class="o">=</span> <span class="n">response</span> + <span class="k">if</span> <span class="n">answer_type</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;gsm8k&quot;</span><span class="p">,</span> <span class="s2">&quot;svamp&quot;</span><span class="p">,</span> <span class="s2">&quot;asdiv&quot;</span><span class="p">,</span> <span class="s2">&quot;addsub&quot;</span><span class="p">,</span> <span class="s2">&quot;singleeq&quot;</span><span class="p">,</span> <span class="s2">&quot;multiarith&quot;</span><span class="p">,</span> <span class="s2">&quot;math&quot;</span><span class="p">):</span> + <span class="n">temp</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> + <span class="n">temp</span> <span class="o">=</span> <span class="p">[</span><span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">re</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;-?\d+\.?\d*&#39;</span><span class="p">,</span> <span class="n">temp</span><span class="p">)]</span> + <span class="k">elif</span> <span class="n">answer_type</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;aqua&quot;</span><span class="p">,</span> <span class="s2">&quot;csqa&quot;</span><span class="p">,</span> <span class="s2">&quot;multiple_choice&quot;</span><span class="p">):</span> + <span class="n">temp</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">findall</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;A|B|C|D|E&#39;</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span> + <span class="k">elif</span> <span class="n">answer_type</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;strategyqa&quot;</span><span class="p">,</span> <span class="s2">&quot;coin_flip&quot;</span><span class="p">):</span> + <span class="n">temp</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="n">temp</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\&quot;</span><span class="s2">|</span><span class="se">\&#39;</span><span class="s2">|</span><span class="se">\n</span><span class="s2">|\.|\s|\:|\,&quot;</span><span class="p">,</span><span class="s2">&quot; &quot;</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span> + <span class="n">temp</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">)</span> + <span class="n">temp</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">temp</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;yes&quot;</span><span class="p">,</span> <span class="s2">&quot;no&quot;</span><span class="p">)]</span> + <span class="k">elif</span> <span class="n">answer_type</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;last_letters&quot;</span><span class="p">):</span> + <span class="n">temp</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\&quot;</span><span class="s2">|</span><span class="se">\&#39;</span><span class="s2">|</span><span class="se">\n</span><span class="s2">|\.|\s&quot;</span><span class="p">,</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span> + <span class="n">temp</span> <span class="o">=</span> <span class="p">[</span><span class="n">temp</span><span class="p">]</span> + <span class="k">elif</span> <span class="n">answer_type</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;pubmedqa&quot;</span><span class="p">,</span> <span class="s2">&quot;binary_choice&quot;</span><span class="p">):</span> + <span class="c1"># pattern = &quot;Output: (yes|no|maybe)&quot;</span> + <span class="c1"># sttr = re.search(pattern, temp)</span> + <span class="c1"># answer = sttr.group(0)[8:] if sttr is not None else &quot;N/A&quot;</span> + <span class="n">pattern</span> <span class="o">=</span> <span class="s2">&quot;(answer|Answer|ANSWER|output|Output|OUTPUT|A): \(*(yes|Yes|YES|no|No|NO|maybe|Maybe|MAYBE)&quot;</span> + <span class="n">sttr</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span> + <span class="k">if</span> <span class="n">sttr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">mid_answer</span> <span class="o">=</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> + <span class="n">mid_answer</span> <span class="o">=</span> <span class="n">mid_answer</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">mid_answer</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">pattern</span> <span class="o">=</span> <span class="s2">&quot;(yes|Yes|YES|no|No|NO|maybe|Maybe|MAYBE)(\.|\s)&quot;</span> + <span class="n">sttr</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span> + <span class="k">if</span> <span class="n">sttr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">)[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="s2">&quot;N/A&quot;</span> + <span class="k">return</span> <span class="n">answer</span> + <span class="k">elif</span> <span class="n">answer_type</span> <span class="o">==</span> <span class="s2">&quot;medmcqa&quot;</span><span class="p">:</span> + <span class="c1"># pattern = &quot;Output: (A|B|C|D).&quot;</span> + <span class="c1"># sttr = re.search(pattern, temp)</span> + <span class="c1"># answer = sttr.group(0)[8:-1].lower() if sttr is not None else &quot;N/A&quot;</span> + <span class="n">pattern</span> <span class="o">=</span> <span class="s2">&quot;(answer|Answer|ANSWER|output|Output|OUTPUT|A): \(*(A|B|C|D|a|b|c|d)&quot;</span> + <span class="n">sttr</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span> + <span class="k">if</span> <span class="n">sttr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">mid_answer</span> <span class="o">=</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">mid_answer</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">pattern</span> <span class="o">=</span> <span class="s2">&quot;\(*(A|B|C|D|a|b|c|d)\)*(\.|\s)&quot;</span> + <span class="n">sttr</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span> + <span class="k">if</span> <span class="n">sttr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="s1">&#39;(&#39;</span> <span class="ow">in</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="s2">&quot;N/A&quot;</span> + <span class="k">return</span> <span class="n">answer</span> + + <span class="k">elif</span> <span class="n">answer_type</span> <span class="o">==</span> <span class="s2">&quot;usmle&quot;</span><span class="p">:</span> + <span class="c1"># pattern = &quot;Output: (A|B|C|D).&quot;</span> + <span class="c1"># sttr = re.search(pattern, temp)</span> + <span class="c1"># answer = sttr.group(0)[8:-1].lower() if sttr is not None else &quot;N/A&quot;</span> + <span class="n">pattern</span> <span class="o">=</span> <span class="s2">&quot;(Answer|Output|A): \(*(A|B|C|D|a|b|c|d)&quot;</span> + <span class="n">sttr</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span> + <span class="k">if</span> <span class="n">sttr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">mid_answer</span> <span class="o">=</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">mid_answer</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">pattern</span> <span class="o">=</span> <span class="s2">&quot;\(*(A|B|C|D|a|b|c|d)\)*(\.|\s)&quot;</span> + <span class="n">sttr</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">pattern</span><span class="p">,</span> <span class="n">temp</span><span class="p">)</span> + <span class="k">if</span> <span class="n">sttr</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="s1">&#39;(&#39;</span> <span class="ow">in</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">sttr</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="s2">&quot;N/A&quot;</span> + <span class="k">return</span> <span class="n">answer</span> + <span class="k">elif</span> <span class="n">answer_type</span> <span class="o">==</span> <span class="s2">&quot;text&quot;</span><span class="p">:</span> + <span class="k">return</span> <span class="n">response</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unsupported answer type: </span><span class="si">{</span><span class="n">answer_type</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span> + + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">temp</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">temp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> + <span class="c1"># if there is . at the end of answer, remove it</span> + <span class="c1"># e.g. answer = 64.</span> + <span class="k">if</span> <span class="n">answer</span> <span class="o">!=</span> <span class="s2">&quot;&quot;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">answer</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;.&quot;</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">answer</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> + + <span class="c1"># round the answer to nearest integer</span> + <span class="k">if</span> <span class="n">answer_type</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;gsm8k&quot;</span><span class="p">,</span> <span class="s2">&quot;svamp&quot;</span><span class="p">):</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">answer</span><span class="p">)))</span> + <span class="k">except</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> <span class="c1"># no sol or sol doesn&#39;t have valid format</span> + <span class="k">elif</span> <span class="n">answer_type</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;last_letters&quot;</span><span class="p">):</span> + <span class="k">try</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="n">answer</span><span class="p">[</span><span class="o">-</span><span class="n">args</span><span class="o">.</span><span class="n">concat_length</span><span class="p">:]</span> + <span class="k">except</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">answer</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> + <span class="k">return</span> <span class="n">answer</span></div> + + +<div class="viewcode-block" id="process_image_flag"><a class="viewcode-back" href="../../../autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.process_image_flag">[docs]</a><span class="k">def</span> <span class="nf">process_image_flag</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">image_flag</span><span class="o">=</span><span class="s2">&quot;&lt;ImageHere&gt;&quot;</span><span class="p">):</span> + <span class="n">texts</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">image_flag</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">texts</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">image_token_indexes</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">text</span><span class="p">)</span> <span class="k">for</span> <span class="n">text</span> <span class="ow">in</span> <span class="n">texts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">image_token_indexes</span> <span class="o">=</span> <span class="p">[]</span> + <span class="c1"># cumsun</span> + <span class="n">image_token_indexes</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">image_token_indexes</span><span class="p">))</span> + <span class="n">texts</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">texts</span><span class="p">)</span> + <span class="k">return</span> <span class="n">texts</span><span class="p">,</span> <span class="n">image_token_indexes</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer 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class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.utils.flash_attention.bloom_flash_attention</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.utils.flash_attention.bloom_flash_attention</h1><div class="highlight"><pre> +<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span> + +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span> +<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span> + +<span class="kn">import</span> <span class="nn">transformers</span> +<span class="kn">from</span> <span class="nn">transformers.models.bloom.modeling_bloom</span> <span class="kn">import</span> <span class="n">dropout_add</span> + +<span class="kn">from</span> <span class="nn">einops</span> <span class="kn">import</span> <span class="n">rearrange</span> + +<span class="kn">from</span> <span class="nn">.triton_flash_attention</span> <span class="kn">import</span> <span class="n">flash_attn_qkvpacked_func</span> + +<div class="viewcode-block" id="forward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.html#lmflow.utils.flash_attention.bloom_flash_attention.forward">[docs]</a><span class="k">def</span> <span class="nf">forward</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">hidden_states</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> + <span class="n">residual</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> + <span class="n">alibi</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> + <span class="n">attention_mask</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> + <span class="n">layer_past</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">head_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">use_cache</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> + <span class="n">output_attentions</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> + <span class="p">):</span> + <span class="n">dtype</span> <span class="o">=</span> <span class="n">hidden_states</span><span class="o">.</span><span class="n">dtype</span> + <span class="n">fused_qkv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">query_key_value</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> <span class="c1"># [batch_size, seq_length, 3 x hidden_size]</span> + + <span class="c1"># 3 x [batch_size, seq_length, num_heads, head_dim]</span> + <span class="p">(</span><span class="n">query_layer</span><span class="p">,</span> <span class="n">key_layer</span><span class="p">,</span> <span class="n">value_layer</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_split_heads</span><span class="p">(</span><span class="n">fused_qkv</span><span class="p">)</span> + + <span class="n">batch_size</span><span class="p">,</span> <span class="n">q_length</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">query_layer</span><span class="o">.</span><span class="n">shape</span> + <span class="n">bsz</span><span class="p">,</span> <span class="n">q_len</span> <span class="o">=</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">q_length</span> + + <span class="k">if</span> <span class="n">layer_past</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">past_key</span><span class="p">,</span> <span class="n">past_value</span> <span class="o">=</span> <span class="n">layer_past</span> + <span class="c1"># concatenate along seq_length dimension:</span> + <span class="c1"># - key: [batch_size * self.num_heads, head_dim, kv_length]</span> + <span class="c1"># - value: [batch_size * self.num_heads, kv_length, head_dim]</span> + <span class="n">key_layer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">past_key</span><span class="p">,</span> <span class="n">key_layer</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> + <span class="n">value_layer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">past_value</span><span class="p">,</span> <span class="n">value_layer</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">use_cache</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span> + <span class="n">present</span> <span class="o">=</span> <span class="p">(</span><span class="n">key_layer</span><span class="p">,</span> <span class="n">value_layer</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">present</span> <span class="o">=</span> <span class="kc">None</span> + + <span class="n">reshaped_alibi</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">alibi</span><span class="p">,</span> <span class="s1">&#39;(b h) one s-&gt; b h one s&#39;</span><span class="p">,</span> <span class="n">h</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">)</span> + <span class="n">reshaped_alibi</span> <span class="o">=</span> <span class="n">reshaped_alibi</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> + + <span class="n">attention_mask</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">attention_mask</span><span class="p">)</span> + <span class="n">attention_mask</span> <span class="o">=</span> <span class="n">attention_mask</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">bool</span><span class="p">()</span> + <span class="n">reshaped_alibi_masked</span> <span class="o">=</span> <span class="n">reshaped_alibi</span><span class="o">.</span><span class="n">masked_fill</span><span class="p">(</span><span class="n">attention_mask</span><span class="p">,</span> <span class="o">-</span><span class="mf">1e9</span><span class="p">)</span> + + <span class="n">reshaped_query_layer</span> <span class="o">=</span> <span class="n">query_layer</span> + <span class="n">reshaped_key_layer</span> <span class="o">=</span> <span class="n">key_layer</span> + <span class="n">reshaped_value_layer</span> <span class="o">=</span> <span class="n">value_layer</span> + + <span class="n">qkv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">reshaped_query_layer</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">reshaped_key_layer</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">reshaped_value_layer</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)],</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span> + + <span class="n">output</span> <span class="o">=</span> <span class="n">flash_attn_qkvpacked_func</span><span class="p">(</span> + <span class="n">qkv</span><span class="p">,</span> <span class="n">reshaped_alibi_masked</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inv_norm_factor</span> + <span class="p">)</span> + + <span class="n">output</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="s1">&#39;b s h d -&gt; (b h) s d&#39;</span><span class="p">)</span> + + <span class="c1"># change view [batch_size, num_heads, q_length, head_dim]</span> + <span class="n">context_layer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_merge_heads</span><span class="p">(</span><span class="n">output</span><span class="p">)</span> + + <span class="c1"># aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232</span> + <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pretraining_tp</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">slow_but_exact</span><span class="p">:</span> + <span class="n">slices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">pretraining_tp</span> + <span class="n">output_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">context_layer</span><span class="p">)</span> + <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pretraining_tp</span><span class="p">):</span> + <span class="n">output_tensor</span> <span class="o">=</span> <span class="n">output_tensor</span> <span class="o">+</span> <span class="n">F</span><span class="o">.</span><span class="n">linear</span><span class="p">(</span> + <span class="n">context_layer</span><span class="p">[:,</span> <span class="p">:,</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">*</span> <span class="n">slices</span><span class="p">)</span> <span class="p">:</span> <span class="nb">int</span><span class="p">((</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">slices</span><span class="p">)],</span> + <span class="bp">self</span><span class="o">.</span><span class="n">dense</span><span class="o">.</span><span class="n">weight</span><span class="p">[:,</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span> <span class="o">*</span> <span class="n">slices</span><span class="p">)</span> <span class="p">:</span> <span class="nb">int</span><span class="p">((</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">slices</span><span class="p">)],</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">output_tensor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dense</span><span class="p">(</span><span class="n">context_layer</span><span class="p">)</span> + + <span class="n">output_tensor</span> <span class="o">=</span> <span class="n">dropout_add</span><span class="p">(</span><span class="n">output_tensor</span><span class="p">,</span> <span class="n">residual</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dropout</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">)</span> + + <span class="n">outputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">output_tensor</span><span class="p">,</span> <span class="n">present</span><span class="p">)</span> + <span class="k">if</span> <span class="n">output_attentions</span><span class="p">:</span> + <span class="n">outputs</span> <span class="o">+=</span> <span class="p">(</span><span class="n">context_layer</span><span class="p">,)</span> + + <span class="k">return</span> <span class="n">outputs</span></div> + + +<span class="c1"># Disable the transformation of the attention mask in LlamaModel as the flash attention</span> +<span class="c1"># requires the attention mask to be the same as the key_padding_mask</span> +<div class="viewcode-block" id="_prepare_attn_mask"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.html#lmflow.utils.flash_attention.bloom_flash_attention._prepare_attn_mask">[docs]</a><span class="k">def</span> <span class="nf">_prepare_attn_mask</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">attention_mask</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">past_key_values_length</span><span class="p">:</span> <span class="nb">int</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">BoolTensor</span><span class="p">:</span> + + <span class="k">return</span> <span class="n">attention_mask</span></div> + +<div class="viewcode-block" id="replace_bloom_attn_with_flash_attn"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.html#lmflow.utils.flash_attention.bloom_flash_attention.replace_bloom_attn_with_flash_attn">[docs]</a><span class="k">def</span> <span class="nf">replace_bloom_attn_with_flash_attn</span><span class="p">():</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">bloom</span><span class="o">.</span><span class="n">modeling_bloom</span><span class="o">.</span><span class="n">BloomModel</span><span class="o">.</span><span class="n">_prepare_attn_mask</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">_prepare_attn_mask</span> + <span class="p">)</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">bloom</span><span class="o">.</span><span class="n">modeling_bloom</span><span class="o">.</span><span class="n">BloomAttention</span><span class="o">.</span><span class="n">forward</span> <span class="o">=</span> <span class="n">forward</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div 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class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.utils.flash_attention.gpt2_flash_attention</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.utils.flash_attention.gpt2_flash_attention</h1><div class="highlight"><pre> +<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span> + +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span> + +<span class="kn">import</span> <span class="nn">transformers</span> +<span class="kn">from</span> <span class="nn">transformers.models.llama.modeling_llama</span> <span class="kn">import</span> <span class="n">apply_rotary_pos_emb</span> + +<span class="kn">from</span> <span class="nn">einops</span> <span class="kn">import</span> <span class="n">rearrange</span> + +<span class="kn">import</span> <span class="nn">flash_attn</span> +<span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">flash_attn</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">flash_attn.flash_attn_interface</span> <span class="kn">import</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span> +<span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">flash_attn</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">flash_attn.flash_attn_interface</span> <span class="kn">import</span> <span class="n">flash_attn_varlen_qkvpacked_func</span> <span class="k">as</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span> + +<span class="kn">from</span> <span class="nn">flash_attn.bert_padding</span> <span class="kn">import</span> <span class="n">unpad_input</span><span class="p">,</span> <span class="n">pad_input</span> + + +<div class="viewcode-block" id="forward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.html#lmflow.utils.flash_attention.gpt2_flash_attention.forward">[docs]</a><span class="k">def</span> <span class="nf">forward</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]],</span> + <span class="n">layer_past</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">attention_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">head_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">encoder_hidden_states</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">encoder_attention_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">use_cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> + <span class="n">output_attentions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> + <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]],</span> <span class="o">...</span><span class="p">]:</span> + + + <span class="k">if</span> <span class="n">encoder_hidden_states</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;q_attn&quot;</span><span class="p">):</span> + <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span> + <span class="s2">&quot;If class is used as cross attention, the weights `q_attn` have to be defined. &quot;</span> + <span class="s2">&quot;Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`.&quot;</span> + <span class="p">)</span> + + <span class="n">query</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">q_attn</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> + <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_attn</span><span class="p">(</span><span class="n">encoder_hidden_states</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">split_size</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> + <span class="n">attention_mask</span> <span class="o">=</span> <span class="n">encoder_attention_mask</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">query</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_attn</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">split_size</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span> + + <span class="n">bsz</span><span class="p">,</span> <span class="n">q_len</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">hidden_states</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> + + <span class="n">query</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_split_heads</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + <span class="n">key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_split_heads</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_split_heads</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + + <span class="c1">#TODO Should we support?</span> + <span class="k">if</span> <span class="n">layer_past</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">past_key</span><span class="p">,</span> <span class="n">past_value</span> <span class="o">=</span> <span class="n">layer_past</span> + <span class="n">key</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">past_key</span><span class="p">,</span> <span class="n">key</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">)</span> + <span class="n">value</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">past_value</span><span class="p">,</span> <span class="n">value</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">)</span> + + <span class="k">assert</span> <span class="n">use_cache</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">,</span> <span class="s2">&quot;Use cache is not supported&quot;</span> + <span class="n">present</span> <span class="o">=</span> <span class="kc">None</span> + <span class="c1"># if use_cache is True:</span> + <span class="c1"># present = (key, value)</span> + <span class="c1"># else:</span> + <span class="c1"># present = None</span> + + <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">reorder_and_upcast_attn</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">,</span> <span class="s2">&quot;reorder_and_upcast_attn is not supported yet&quot;</span> + + <span class="n">qkv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">query</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">],</span> <span class="n">dim</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span> + <span class="n">qkv</span> <span class="o">=</span> <span class="n">qkv</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="c1"># [bsz, seq_len, 3, heads, hiddens_per_head]</span> + + <span class="c1"># breakpoint()</span> + <span class="n">key_padding_mask</span> <span class="o">=</span> <span class="n">attention_mask</span> + <span class="c1"># key_padding_mask = None</span> + <span class="c1"># breakpoint()</span> + <span class="k">if</span> <span class="n">key_padding_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">qkv</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">qkv</span><span class="p">,</span> <span class="s2">&quot;b s ... -&gt; (b s) ...&quot;</span><span class="p">)</span> + <span class="n">max_s</span> <span class="o">=</span> <span class="n">q_len</span> + <span class="n">cu_q_lens</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span> + <span class="mi">0</span><span class="p">,</span> <span class="p">(</span><span class="n">bsz</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">q_len</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="n">q_len</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">qkv</span><span class="o">.</span><span class="n">device</span> + <span class="p">)</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span><span class="p">(</span> + <span class="n">qkv</span><span class="p">,</span> <span class="n">cu_q_lens</span><span class="p">,</span> <span class="n">max_s</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="s2">&quot;(b s) ... -&gt; b s ...&quot;</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">bsz</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># flip in flash attention</span> + <span class="n">key_padding_mask</span> <span class="o">=</span> <span class="n">key_padding_mask</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span> + <span class="n">key_padding_mask</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">key_padding_mask</span><span class="p">)</span> + <span class="n">key_padding_mask</span> <span class="o">=</span> <span class="n">key_padding_mask</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> + <span class="n">nheads</span> <span class="o">=</span> <span class="n">qkv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> + <span class="n">x</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">qkv</span><span class="p">,</span> <span class="s2">&quot;b s three h d -&gt; b s (three h d)&quot;</span><span class="p">)</span> + <span class="n">x_unpad</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">cu_q_lens</span><span class="p">,</span> <span class="n">max_s</span> <span class="o">=</span> <span class="n">unpad_input</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">key_padding_mask</span><span class="p">)</span> + <span class="n">x_unpad</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span> + <span class="n">x_unpad</span><span class="p">,</span> <span class="s2">&quot;nnz (three h d) -&gt; nnz three h d&quot;</span><span class="p">,</span> <span class="n">three</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">h</span><span class="o">=</span><span class="n">nheads</span> + <span class="p">)</span> + <span class="n">output_unpad</span> <span class="o">=</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span><span class="p">(</span> + <span class="n">x_unpad</span><span class="p">,</span> <span class="n">cu_q_lens</span><span class="p">,</span> <span class="n">max_s</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span> + <span class="n">pad_input</span><span class="p">(</span> + <span class="n">rearrange</span><span class="p">(</span><span class="n">output_unpad</span><span class="p">,</span> <span class="s2">&quot;nnz h d -&gt; nnz (h d)&quot;</span><span class="p">),</span> <span class="n">indices</span><span class="p">,</span> <span class="n">bsz</span><span class="p">,</span> <span class="n">q_len</span> + <span class="p">),</span> + <span class="s2">&quot;b s (h d) -&gt; b s h d&quot;</span><span class="p">,</span> + <span class="n">h</span><span class="o">=</span><span class="n">nheads</span><span class="p">,</span> + <span class="p">)</span> + <span class="c1"># if self.reorder_and_upcast_attn:</span> + <span class="c1"># attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask)</span> + <span class="c1"># else:</span> + <span class="c1"># attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="s1">&#39;b s h d -&gt; b h s d&#39;</span><span class="p">)</span> + <span class="n">attn_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_merge_heads</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + <span class="n">attn_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_proj</span><span class="p">(</span><span class="n">attn_output</span><span class="p">)</span> + <span class="n">attn_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resid_dropout</span><span class="p">(</span><span class="n">attn_output</span><span class="p">)</span> + + <span class="n">outputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">attn_output</span><span class="p">,</span> <span class="n">present</span><span class="p">)</span> + + <span class="k">assert</span> <span class="n">output_attentions</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">,</span> <span class="s2">&quot;output attentions is not supported yet&quot;</span> + <span class="c1"># if output_attentions:</span> + <span class="c1"># outputs += (attn_weights,)</span> + + <span class="k">return</span> <span class="n">outputs</span> <span class="c1"># a, present, (attentions)</span></div> + + +<span class="c1"># Disable the transformation of the attention mask in LlamaModel as the flash attention</span> +<span class="c1"># requires the attention mask to be the same as the key_padding_mask</span> +<div class="viewcode-block" id="_prepare_decoder_attention_mask"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.html#lmflow.utils.flash_attention.gpt2_flash_attention._prepare_decoder_attention_mask">[docs]</a><span class="k">def</span> <span class="nf">_prepare_decoder_attention_mask</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">attention_mask</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">,</span> <span class="n">inputs_embeds</span><span class="p">,</span> <span class="n">past_key_values_length</span> +<span class="p">):</span> + <span class="c1"># [bsz, seq_len]</span> + <span class="k">return</span> <span class="n">attention_mask</span></div> + + +<div class="viewcode-block" id="replace_gpt2_attn_with_flash_attn"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.html#lmflow.utils.flash_attention.gpt2_flash_attention.replace_gpt2_attn_with_flash_attn">[docs]</a><span class="k">def</span> <span class="nf">replace_gpt2_attn_with_flash_attn</span><span class="p">():</span> + <span class="c1"># transformers.models.gpt2.modeling_gpt2.LlamaModel._prepare_decoder_attention_mask = (</span> + <span class="c1"># _prepare_decoder_attention_mask</span> + <span class="c1"># )</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">gpt2</span><span class="o">.</span><span class="n">modeling_gpt2</span><span class="o">.</span><span class="n">GPT2Attention</span><span class="o">.</span><span class="n">forward</span> <span class="o">=</span> <span class="n">forward</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div class="footer-item"> + <p class="copyright"> + + © Copyright LMFlow 2023. + <br/> + + </p> +</div> + + <div class="footer-item"> 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class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.utils.flash_attention.gpt_neo_flash_attention</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.utils.flash_attention.gpt_neo_flash_attention</h1><div class="highlight"><pre> +<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span> + +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">transformers</span> +<span class="kn">from</span> <span class="nn">einops</span> <span class="kn">import</span> <span class="n">rearrange</span> + +<span class="kn">import</span> <span class="nn">flash_attn</span> +<span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">flash_attn</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">flash_attn.flash_attn_interface</span> <span class="kn">import</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span> +<span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">flash_attn</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">flash_attn.flash_attn_interface</span> <span class="kn">import</span> <span class="n">flash_attn_varlen_qkvpacked_func</span> <span class="k">as</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span> + +<span class="kn">from</span> <span class="nn">flash_attn.bert_padding</span> <span class="kn">import</span> <span class="n">unpad_input</span><span class="p">,</span> <span class="n">pad_input</span> + +<div class="viewcode-block" id="_attn"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.html#lmflow.utils.flash_attention.gpt_neo_flash_attention._attn">[docs]</a><span class="k">def</span> <span class="nf">_attn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">query</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">attention_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">head_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="c1"># (batch, head, seq_length, head_features)</span> + <span class="n">query</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">)</span> + <span class="n">key</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">)</span> + <span class="n">query</span> <span class="o">=</span> <span class="n">query</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">))</span> + <span class="n">qkv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span> + <span class="p">[</span><span class="n">query</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span> + <span class="p">)</span><span class="c1"># [bsz, nh, 3, t, hd]</span> + <span class="n">qkv</span> <span class="o">=</span> <span class="n">qkv</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="c1">## [bsz, q_len, 3, nh, hd]</span> + <span class="n">bsz</span> <span class="o">=</span> <span class="n">qkv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + <span class="n">q_len</span> <span class="o">=</span> <span class="n">qkv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> + + <span class="n">attention_mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">attention_mask</span> <span class="o">==</span> <span class="o">-</span><span class="mf">0.0</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> + <span class="n">key_padding_mask</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">attention_mask</span><span class="p">,</span> <span class="s2">&quot;b () () s -&gt; b s&quot;</span><span class="p">)</span> <span class="k">if</span> <span class="n">attention_mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span> + <span class="k">if</span> <span class="n">key_padding_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">qkv</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">qkv</span><span class="p">,</span> <span class="s2">&quot;b s ... -&gt; (b s) ...&quot;</span><span class="p">)</span> + <span class="n">max_s</span> <span class="o">=</span> <span class="n">q_len</span> + <span class="n">cu_q_lens</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span> + <span class="mi">0</span><span class="p">,</span> <span class="p">(</span><span class="n">bsz</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">q_len</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="n">q_len</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">qkv</span><span class="o">.</span><span class="n">device</span> + <span class="p">)</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span><span class="p">(</span> + <span class="n">qkv</span><span class="p">,</span> <span class="n">cu_q_lens</span><span class="p">,</span> <span class="n">max_s</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">attn_dropout</span><span class="o">.</span><span class="n">p</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="k">else</span> <span class="mf">0.0</span> <span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span><span class="c1"># attention compute</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="s2">&quot;(b s) ... -&gt; b s ...&quot;</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">bsz</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">nheads</span> <span class="o">=</span> <span class="n">qkv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> + <span class="n">x</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">qkv</span><span class="p">,</span> <span class="s2">&quot;b s three h d -&gt; b s (three h d)&quot;</span><span class="p">)</span> + <span class="n">x_unpad</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">cu_q_lens</span><span class="p">,</span> <span class="n">max_s</span> <span class="o">=</span> <span class="n">unpad_input</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">key_padding_mask</span><span class="p">)</span> + <span class="n">x_unpad</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span> + <span class="n">x_unpad</span><span class="p">,</span> <span class="s2">&quot;nnz (three h d) -&gt; nnz three h d&quot;</span><span class="p">,</span> <span class="n">three</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">h</span><span class="o">=</span><span class="n">nheads</span> + <span class="p">)</span> + <span class="n">output_unpad</span> <span class="o">=</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span><span class="p">(</span> + <span class="n">x_unpad</span><span class="p">,</span> <span class="n">cu_q_lens</span><span class="p">,</span> <span class="n">max_s</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">attn_dropout</span><span class="o">.</span><span class="n">p</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="k">else</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span> + <span class="n">pad_input</span><span class="p">(</span> + <span class="n">rearrange</span><span class="p">(</span><span class="n">output_unpad</span><span class="p">,</span> <span class="s2">&quot;nnz h d -&gt; nnz (h d)&quot;</span><span class="p">),</span> <span class="n">indices</span><span class="p">,</span> <span class="n">bsz</span><span class="p">,</span> <span class="n">q_len</span> + <span class="p">),</span> + <span class="s2">&quot;b s (h d) -&gt; b s h d&quot;</span><span class="p">,</span> + <span class="n">h</span><span class="o">=</span><span class="n">nheads</span><span class="p">,</span> + <span class="p">)</span> + + <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="kc">None</span></div> + +<div class="viewcode-block" id="forward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.html#lmflow.utils.flash_attention.gpt_neo_flash_attention.forward">[docs]</a><span class="k">def</span> <span class="nf">forward</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">hidden_states</span><span class="p">,</span> + <span class="n">attention_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">layer_past</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">head_mask</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> + <span class="n">use_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">output_attentions</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="p">):</span> + + <span class="k">assert</span> <span class="n">head_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;head_mask is not supported&quot;</span> + <span class="k">assert</span> <span class="ow">not</span> <span class="n">output_attentions</span><span class="p">,</span> <span class="s2">&quot;output_attentions is not supported&quot;</span> + <span class="k">assert</span> <span class="ow">not</span> <span class="n">use_cache</span><span class="p">,</span> <span class="s2">&quot;use_cache is not supported&quot;</span> + + <span class="n">query</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">q_proj</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> + <span class="n">key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_proj</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> + <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">v_proj</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> + + <span class="n">query</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_split_heads</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + <span class="n">key</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_split_heads</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + <span class="n">value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_split_heads</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + + <span class="k">if</span> <span class="n">layer_past</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">past_key</span> <span class="o">=</span> <span class="n">layer_past</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> + <span class="n">past_value</span> <span class="o">=</span> <span class="n">layer_past</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> + <span class="n">key</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">past_key</span><span class="p">,</span> <span class="n">key</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">)</span> + <span class="n">value</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">past_value</span><span class="p">,</span> <span class="n">value</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">)</span> + + <span class="n">present</span> <span class="o">=</span> <span class="kc">None</span> + <span class="n">attn_output</span><span class="p">,</span> <span class="n">attn_weights</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_attn</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">attention_mask</span><span class="p">,</span> <span class="n">head_mask</span><span class="p">)</span> + <span class="n">new_shape</span> <span class="o">=</span> <span class="n">attn_output</span><span class="o">.</span><span class="n">size</span><span class="p">()[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">,)</span> + <span class="n">attn_output</span> <span class="o">=</span> <span class="n">attn_output</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">new_shape</span><span class="p">)</span> + <span class="n">attn_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">out_proj</span><span class="p">(</span><span class="n">attn_output</span><span class="p">)</span> + <span class="n">attn_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resid_dropout</span><span class="p">(</span><span class="n">attn_output</span><span class="p">)</span> + + <span class="n">outputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">attn_output</span><span class="p">,</span> <span class="n">present</span><span class="p">)</span> + + <span class="k">return</span> <span class="n">outputs</span> <span class="c1"># a, present, (attentions)</span></div> + +<div class="viewcode-block" id="replace_gpt_neo_attn_with_flash_attn"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.html#lmflow.utils.flash_attention.gpt_neo_flash_attention.replace_gpt_neo_attn_with_flash_attn">[docs]</a><span class="k">def</span> <span class="nf">replace_gpt_neo_attn_with_flash_attn</span><span class="p">():</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">gpt_neo</span><span class="o">.</span><span class="n">modeling_gpt_neo</span><span class="o">.</span><span class="n">GPTNeoSelfAttention</span><span class="o">.</span><span class="n">_attn</span> <span class="o">=</span> <span class="n">_attn</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">gpt_neo</span><span class="o">.</span><span class="n">modeling_gpt_neo</span><span class="o">.</span><span class="n">GPTNeoSelfAttention</span><span class="o">.</span><span class="n">forward</span> <span class="o">=</span> <span class="n">forward</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div 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class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.utils.flash_attention.llama_flash_attention</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.utils.flash_attention.llama_flash_attention</h1><div class="highlight"><pre> +<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span> + +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span> + +<span class="kn">import</span> <span class="nn">transformers</span> +<span class="kn">from</span> <span class="nn">transformers.models.llama.modeling_llama</span> <span class="kn">import</span> <span class="n">apply_rotary_pos_emb</span> + +<span class="kn">from</span> <span class="nn">einops</span> <span class="kn">import</span> <span class="n">rearrange</span> + +<span class="kn">import</span> <span class="nn">flash_attn</span> +<span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">flash_attn</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">flash_attn.flash_attn_interface</span> <span class="kn">import</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span> +<span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">flash_attn</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> + <span class="kn">from</span> <span class="nn">flash_attn.flash_attn_interface</span> <span class="kn">import</span> <span class="n">flash_attn_varlen_qkvpacked_func</span> <span class="k">as</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span> + +<span class="kn">from</span> <span class="nn">flash_attn.bert_padding</span> <span class="kn">import</span> <span class="n">unpad_input</span><span class="p">,</span> <span class="n">pad_input</span> + + +<div class="viewcode-block" id="forward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.html#lmflow.utils.flash_attention.llama_flash_attention.forward">[docs]</a><span class="k">def</span> <span class="nf">forward</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> + <span class="n">hidden_states</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> + <span class="n">attention_mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">position_ids</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">past_key_value</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> + <span class="n">output_attentions</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> + <span class="n">use_cache</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> +<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]]:</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;Input shape: Batch x Time x Channel</span> + +<span class="sd"> attention_mask: [bsz, q_len]</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="n">bsz</span><span class="p">,</span> <span class="n">q_len</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">hidden_states</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> + + <span class="n">query_states</span> <span class="o">=</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">q_proj</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> + <span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">bsz</span><span class="p">,</span> <span class="n">q_len</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + <span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> + <span class="p">)</span> + <span class="n">key_states</span> <span class="o">=</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">k_proj</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> + <span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">bsz</span><span class="p">,</span> <span class="n">q_len</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + <span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> + <span class="p">)</span> + <span class="n">value_states</span> <span class="o">=</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">v_proj</span><span class="p">(</span><span class="n">hidden_states</span><span class="p">)</span> + <span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">bsz</span><span class="p">,</span> <span class="n">q_len</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">head_dim</span><span class="p">)</span> + <span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> + <span class="p">)</span> + <span class="c1"># [bsz, q_len, nh, hd]</span> + <span class="c1"># [bsz, nh, q_len, hd]</span> + + <span class="n">kv_seq_len</span> <span class="o">=</span> <span class="n">key_states</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> + <span class="k">assert</span> <span class="n">past_key_value</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;past_key_value is not supported&quot;</span> + + <span class="n">cos</span><span class="p">,</span> <span class="n">sin</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rotary_emb</span><span class="p">(</span><span class="n">value_states</span><span class="p">,</span> <span class="n">seq_len</span><span class="o">=</span><span class="n">kv_seq_len</span><span class="p">)</span> + <span class="n">query_states</span><span class="p">,</span> <span class="n">key_states</span> <span class="o">=</span> <span class="n">apply_rotary_pos_emb</span><span class="p">(</span> + <span class="n">query_states</span><span class="p">,</span> <span class="n">key_states</span><span class="p">,</span> <span class="n">cos</span><span class="p">,</span> <span class="n">sin</span><span class="p">,</span> <span class="n">position_ids</span> + <span class="p">)</span> + <span class="c1"># [bsz, nh, t, hd]</span> + <span class="k">assert</span> <span class="ow">not</span> <span class="n">output_attentions</span><span class="p">,</span> <span class="s2">&quot;output_attentions is not supported&quot;</span> + <span class="k">assert</span> <span class="ow">not</span> <span class="n">use_cache</span><span class="p">,</span> <span class="s2">&quot;use_cache is not supported&quot;</span> + + <span class="c1"># Flash attention codes from</span> + <span class="c1"># https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py</span> + + <span class="c1"># transform the data into the format required by flash attention</span> + <span class="n">qkv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span> + <span class="p">[</span><span class="n">query_states</span><span class="p">,</span> <span class="n">key_states</span><span class="p">,</span> <span class="n">value_states</span><span class="p">],</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span> + <span class="p">)</span> <span class="c1"># [bsz, nh, 3, q_len, hd]</span> + <span class="n">qkv</span> <span class="o">=</span> <span class="n">qkv</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="c1"># [bsz, q_len, 3, nh, hd]</span> + <span class="c1"># We have disabled _prepare_decoder_attention_mask in LlamaModel</span> + <span class="c1"># the attention_mask should be the same as the key_padding_mask</span> + <span class="n">key_padding_mask</span> <span class="o">=</span> <span class="n">attention_mask</span> + + <span class="k">if</span> <span class="n">key_padding_mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> + <span class="n">qkv</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">qkv</span><span class="p">,</span> <span class="s2">&quot;b s ... -&gt; (b s) ...&quot;</span><span class="p">)</span> + <span class="n">max_s</span> <span class="o">=</span> <span class="n">q_len</span> + <span class="n">cu_q_lens</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span> + <span class="mi">0</span><span class="p">,</span> <span class="p">(</span><span class="n">bsz</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">q_len</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="n">q_len</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">qkv</span><span class="o">.</span><span class="n">device</span> + <span class="p">)</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span><span class="p">(</span> + <span class="n">qkv</span><span class="p">,</span> <span class="n">cu_q_lens</span><span class="p">,</span> <span class="n">max_s</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="s2">&quot;(b s) ... -&gt; b s ...&quot;</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">bsz</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">nheads</span> <span class="o">=</span> <span class="n">qkv</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> + <span class="n">x</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span><span class="n">qkv</span><span class="p">,</span> <span class="s2">&quot;b s three h d -&gt; b s (three h d)&quot;</span><span class="p">)</span> + <span class="n">x_unpad</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">cu_q_lens</span><span class="p">,</span> <span class="n">max_s</span> <span class="o">=</span> <span class="n">unpad_input</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">key_padding_mask</span><span class="p">)</span> + <span class="n">x_unpad</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span> + <span class="n">x_unpad</span><span class="p">,</span> <span class="s2">&quot;nnz (three h d) -&gt; nnz three h d&quot;</span><span class="p">,</span> <span class="n">three</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">h</span><span class="o">=</span><span class="n">nheads</span> + <span class="p">)</span> + <span class="n">output_unpad</span> <span class="o">=</span> <span class="n">flash_attn_unpadded_qkvpacked_func</span><span class="p">(</span> + <span class="n">x_unpad</span><span class="p">,</span> <span class="n">cu_q_lens</span><span class="p">,</span> <span class="n">max_s</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">True</span> + <span class="p">)</span> + <span class="n">output</span> <span class="o">=</span> <span class="n">rearrange</span><span class="p">(</span> + <span class="n">pad_input</span><span class="p">(</span> + <span class="n">rearrange</span><span class="p">(</span><span class="n">output_unpad</span><span class="p">,</span> <span class="s2">&quot;nnz h d -&gt; nnz (h d)&quot;</span><span class="p">),</span> <span class="n">indices</span><span class="p">,</span> <span class="n">bsz</span><span class="p">,</span> <span class="n">q_len</span> + <span class="p">),</span> + <span class="s2">&quot;b s (h d) -&gt; b s h d&quot;</span><span class="p">,</span> + <span class="n">h</span><span class="o">=</span><span class="n">nheads</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">o_proj</span><span class="p">(</span><span class="n">rearrange</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="s2">&quot;b s h d -&gt; b s (h d)&quot;</span><span class="p">)),</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span></div> + + +<span class="c1"># Disable the transformation of the attention mask in LlamaModel as the flash attention</span> +<span class="c1"># requires the attention mask to be the same as the key_padding_mask</span> +<div class="viewcode-block" id="_prepare_decoder_attention_mask"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.html#lmflow.utils.flash_attention.llama_flash_attention._prepare_decoder_attention_mask">[docs]</a><span class="k">def</span> <span class="nf">_prepare_decoder_attention_mask</span><span class="p">(</span> + <span class="bp">self</span><span class="p">,</span> <span class="n">attention_mask</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">,</span> <span class="n">inputs_embeds</span><span class="p">,</span> <span class="n">past_key_values_length</span> +<span class="p">):</span> + <span class="c1"># [bsz, seq_len]</span> + <span class="k">return</span> <span class="n">attention_mask</span></div> + + +<div class="viewcode-block" id="replace_llama_attn_with_flash_attn"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.html#lmflow.utils.flash_attention.llama_flash_attention.replace_llama_attn_with_flash_attn">[docs]</a><span class="k">def</span> <span class="nf">replace_llama_attn_with_flash_attn</span><span class="p">():</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">llama</span><span class="o">.</span><span class="n">modeling_llama</span><span class="o">.</span><span class="n">LlamaModel</span><span class="o">.</span><span class="n">_prepare_decoder_attention_mask</span> <span class="o">=</span> <span class="p">(</span> + <span class="n">_prepare_decoder_attention_mask</span> + <span class="p">)</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">llama</span><span class="o">.</span><span class="n">modeling_llama</span><span class="o">.</span><span class="n">LlamaAttention</span><span class="o">.</span><span class="n">forward</span> <span class="o">=</span> <span class="n">forward</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script 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+<div class="header-article-items header-article__inner"> + + <div class="header-article-items__start"> + + <div class="header-article-item"> + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.utils.flash_attention.triton_flash_attention</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.utils.flash_attention.triton_flash_attention</h1><div class="highlight"><pre> +<span></span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd">*Experimental* implementation of FlashAttention in Triton.</span> +<span class="sd">Tested with triton==2.0.0.dev20221202.</span> +<span class="sd">Triton 2.0 has a new backend (MLIR) but seems like it doesn&#39;t yet work for head dimensions</span> +<span class="sd">other than 64:</span> +<span class="sd">https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207</span> +<span class="sd">We&#39;ll update this implementation with the new Triton backend once this is fixed.</span> + +<span class="sd">We use the FlashAttention implementation from Phil Tillet a starting point.</span> +<span class="sd">https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py</span> + +<span class="sd">Changes:</span> +<span class="sd">- Implement both causal and non-causal attention.</span> +<span class="sd">- Implement both self-attention and cross-attention.</span> +<span class="sd">- Support arbitrary seqlens (not just multiples of 128), for both forward and backward.</span> +<span class="sd">- Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward.</span> +<span class="sd">- Support attention bias.</span> +<span class="sd">- Speed up the forward pass a bit, and only store the LSE instead of m and l.</span> +<span class="sd">- Make the backward for d=128 much faster by reducing register spilling.</span> +<span class="sd">- Optionally parallelize the backward pass across seqlen_k, to deal with the case of</span> +<span class="sd">small batch size * nheads.</span> + +<span class="sd">Caution:</span> +<span class="sd">- This is an *experimental* implementation. The forward pass should be quite robust but</span> +<span class="sd">I&#39;m not 100% sure that the backward pass doesn&#39;t have race conditions (due to the Triton compiler).</span> +<span class="sd">- This implementation has only been tested on A100.</span> +<span class="sd">- If you plan to use headdim other than 64 and 128, you should test for race conditions</span> +<span class="sd">(due to the Triton compiler), as done in tests/test_flash_attn.py</span> +<span class="sd">&quot;test_flash_attn_triton_race_condition&quot;. I&#39;ve tested and fixed many race conditions</span> +<span class="sd">for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I&#39;m still not 100% confident</span> +<span class="sd">that there are none left for other head dimensions.</span> + +<span class="sd">Differences between this Triton version and the CUDA version:</span> +<span class="sd">- Triton version doesn&#39;t support dropout.</span> +<span class="sd">- Triton forward is generally faster than CUDA forward, while Triton backward is</span> +<span class="sd">generally slower than CUDA backward. Overall Triton forward + backward is slightly slower</span> +<span class="sd">than CUDA forward + backward.</span> +<span class="sd">- Triton version doesn&#39;t support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor).</span> +<span class="sd">- Triton version supports attention bias, while CUDA version doesn&#39;t.</span> +<span class="sd">&quot;&quot;&quot;</span> + +<span class="kn">import</span> <span class="nn">math</span> + +<span class="kn">import</span> <span class="nn">torch</span> + +<span class="kn">import</span> <span class="nn">triton</span> +<span class="kn">import</span> <span class="nn">triton.language</span> <span class="k">as</span> <span class="nn">tl</span> + + +<span class="c1"># Disabling autotune for now, set num_warps=4 if headdim=64 and num_warps=8 if headdim=128</span> +<span class="c1"># @triton.autotune(</span> +<span class="c1"># configs=[</span> +<span class="c1"># triton.Config({&quot;BLOCK_M&quot;: 128, &quot;BLOCK_N&quot;: 128}, num_warps=4, num_stages=1),</span> +<span class="c1"># # This config has a race condition when EVEN_M == False, disabling it for now.</span> +<span class="c1"># # triton.Config({&quot;BLOCK_M&quot;: 64, &quot;BLOCK_N&quot;: 64}, num_warps=4, num_stages=1),</span> +<span class="c1"># ],</span> +<span class="c1"># key=[&#39;CACHE_KEY_SEQLEN_Q&#39;, &#39;CACHE_KEY_SEQLEN_K&#39;, &#39;BIAS_TYPE&#39;, &#39;IS_CAUSAL&#39;, &#39;BLOCK_HEADDIM&#39;]</span> +<span class="c1"># )</span> +<span class="nd">@triton</span><span class="o">.</span><span class="n">heuristics</span><span class="p">(</span> + <span class="p">{</span> + <span class="s2">&quot;EVEN_M&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">args</span><span class="p">:</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;seqlen_q&quot;</span><span class="p">]</span> <span class="o">%</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;BLOCK_M&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> + <span class="s2">&quot;EVEN_N&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">args</span><span class="p">:</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;seqlen_k&quot;</span><span class="p">]</span> <span class="o">%</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;BLOCK_N&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> + <span class="s2">&quot;EVEN_HEADDIM&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">args</span><span class="p">:</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;headdim&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;BLOCK_HEADDIM&quot;</span><span class="p">],</span> + <span class="p">}</span> +<span class="p">)</span> +<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span> +<div class="viewcode-block" id="_fwd_kernel"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._fwd_kernel">[docs]</a><span class="k">def</span> <span class="nf">_fwd_kernel</span><span class="p">(</span> + <span class="n">Q</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Bias</span><span class="p">,</span> <span class="n">Out</span><span class="p">,</span> + <span class="n">Lse</span><span class="p">,</span> <span class="n">TMP</span><span class="p">,</span> <span class="c1"># NOTE: TMP is a scratchpad buffer to workaround a compiler bug</span> + <span class="n">softmax_scale</span><span class="p">,</span> + <span class="n">stride_qb</span><span class="p">,</span> <span class="n">stride_qh</span><span class="p">,</span> <span class="n">stride_qm</span><span class="p">,</span> + <span class="n">stride_kb</span><span class="p">,</span> <span class="n">stride_kh</span><span class="p">,</span> <span class="n">stride_kn</span><span class="p">,</span> + <span class="n">stride_vb</span><span class="p">,</span> <span class="n">stride_vh</span><span class="p">,</span> <span class="n">stride_vn</span><span class="p">,</span> + <span class="n">stride_bb</span><span class="p">,</span> <span class="n">stride_bh</span><span class="p">,</span> <span class="n">stride_bm</span><span class="p">,</span> + <span class="n">stride_ob</span><span class="p">,</span> <span class="n">stride_oh</span><span class="p">,</span> <span class="n">stride_om</span><span class="p">,</span> + <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">seqlen_q_rounded</span><span class="p">,</span> <span class="n">headdim</span><span class="p">,</span> + <span class="n">CACHE_KEY_SEQLEN_Q</span><span class="p">,</span> <span class="n">CACHE_KEY_SEQLEN_K</span><span class="p">,</span> + <span class="n">BIAS_TYPE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">IS_CAUSAL</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">BLOCK_HEADDIM</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">EVEN_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">EVEN_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">BLOCK_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> +<span class="p">):</span> + <span class="n">start_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> + <span class="n">off_hb</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> + <span class="n">off_b</span> <span class="o">=</span> <span class="n">off_hb</span> <span class="o">//</span> <span class="n">nheads</span> + <span class="n">off_h</span> <span class="o">=</span> <span class="n">off_hb</span> <span class="o">%</span> <span class="n">nheads</span> + <span class="c1"># off_b = tl.program_id(1)</span> + <span class="c1"># off_h = tl.program_id(2)</span> + <span class="c1"># off_hb = off_b * nheads + off_h</span> + <span class="c1"># initialize offsets</span> + <span class="n">offs_m</span> <span class="o">=</span> <span class="n">start_m</span> <span class="o">*</span> <span class="n">BLOCK_M</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_M</span><span class="p">)</span> + <span class="n">offs_n</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="p">)</span> + <span class="n">offs_d</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">)</span> + <span class="c1"># Initialize pointers to Q, K, V</span> + <span class="c1"># Adding parenthesis around indexing might use int32 math instead of int64 math?</span> + <span class="c1"># https://github.com/openai/triton/issues/741</span> + <span class="c1"># I&#39;m seeing a tiny bit of difference (5-7us)</span> + <span class="n">q_ptrs</span> <span class="o">=</span> <span class="n">Q</span> <span class="o">+</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_qb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_qh</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_qm</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">k_ptrs</span> <span class="o">=</span> <span class="n">K</span> <span class="o">+</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_kb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_kh</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_kn</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">v_ptrs</span> <span class="o">=</span> <span class="n">V</span> <span class="o">+</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_vb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_vh</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_vn</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="k">if</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;vector&#39;</span><span class="p">:</span> + <span class="n">b_ptrs</span> <span class="o">=</span> <span class="n">Bias</span> <span class="o">+</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_bb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_bh</span> <span class="o">+</span> <span class="n">offs_n</span> + <span class="k">elif</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;matrix&#39;</span><span class="p">:</span> + <span class="n">b_ptrs</span> <span class="o">=</span> <span class="n">Bias</span> <span class="o">+</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_bb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_bh</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_bm</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="c1"># initialize pointer to m and l</span> + <span class="n">t_ptrs</span> <span class="o">=</span> <span class="n">TMP</span> <span class="o">+</span> <span class="n">off_hb</span> <span class="o">*</span> <span class="n">seqlen_q_rounded</span> <span class="o">+</span> <span class="n">offs_m</span> + <span class="n">lse_i</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">BLOCK_M</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> <span class="o">-</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">)</span> + <span class="n">m_i</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">BLOCK_M</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> <span class="o">-</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">)</span> + <span class="n">acc_o</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">BLOCK_M</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="c1"># load q: it will stay in SRAM throughout</span> + <span class="c1"># [2022-10-30] TD: Triton bug - in the case of EVEN_M=True and EVEN_N=False, if we just call</span> + <span class="c1"># tl.load(q_ptrs), we get the wrong output!</span> + <span class="k">if</span> <span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_N</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">q</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">q_ptrs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">q</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">q_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">q</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">q_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">q</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">q_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="c1"># loop over k, v and update accumulator</span> + <span class="n">end_n</span> <span class="o">=</span> <span class="n">seqlen_k</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">IS_CAUSAL</span> <span class="k">else</span> <span class="n">tl</span><span class="o">.</span><span class="n">minimum</span><span class="p">((</span><span class="n">start_m</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">BLOCK_M</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">)</span> + <span class="k">for</span> <span class="n">start_n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">end_n</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="p">):</span> + <span class="n">start_n</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">multiple_of</span><span class="p">(</span><span class="n">start_n</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="p">)</span> + <span class="c1"># -- compute qk ----</span> + <span class="k">if</span> <span class="n">EVEN_N</span> <span class="o">&amp;</span> <span class="n">EVEN_M</span><span class="p">:</span> <span class="c1"># If we just do &quot;if EVEN_N&quot;, there seems to be some race condition</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">k_ptrs</span> <span class="o">+</span> <span class="n">start_n</span> <span class="o">*</span> <span class="n">stride_kn</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">k_ptrs</span> <span class="o">+</span> <span class="n">start_n</span> <span class="o">*</span> <span class="n">stride_kn</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">k_ptrs</span> <span class="o">+</span> <span class="n">start_n</span> <span class="o">*</span> <span class="n">stride_kn</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">start_n</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">,</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">k_ptrs</span> <span class="o">+</span> <span class="n">start_n</span> <span class="o">*</span> <span class="n">stride_kn</span><span class="p">,</span> + <span class="n">mask</span><span class="o">=</span><span class="p">((</span><span class="n">start_n</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="n">qk</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">BLOCK_M</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">qk</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">trans_b</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="c1"># Trying to combine the two masks seem to make the result wrong</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">EVEN_N</span><span class="p">:</span> <span class="c1"># Need to mask out otherwise the softmax is wrong</span> + <span class="n">qk</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">start_n</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;-inf&quot;</span><span class="p">))</span> + <span class="k">if</span> <span class="n">IS_CAUSAL</span><span class="p">:</span> + <span class="n">qk</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="n">start_n</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:],</span> <span class="mi">0</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;-inf&quot;</span><span class="p">))</span> + <span class="k">if</span> <span class="n">BIAS_TYPE</span> <span class="o">!=</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;vector&#39;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_N</span><span class="p">:</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span> <span class="o">+</span> <span class="n">start_n</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span> <span class="o">+</span> <span class="n">start_n</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">start_n</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">bias</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> + <span class="k">elif</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;matrix&#39;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_N</span><span class="p">:</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span> <span class="o">+</span> <span class="n">start_n</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span> <span class="o">+</span> <span class="n">start_n</span><span class="p">,</span> + <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> + <span class="o">&amp;</span> <span class="p">((</span><span class="n">start_n</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">),</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="c1"># Slightly faster to multiply the softmax_scale in the tl.exp below since the compiler</span> + <span class="c1"># can then fuse the mult and add into an fma instruction. But if we have bias we need to</span> + <span class="c1"># to multiply with softmax_scale here.</span> + <span class="n">qk</span> <span class="o">=</span> <span class="n">qk</span> <span class="o">*</span> <span class="n">softmax_scale</span> <span class="o">+</span> <span class="n">bias</span> + <span class="n">m_ij</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">qk</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">lse_i</span><span class="p">)</span> + <span class="n">p</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">qk</span> <span class="o">-</span> <span class="n">m_ij</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">])</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">m_ij</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">qk</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">softmax_scale</span><span class="p">,</span> <span class="n">lse_i</span><span class="p">)</span> + <span class="n">p</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">qk</span> <span class="o">*</span> <span class="n">softmax_scale</span> <span class="o">-</span> <span class="n">m_ij</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">])</span> + <span class="n">l_ij</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> + + <span class="c1"># scale acc_o</span> + <span class="n">acc_o_scale</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">m_i</span> <span class="o">-</span> <span class="n">m_ij</span><span class="p">)</span> + + <span class="c1"># # -- update output accumulator --</span> + <span class="c1"># BUG: have to store and immediately load</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">t_ptrs</span><span class="p">,</span> <span class="n">acc_o_scale</span><span class="p">)</span> + <span class="n">acc_o_scale</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">t_ptrs</span><span class="p">)</span> + <span class="n">acc_o</span> <span class="o">=</span> <span class="n">acc_o</span> <span class="o">*</span> <span class="n">acc_o_scale</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> + <span class="c1"># update acc_o</span> + <span class="k">if</span> <span class="n">EVEN_N</span> <span class="o">&amp;</span> <span class="n">EVEN_M</span><span class="p">:</span> <span class="c1"># If we just do &quot;if EVEN_N&quot;, there seems to be some race condition</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">v</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">v_ptrs</span> <span class="o">+</span> <span class="n">start_n</span> <span class="o">*</span> <span class="n">stride_vn</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">v</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">v_ptrs</span> <span class="o">+</span> <span class="n">start_n</span> <span class="o">*</span> <span class="n">stride_vn</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">v</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">v_ptrs</span> <span class="o">+</span> <span class="n">start_n</span> <span class="o">*</span> <span class="n">stride_vn</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">start_n</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">,</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">v</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">v_ptrs</span> <span class="o">+</span> <span class="n">start_n</span> <span class="o">*</span> <span class="n">stride_vn</span><span class="p">,</span> + <span class="n">mask</span><span class="o">=</span><span class="p">((</span><span class="n">start_n</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="n">p</span> <span class="o">=</span> <span class="n">p</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> + <span class="n">acc_o</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> + + <span class="c1"># -- update statistics</span> + <span class="n">m_i</span> <span class="o">=</span> <span class="n">m_ij</span> + <span class="n">l_i_new</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">lse_i</span> <span class="o">-</span> <span class="n">m_ij</span><span class="p">)</span> <span class="o">+</span> <span class="n">l_ij</span> + <span class="n">lse_i</span> <span class="o">=</span> <span class="n">m_ij</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">l_i_new</span><span class="p">)</span> + + <span class="n">o_scale</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">m_i</span> <span class="o">-</span> <span class="n">lse_i</span><span class="p">)</span> + <span class="c1"># BUG: have to store and immediately load</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">t_ptrs</span><span class="p">,</span> <span class="n">o_scale</span><span class="p">)</span> + <span class="n">o_scale</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">t_ptrs</span><span class="p">)</span> + <span class="n">acc_o</span> <span class="o">=</span> <span class="n">acc_o</span> <span class="o">*</span> <span class="n">o_scale</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> + <span class="c1"># rematerialize offsets to save registers</span> + <span class="n">start_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> + <span class="n">offs_m</span> <span class="o">=</span> <span class="n">start_m</span> <span class="o">*</span> <span class="n">BLOCK_M</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_M</span><span class="p">)</span> + <span class="c1"># write back l and m</span> + <span class="n">lse_ptrs</span> <span class="o">=</span> <span class="n">Lse</span> <span class="o">+</span> <span class="n">off_hb</span> <span class="o">*</span> <span class="n">seqlen_q_rounded</span> <span class="o">+</span> <span class="n">offs_m</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">lse_ptrs</span><span class="p">,</span> <span class="n">lse_i</span><span class="p">)</span> + <span class="c1"># initialize pointers to output</span> + <span class="n">offs_d</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">)</span> + <span class="n">out_ptrs</span> <span class="o">=</span> <span class="n">Out</span> <span class="o">+</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_ob</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_oh</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_om</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="k">if</span> <span class="n">EVEN_M</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">out_ptrs</span><span class="p">,</span> <span class="n">acc_o</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">out_ptrs</span><span class="p">,</span> <span class="n">acc_o</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">out_ptrs</span><span class="p">,</span> <span class="n">acc_o</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">out_ptrs</span><span class="p">,</span> <span class="n">acc_o</span><span class="p">,</span> + <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">))</span></div> + + +<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span> +<div class="viewcode-block" id="_bwd_preprocess_do_o_dot"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._bwd_preprocess_do_o_dot">[docs]</a><span class="k">def</span> <span class="nf">_bwd_preprocess_do_o_dot</span><span class="p">(</span> + <span class="n">Out</span><span class="p">,</span> <span class="n">DO</span><span class="p">,</span> <span class="n">Delta</span><span class="p">,</span> + <span class="n">stride_ob</span><span class="p">,</span> <span class="n">stride_oh</span><span class="p">,</span> <span class="n">stride_om</span><span class="p">,</span> + <span class="n">stride_dob</span><span class="p">,</span> <span class="n">stride_doh</span><span class="p">,</span> <span class="n">stride_dom</span><span class="p">,</span> + <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_q_rounded</span><span class="p">,</span> <span class="n">headdim</span><span class="p">,</span> + <span class="n">BLOCK_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> +<span class="p">):</span> + <span class="n">start_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> + <span class="n">off_hb</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> + <span class="n">off_b</span> <span class="o">=</span> <span class="n">off_hb</span> <span class="o">//</span> <span class="n">nheads</span> + <span class="n">off_h</span> <span class="o">=</span> <span class="n">off_hb</span> <span class="o">%</span> <span class="n">nheads</span> + <span class="c1"># initialize offsets</span> + <span class="n">offs_m</span> <span class="o">=</span> <span class="n">start_m</span> <span class="o">*</span> <span class="n">BLOCK_M</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_M</span><span class="p">)</span> + <span class="n">offs_d</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">)</span> + <span class="c1"># load</span> + <span class="n">o</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">Out</span> <span class="o">+</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_ob</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_oh</span> <span class="o">+</span> <span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_om</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:],</span> + <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">do</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">DO</span> <span class="o">+</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_dob</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_doh</span> <span class="o">+</span> <span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_dom</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:],</span> + <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">delta</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">o</span> <span class="o">*</span> <span class="n">do</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> + <span class="c1"># write-back</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">Delta</span> <span class="o">+</span> <span class="n">off_hb</span> <span class="o">*</span> <span class="n">seqlen_q_rounded</span> <span class="o">+</span> <span class="n">offs_m</span><span class="p">,</span> <span class="n">delta</span><span class="p">)</span></div> + + +<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span> +<div class="viewcode-block" id="_bwd_store_dk_dv"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._bwd_store_dk_dv">[docs]</a><span class="k">def</span> <span class="nf">_bwd_store_dk_dv</span><span class="p">(</span> + <span class="n">dk_ptrs</span><span class="p">,</span> <span class="n">dv_ptrs</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="n">offs_n</span><span class="p">,</span> <span class="n">offs_d</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">headdim</span><span class="p">,</span> + <span class="n">EVEN_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">EVEN_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> +<span class="p">):</span> + <span class="c1"># [2022-11-01] TD: Same bug. In the case of EVEN_N=True and EVEN_M=False,</span> + <span class="c1"># if we just call tl.store(dv_ptrs), there&#39;s a race condition</span> + <span class="k">if</span> <span class="n">EVEN_N</span> <span class="o">&amp;</span> <span class="n">EVEN_M</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dv_ptrs</span><span class="p">,</span> <span class="n">dv</span><span class="p">)</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dk_ptrs</span><span class="p">,</span> <span class="n">dk</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dv_ptrs</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">)</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dk_ptrs</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dv_ptrs</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">)</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dk_ptrs</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dv_ptrs</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">))</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dk_ptrs</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">))</span></div> + + +<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span> +<div class="viewcode-block" id="_bwd_kernel_one_col_block"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel_one_col_block">[docs]</a><span class="k">def</span> <span class="nf">_bwd_kernel_one_col_block</span><span class="p">(</span> + <span class="n">start_n</span><span class="p">,</span> + <span class="n">Q</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Bias</span><span class="p">,</span> + <span class="n">DO</span><span class="p">,</span> <span class="n">DQ</span><span class="p">,</span> <span class="n">DK</span><span class="p">,</span> <span class="n">DV</span><span class="p">,</span> + <span class="n">LSE</span><span class="p">,</span> <span class="n">D</span><span class="p">,</span> + <span class="n">softmax_scale</span><span class="p">,</span> + <span class="n">stride_qm</span><span class="p">,</span> <span class="n">stride_kn</span><span class="p">,</span> <span class="n">stride_vn</span><span class="p">,</span> <span class="n">stride_bm</span><span class="p">,</span> + <span class="n">stride_dom</span><span class="p">,</span> <span class="n">stride_dqm</span><span class="p">,</span> <span class="n">stride_dkn</span><span class="p">,</span> <span class="n">stride_dvn</span><span class="p">,</span> + <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">headdim</span><span class="p">,</span> + <span class="n">ATOMIC_ADD</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">BIAS_TYPE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">IS_CAUSAL</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">BLOCK_HEADDIM</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">EVEN_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">EVEN_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">BLOCK_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> +<span class="p">):</span> + <span class="c1"># We need to make sure begin_m is a multiple of BLOCK_M (not BLOCK_N)</span> + <span class="n">begin_m</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">IS_CAUSAL</span> <span class="k">else</span> <span class="p">((</span><span class="n">start_n</span> <span class="o">*</span> <span class="n">BLOCK_N</span><span class="p">)</span> <span class="o">//</span> <span class="n">BLOCK_M</span><span class="p">)</span> <span class="o">*</span> <span class="n">BLOCK_M</span> + <span class="c1"># initialize row/col offsets</span> + <span class="n">offs_qm</span> <span class="o">=</span> <span class="n">begin_m</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_M</span><span class="p">)</span> + <span class="n">offs_n</span> <span class="o">=</span> <span class="n">start_n</span> <span class="o">*</span> <span class="n">BLOCK_N</span> <span class="o">+</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="p">)</span> + <span class="n">offs_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_M</span><span class="p">)</span> + <span class="n">offs_d</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">)</span> + <span class="c1"># initialize pointers to value-like data</span> + <span class="n">q_ptrs</span> <span class="o">=</span> <span class="n">Q</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_qm</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_qm</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">k_ptrs</span> <span class="o">=</span> <span class="n">K</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_kn</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">v_ptrs</span> <span class="o">=</span> <span class="n">V</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_vn</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">do_ptrs</span> <span class="o">=</span> <span class="n">DO</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_qm</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_dom</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">dq_ptrs</span> <span class="o">=</span> <span class="n">DQ</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_qm</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_dqm</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="k">if</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;vector&#39;</span><span class="p">:</span> + <span class="n">b_ptrs</span> <span class="o">=</span> <span class="n">Bias</span> <span class="o">+</span> <span class="n">offs_n</span> + <span class="k">elif</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;matrix&#39;</span><span class="p">:</span> + <span class="n">b_ptrs</span> <span class="o">=</span> <span class="n">Bias</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_qm</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_bm</span> <span class="o">+</span> <span class="n">offs_n</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="c1"># initialize dv and dk</span> + <span class="n">dv</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">BLOCK_N</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">dk</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">BLOCK_N</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="c1"># There seems to be some problem with Triton pipelining that makes results wrong for</span> + <span class="c1"># headdim=64, seqlen=(113, 255), bias_type=&#39;matrix&#39;. In this case the for loop</span> + <span class="c1"># may have zero step, and pipelining with the bias matrix could screw it up.</span> + <span class="c1"># So we just exit early.</span> + <span class="k">if</span> <span class="n">begin_m</span> <span class="o">&gt;=</span> <span class="n">seqlen_q</span><span class="p">:</span> + <span class="n">dv_ptrs</span> <span class="o">=</span> <span class="n">DV</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_dvn</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">dk_ptrs</span> <span class="o">=</span> <span class="n">DK</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_dkn</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">_bwd_store_dk_dv</span><span class="p">(</span><span class="n">dk_ptrs</span><span class="p">,</span> <span class="n">dv_ptrs</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="n">offs_n</span><span class="p">,</span> <span class="n">offs_d</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">headdim</span><span class="p">,</span> + <span class="n">EVEN_M</span><span class="o">=</span><span class="n">EVEN_M</span><span class="p">,</span> <span class="n">EVEN_N</span><span class="o">=</span><span class="n">EVEN_N</span><span class="p">,</span> <span class="n">EVEN_HEADDIM</span><span class="o">=</span><span class="n">EVEN_HEADDIM</span><span class="p">)</span> + <span class="k">return</span> + <span class="c1"># k and v stay in SRAM throughout</span> + <span class="c1"># [2022-10-30] TD: Same bug as the fwd. In the case of EVEN_N=True and EVEN_M=False,</span> + <span class="c1"># if we just call tl.load(k_ptrs), we get the wrong output!</span> + <span class="k">if</span> <span class="n">EVEN_N</span> <span class="o">&amp;</span> <span class="n">EVEN_M</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">k_ptrs</span><span class="p">)</span> + <span class="n">v</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">v_ptrs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">k_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="n">v</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">v_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">k_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="n">v</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">v_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">k</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">k_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="n">v</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">v_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="c1"># loop over rows</span> + <span class="n">num_block_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">seqlen_q</span><span class="p">,</span> <span class="n">BLOCK_M</span><span class="p">)</span> + <span class="k">for</span> <span class="n">start_m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">begin_m</span><span class="p">,</span> <span class="n">num_block_m</span> <span class="o">*</span> <span class="n">BLOCK_M</span><span class="p">,</span> <span class="n">BLOCK_M</span><span class="p">):</span> + <span class="n">start_m</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">multiple_of</span><span class="p">(</span><span class="n">start_m</span><span class="p">,</span> <span class="n">BLOCK_M</span><span class="p">)</span> + <span class="n">offs_m_curr</span> <span class="o">=</span> <span class="n">start_m</span> <span class="o">+</span> <span class="n">offs_m</span> + <span class="c1"># load q, k, v, do on-chip</span> + <span class="c1"># Same bug as below. Otherwise gives wrong result for headdim=40, seqlen=(128, 117)</span> + <span class="k">if</span> <span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">q</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">q_ptrs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">q</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">q_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">q</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">q_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> + <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="c1"># recompute p = softmax(qk, dim=-1).T</span> + <span class="n">qk</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">trans_b</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="c1"># Trying to combine the two masks seem to make the result wrong</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">EVEN_N</span><span class="p">:</span> <span class="c1"># Need to mask out otherwise the softmax is wrong</span> + <span class="n">qk</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">offs_n</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">qk</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;-inf&quot;</span><span class="p">))</span> + <span class="k">if</span> <span class="n">IS_CAUSAL</span><span class="p">:</span> + <span class="n">qk</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]),</span> <span class="n">qk</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;-inf&quot;</span><span class="p">))</span> + <span class="k">if</span> <span class="n">BIAS_TYPE</span> <span class="o">!=</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">debug_barrier</span><span class="p">()</span> <span class="c1"># Race condition otherwise</span> + <span class="k">if</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;vector&#39;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_N</span><span class="p">:</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_n</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">bias</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> + <span class="k">elif</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;matrix&#39;</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_N</span><span class="p">:</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">b_ptrs</span><span class="p">,</span> + <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> + <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">seqlen_k</span><span class="p">),</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">tl</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">qk</span> <span class="o">=</span> <span class="n">qk</span> <span class="o">*</span> <span class="n">softmax_scale</span> <span class="o">+</span> <span class="n">bias</span> + <span class="c1"># There seems to be a race condition when headdim=48/96, and dq, dk, dv are wrong.</span> + <span class="c1"># Also wrong for headdim=64.</span> + <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_HEADDIM</span><span class="p">):</span> + <span class="n">tl</span><span class="o">.</span><span class="n">debug_barrier</span><span class="p">()</span> + <span class="n">lse_i</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">LSE</span> <span class="o">+</span> <span class="n">offs_m_curr</span><span class="p">)</span> + <span class="k">if</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span> + <span class="n">p</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">qk</span> <span class="o">*</span> <span class="n">softmax_scale</span> <span class="o">-</span> <span class="n">lse_i</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">])</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">p</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">qk</span> <span class="o">-</span> <span class="n">lse_i</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">])</span> + <span class="c1"># compute dv</span> + <span class="c1"># [2022-10-30] TD: A Triton bug: if EVEN_M=True and EVEN_HEADDIM=False, if we call</span> + <span class="c1"># do = tl.load(do_ptrs, mask=offs_d[None, :] &lt; headdim, other=0.0), we get wrong outputs</span> + <span class="c1"># in the case of headdim=48/96, seqlen_q &amp; seqlen_k &gt;= 512. If headdim=40 or seqlen &lt; 512,</span> + <span class="c1"># the output is correct.</span> + <span class="k">if</span> <span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">do</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">do_ptrs</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="c1"># [2022-11-01] TD: Triton bug, there&#39;s a race condition if we just use m_mask and not d_mask.</span> + <span class="n">do</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">do_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> + <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> + <span class="c1"># if EVEN_M:</span> + <span class="c1"># if EVEN_HEADDIM:</span> + <span class="c1"># do = tl.load(do_ptrs)</span> + <span class="c1"># else:</span> + <span class="c1"># do = tl.load(do_ptrs, mask=offs_d[None, :] &lt; headdim, other=0.0)</span> + <span class="c1"># else:</span> + <span class="c1"># if EVEN_HEADDIM:</span> + <span class="c1"># do = tl.load(do_ptrs, mask=offs_m_curr[:, None] &lt; seqlen_q, other=0.0)</span> + <span class="c1"># else:</span> + <span class="c1"># do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] &lt; seqlen_q)</span> + <span class="c1"># &amp; (offs_d[None, :] &lt; headdim), other=0.0)</span> + <span class="n">dv</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">do</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="n">do</span><span class="p">,</span> <span class="n">trans_a</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="c1"># compute dp = dot(v, do)</span> + <span class="c1"># There seems to be a race condition when headdim=48/96, and dq, dk are wrong.</span> + <span class="c1"># Also wrong for headdim=128, seqlen=(108, 256), and ATOMIC_ADD=True</span> + <span class="c1"># Also wrong for headdim=64, seqlen=(1023, 1024), and ATOMIC_ADD=False</span> + <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_HEADDIM</span><span class="p">):</span> + <span class="n">tl</span><span class="o">.</span><span class="n">debug_barrier</span><span class="p">()</span> + <span class="n">dp</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">do</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">trans_b</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="c1"># There&#39;s a race condition for headdim=48</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">debug_barrier</span><span class="p">()</span> + <span class="c1"># compute ds = p * (dp - delta[:, None])</span> + <span class="c1"># Putting the subtraction after the dp matmul (instead of before) is slightly faster</span> + <span class="n">Di</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">D</span> <span class="o">+</span> <span class="n">offs_m_curr</span><span class="p">)</span> + <span class="c1"># Converting ds to q.dtype here reduces register pressure and makes it much faster</span> + <span class="c1"># for BLOCK_HEADDIM=128</span> + <span class="n">ds</span> <span class="o">=</span> <span class="p">(</span><span class="n">p</span> <span class="o">*</span> <span class="p">(</span><span class="n">dp</span> <span class="o">-</span> <span class="n">Di</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">])</span> <span class="o">*</span> <span class="n">softmax_scale</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> + <span class="c1"># compute dk = dot(ds.T, q)</span> + <span class="n">dk</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">trans_a</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="c1"># compute dq</span> + <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_HEADDIM</span><span class="p">):</span> <span class="c1"># Otherewise there&#39;s a race condition when BIAS_TYPE=&#39;matrix&#39;</span> + <span class="n">tl</span><span class="o">.</span><span class="n">debug_barrier</span><span class="p">()</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">ATOMIC_ADD</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> <span class="c1"># Race condition if we just do EVEN_M</span> + <span class="n">dq</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">dq_ptrs</span><span class="p">,</span> <span class="n">eviction_policy</span><span class="o">=</span><span class="s2">&quot;evict_last&quot;</span><span class="p">)</span> + <span class="n">dq</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dq_ptrs</span><span class="p">,</span> <span class="n">dq</span><span class="p">,</span> <span class="n">eviction_policy</span><span class="o">=</span><span class="s2">&quot;evict_last&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">dq</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">dq_ptrs</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> + <span class="n">eviction_policy</span><span class="o">=</span><span class="s2">&quot;evict_last&quot;</span><span class="p">)</span> + <span class="n">dq</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dq_ptrs</span><span class="p">,</span> <span class="n">dq</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">,</span> + <span class="n">eviction_policy</span><span class="o">=</span><span class="s2">&quot;evict_last&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">dq</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">dq_ptrs</span><span class="p">,</span> + <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> + <span class="n">other</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">eviction_policy</span><span class="o">=</span><span class="s2">&quot;evict_last&quot;</span><span class="p">)</span> + <span class="n">dq</span> <span class="o">+=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> + <span class="n">tl</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">dq_ptrs</span><span class="p">,</span> <span class="n">dq</span><span class="p">,</span> + <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">),</span> + <span class="n">eviction_policy</span><span class="o">=</span><span class="s2">&quot;evict_last&quot;</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> <span class="c1"># If we&#39;re parallelizing across the seqlen_k dimension</span> + <span class="n">dq</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span> + <span class="k">if</span> <span class="n">EVEN_M</span> <span class="o">&amp;</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> <span class="c1"># Race condition if we just do EVEN_M</span> + <span class="n">tl</span><span class="o">.</span><span class="n">atomic_add</span><span class="p">(</span><span class="n">dq_ptrs</span><span class="p">,</span> <span class="n">dq</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">if</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">atomic_add</span><span class="p">(</span><span class="n">dq_ptrs</span><span class="p">,</span> <span class="n">dq</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">tl</span><span class="o">.</span><span class="n">atomic_add</span><span class="p">(</span><span class="n">dq_ptrs</span><span class="p">,</span> <span class="n">dq</span><span class="p">,</span> + <span class="n">mask</span><span class="o">=</span><span class="p">(</span><span class="n">offs_m_curr</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">seqlen_q</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&lt;</span> <span class="n">headdim</span><span class="p">))</span> + <span class="c1"># increment pointers</span> + <span class="n">dq_ptrs</span> <span class="o">+=</span> <span class="n">BLOCK_M</span> <span class="o">*</span> <span class="n">stride_dqm</span> + <span class="n">q_ptrs</span> <span class="o">+=</span> <span class="n">BLOCK_M</span> <span class="o">*</span> <span class="n">stride_qm</span> + <span class="n">do_ptrs</span> <span class="o">+=</span> <span class="n">BLOCK_M</span> <span class="o">*</span> <span class="n">stride_dom</span> + <span class="k">if</span> <span class="n">BIAS_TYPE</span> <span class="o">==</span> <span class="s1">&#39;matrix&#39;</span><span class="p">:</span> + <span class="n">b_ptrs</span> <span class="o">+=</span> <span class="n">BLOCK_M</span> <span class="o">*</span> <span class="n">stride_bm</span> + <span class="c1"># write-back</span> + <span class="n">dv_ptrs</span> <span class="o">=</span> <span class="n">DV</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_dvn</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">dk_ptrs</span> <span class="o">=</span> <span class="n">DK</span> <span class="o">+</span> <span class="p">(</span><span class="n">offs_n</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">stride_dkn</span> <span class="o">+</span> <span class="n">offs_d</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:])</span> + <span class="n">_bwd_store_dk_dv</span><span class="p">(</span><span class="n">dk_ptrs</span><span class="p">,</span> <span class="n">dv_ptrs</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="n">offs_n</span><span class="p">,</span> <span class="n">offs_d</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">headdim</span><span class="p">,</span> + <span class="n">EVEN_M</span><span class="o">=</span><span class="n">EVEN_M</span><span class="p">,</span> <span class="n">EVEN_N</span><span class="o">=</span><span class="n">EVEN_N</span><span class="p">,</span> <span class="n">EVEN_HEADDIM</span><span class="o">=</span><span class="n">EVEN_HEADDIM</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="init_to_zero"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.init_to_zero">[docs]</a><span class="k">def</span> <span class="nf">init_to_zero</span><span class="p">(</span><span class="n">name</span><span class="p">):</span> + <span class="k">return</span> <span class="k">lambda</span> <span class="n">nargs</span><span class="p">:</span> <span class="n">nargs</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span></div> + + +<span class="nd">@triton</span><span class="o">.</span><span class="n">autotune</span><span class="p">(</span> + <span class="n">configs</span><span class="o">=</span><span class="p">[</span> + <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s2">&quot;BLOCK_M&quot;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s2">&quot;BLOCK_N&quot;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s2">&quot;SEQUENCE_PARALLEL&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">pre_hook</span><span class="o">=</span><span class="n">init_to_zero</span><span class="p">(</span><span class="s1">&#39;DQ&#39;</span><span class="p">)),</span> + <span class="n">triton</span><span class="o">.</span><span class="n">Config</span><span class="p">({</span><span class="s2">&quot;BLOCK_M&quot;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s2">&quot;BLOCK_N&quot;</span><span class="p">:</span> <span class="mi">128</span><span class="p">,</span> <span class="s2">&quot;SEQUENCE_PARALLEL&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">},</span> <span class="n">num_warps</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">num_stages</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">pre_hook</span><span class="o">=</span><span class="n">init_to_zero</span><span class="p">(</span><span class="s1">&#39;DQ&#39;</span><span class="p">)),</span> + <span class="c1"># Other configs seem to give wrong results when seqlen_q % 128 != 0, disabling them for now</span> + <span class="c1"># # Kernel is buggy (give wrong result) if we set BLOCK_m=128, BLOCK_n=64, num_warps=*4*</span> + <span class="c1"># triton.Config({&quot;BLOCK_M&quot;: 128, &quot;BLOCK_N&quot;: 64, &quot;SEQUENCE_PARALLEL&quot;: False}, num_warps=8, num_stages=1, pre_hook=init_to_zero(&#39;DQ&#39;)),</span> + <span class="c1"># triton.Config({&quot;BLOCK_M&quot;: 128, &quot;BLOCK_N&quot;: 64, &quot;SEQUENCE_PARALLEL&quot;: True}, num_warps=8, num_stages=1, pre_hook=init_to_zero(&#39;DQ&#39;)),</span> + <span class="c1"># triton.Config({&quot;BLOCK_M&quot;: 64, &quot;BLOCK_N&quot;: 64, &quot;SEQUENCE_PARALLEL&quot;: False}, num_warps=4, num_stages=1, pre_hook=init_to_zero(&#39;DQ&#39;)),</span> + <span class="c1"># triton.Config({&quot;BLOCK_M&quot;: 64, &quot;BLOCK_N&quot;: 64, &quot;SEQUENCE_PARALLEL&quot;: True}, num_warps=4, num_stages=1, pre_hook=init_to_zero(&#39;DQ&#39;)),</span> + <span class="p">],</span> + <span class="n">key</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;CACHE_KEY_SEQLEN_Q&#39;</span><span class="p">,</span> <span class="s1">&#39;CACHE_KEY_SEQLEN_K&#39;</span><span class="p">,</span> <span class="s1">&#39;BIAS_TYPE&#39;</span><span class="p">,</span> <span class="s1">&#39;IS_CAUSAL&#39;</span><span class="p">,</span> <span class="s1">&#39;BLOCK_HEADDIM&#39;</span><span class="p">],</span> +<span class="p">)</span> +<span class="nd">@triton</span><span class="o">.</span><span class="n">heuristics</span><span class="p">(</span> + <span class="p">{</span> + <span class="s2">&quot;EVEN_M&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">args</span><span class="p">:</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;seqlen_q&quot;</span><span class="p">]</span> <span class="o">%</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;BLOCK_M&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> + <span class="s2">&quot;EVEN_N&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">args</span><span class="p">:</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;seqlen_k&quot;</span><span class="p">]</span> <span class="o">%</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;BLOCK_N&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> + <span class="s2">&quot;EVEN_HEADDIM&quot;</span><span class="p">:</span> <span class="k">lambda</span> <span class="n">args</span><span class="p">:</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;headdim&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">args</span><span class="p">[</span><span class="s2">&quot;BLOCK_HEADDIM&quot;</span><span class="p">],</span> + <span class="p">}</span> +<span class="p">)</span> +<span class="nd">@triton</span><span class="o">.</span><span class="n">jit</span> +<div class="viewcode-block" id="_bwd_kernel"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel">[docs]</a><span class="k">def</span> <span class="nf">_bwd_kernel</span><span class="p">(</span> + <span class="n">Q</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Bias</span><span class="p">,</span> + <span class="n">DO</span><span class="p">,</span> <span class="n">DQ</span><span class="p">,</span> <span class="n">DK</span><span class="p">,</span> <span class="n">DV</span><span class="p">,</span> + <span class="n">LSE</span><span class="p">,</span> <span class="n">D</span><span class="p">,</span> + <span class="n">softmax_scale</span><span class="p">,</span> + <span class="n">stride_qb</span><span class="p">,</span> <span class="n">stride_qh</span><span class="p">,</span> <span class="n">stride_qm</span><span class="p">,</span> + <span class="n">stride_kb</span><span class="p">,</span> <span class="n">stride_kh</span><span class="p">,</span> <span class="n">stride_kn</span><span class="p">,</span> + <span class="n">stride_vb</span><span class="p">,</span> <span class="n">stride_vh</span><span class="p">,</span> <span class="n">stride_vn</span><span class="p">,</span> + <span class="n">stride_bb</span><span class="p">,</span> <span class="n">stride_bh</span><span class="p">,</span> <span class="n">stride_bm</span><span class="p">,</span> + <span class="n">stride_dob</span><span class="p">,</span> <span class="n">stride_doh</span><span class="p">,</span> <span class="n">stride_dom</span><span class="p">,</span> + <span class="n">stride_dqb</span><span class="p">,</span> <span class="n">stride_dqh</span><span class="p">,</span> <span class="n">stride_dqm</span><span class="p">,</span> + <span class="n">stride_dkb</span><span class="p">,</span> <span class="n">stride_dkh</span><span class="p">,</span> <span class="n">stride_dkn</span><span class="p">,</span> + <span class="n">stride_dvb</span><span class="p">,</span> <span class="n">stride_dvh</span><span class="p">,</span> <span class="n">stride_dvn</span><span class="p">,</span> + <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">seqlen_q_rounded</span><span class="p">,</span> <span class="n">headdim</span><span class="p">,</span> + <span class="n">CACHE_KEY_SEQLEN_Q</span><span class="p">,</span> <span class="n">CACHE_KEY_SEQLEN_K</span><span class="p">,</span> + <span class="n">BIAS_TYPE</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">IS_CAUSAL</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">BLOCK_HEADDIM</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">SEQUENCE_PARALLEL</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">EVEN_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">EVEN_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">EVEN_HEADDIM</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> + <span class="n">BLOCK_M</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="p">:</span> <span class="n">tl</span><span class="o">.</span><span class="n">constexpr</span><span class="p">,</span> +<span class="p">):</span> + <span class="n">off_hb</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> + <span class="n">off_b</span> <span class="o">=</span> <span class="n">off_hb</span> <span class="o">//</span> <span class="n">nheads</span> + <span class="n">off_h</span> <span class="o">=</span> <span class="n">off_hb</span> <span class="o">%</span> <span class="n">nheads</span> + <span class="c1"># offset pointers for batch/head</span> + <span class="n">Q</span> <span class="o">+=</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_qb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_qh</span> + <span class="n">K</span> <span class="o">+=</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_kb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_kh</span> + <span class="n">V</span> <span class="o">+=</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_vb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_vh</span> + <span class="n">DO</span> <span class="o">+=</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_dob</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_doh</span> + <span class="n">DQ</span> <span class="o">+=</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_dqb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_dqh</span> + <span class="n">DK</span> <span class="o">+=</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_dkb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_dkh</span> + <span class="n">DV</span> <span class="o">+=</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_dvb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_dvh</span> + <span class="k">if</span> <span class="n">BIAS_TYPE</span> <span class="o">!=</span> <span class="s1">&#39;none&#39;</span><span class="p">:</span> + <span class="n">Bias</span> <span class="o">+=</span> <span class="n">off_b</span> <span class="o">*</span> <span class="n">stride_bb</span> <span class="o">+</span> <span class="n">off_h</span> <span class="o">*</span> <span class="n">stride_bh</span> + <span class="c1"># pointer to row-wise quantities in value-like data</span> + <span class="n">D</span> <span class="o">+=</span> <span class="n">off_hb</span> <span class="o">*</span> <span class="n">seqlen_q_rounded</span> + <span class="n">LSE</span> <span class="o">+=</span> <span class="n">off_hb</span> <span class="o">*</span> <span class="n">seqlen_q_rounded</span> + <span class="k">if</span> <span class="ow">not</span> <span class="n">SEQUENCE_PARALLEL</span><span class="p">:</span> + <span class="n">num_block_n</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">seqlen_k</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="p">)</span> + <span class="k">for</span> <span class="n">start_n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_block_n</span><span class="p">):</span> + <span class="n">_bwd_kernel_one_col_block</span><span class="p">(</span> + <span class="n">start_n</span><span class="p">,</span> + <span class="n">Q</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Bias</span><span class="p">,</span> + <span class="n">DO</span><span class="p">,</span> <span class="n">DQ</span><span class="p">,</span> <span class="n">DK</span><span class="p">,</span> <span class="n">DV</span><span class="p">,</span> + <span class="n">LSE</span><span class="p">,</span> <span class="n">D</span><span class="p">,</span> + <span class="n">softmax_scale</span><span class="p">,</span> + <span class="n">stride_qm</span><span class="p">,</span> <span class="n">stride_kn</span><span class="p">,</span> <span class="n">stride_vn</span><span class="p">,</span> <span class="n">stride_bm</span><span class="p">,</span> + <span class="n">stride_dom</span><span class="p">,</span> <span class="n">stride_dqm</span><span class="p">,</span> <span class="n">stride_dkn</span><span class="p">,</span> <span class="n">stride_dvn</span><span class="p">,</span> + <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">headdim</span><span class="p">,</span> + <span class="n">ATOMIC_ADD</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> + <span class="n">BIAS_TYPE</span><span class="o">=</span><span class="n">BIAS_TYPE</span><span class="p">,</span> + <span class="n">IS_CAUSAL</span><span class="o">=</span><span class="n">IS_CAUSAL</span><span class="p">,</span> + <span class="n">BLOCK_HEADDIM</span><span class="o">=</span><span class="n">BLOCK_HEADDIM</span><span class="p">,</span> + <span class="n">EVEN_M</span><span class="o">=</span><span class="n">EVEN_M</span><span class="p">,</span> <span class="n">EVEN_N</span><span class="o">=</span><span class="n">EVEN_N</span><span class="p">,</span> <span class="n">EVEN_HEADDIM</span><span class="o">=</span><span class="n">EVEN_HEADDIM</span><span class="p">,</span> + <span class="n">BLOCK_M</span><span class="o">=</span><span class="n">BLOCK_M</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="o">=</span><span class="n">BLOCK_N</span> + <span class="p">)</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">start_n</span> <span class="o">=</span> <span class="n">tl</span><span class="o">.</span><span class="n">program_id</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> + <span class="n">_bwd_kernel_one_col_block</span><span class="p">(</span> + <span class="n">start_n</span><span class="p">,</span> + <span class="n">Q</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">V</span><span class="p">,</span> <span class="n">Bias</span><span class="p">,</span> + <span class="n">DO</span><span class="p">,</span> <span class="n">DQ</span><span class="p">,</span> <span class="n">DK</span><span class="p">,</span> <span class="n">DV</span><span class="p">,</span> + <span class="n">LSE</span><span class="p">,</span> <span class="n">D</span><span class="p">,</span> + <span class="n">softmax_scale</span><span class="p">,</span> + <span class="n">stride_qm</span><span class="p">,</span> <span class="n">stride_kn</span><span class="p">,</span> <span class="n">stride_vn</span><span class="p">,</span> <span class="n">stride_bm</span><span class="p">,</span> + <span class="n">stride_dom</span><span class="p">,</span> <span class="n">stride_dqm</span><span class="p">,</span> <span class="n">stride_dkn</span><span class="p">,</span> <span class="n">stride_dvn</span><span class="p">,</span> + <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">headdim</span><span class="p">,</span> + <span class="n">ATOMIC_ADD</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> + <span class="n">BIAS_TYPE</span><span class="o">=</span><span class="n">BIAS_TYPE</span><span class="p">,</span> + <span class="n">IS_CAUSAL</span><span class="o">=</span><span class="n">IS_CAUSAL</span><span class="p">,</span> + <span class="n">BLOCK_HEADDIM</span><span class="o">=</span><span class="n">BLOCK_HEADDIM</span><span class="p">,</span> + <span class="n">EVEN_M</span><span class="o">=</span><span class="n">EVEN_M</span><span class="p">,</span> <span class="n">EVEN_N</span><span class="o">=</span><span class="n">EVEN_N</span><span class="p">,</span> <span class="n">EVEN_HEADDIM</span><span class="o">=</span><span class="n">EVEN_HEADDIM</span><span class="p">,</span> + <span class="n">BLOCK_M</span><span class="o">=</span><span class="n">BLOCK_M</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="o">=</span><span class="n">BLOCK_N</span> + <span class="p">)</span></div> + + +<div class="viewcode-block" id="_flash_attn_forward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_forward">[docs]</a><span class="k">def</span> <span class="nf">_flash_attn_forward</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="c1"># shape constraints</span> + <span class="n">batch</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">nheads</span><span class="p">,</span> <span class="n">d</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">shape</span> + <span class="n">_</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">shape</span> + <span class="k">assert</span> <span class="n">k</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">nheads</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span> + <span class="k">assert</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">nheads</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span> + <span class="k">assert</span> <span class="n">d</span> <span class="o">&lt;=</span> <span class="mi">128</span><span class="p">,</span> <span class="s1">&#39;FlashAttention only support head dimensions up to 128&#39;</span> + <span class="k">assert</span> <span class="n">q</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">k</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">v</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="s1">&#39;All tensors must have the same type&#39;</span> + <span class="k">assert</span> <span class="n">q</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">bfloat16</span><span class="p">],</span> <span class="s1">&#39;Only support fp16 and bf16&#39;</span> + <span class="k">assert</span> <span class="n">q</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">k</span><span class="o">.</span><span class="n">is_cuda</span> <span class="ow">and</span> <span class="n">v</span><span class="o">.</span><span class="n">is_cuda</span> + <span class="n">softmax_scale</span> <span class="o">=</span> <span class="n">softmax_scale</span> <span class="ow">or</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> + + <span class="n">has_bias</span> <span class="o">=</span> <span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="n">bias_type</span> <span class="o">=</span> <span class="s1">&#39;none&#39;</span> + <span class="k">if</span> <span class="n">has_bias</span><span class="p">:</span> + <span class="k">assert</span> <span class="n">bias</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="n">q</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">]</span> + <span class="k">assert</span> <span class="n">bias</span><span class="o">.</span><span class="n">is_cuda</span> + <span class="k">assert</span> <span class="n">bias</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">4</span> + <span class="k">if</span> <span class="n">bias</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">bias</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> + <span class="k">if</span> <span class="n">bias</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">):</span> + <span class="n">bias_type</span> <span class="o">=</span> <span class="s1">&#39;vector&#39;</span> + <span class="k">elif</span> <span class="n">bias</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span> <span class="o">==</span> <span class="p">(</span><span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">):</span> + <span class="n">bias_type</span> <span class="o">=</span> <span class="s1">&#39;matrix&#39;</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;Last 2 dimensions of bias must be (1, seqlen_k)&#39;</span> + <span class="s1">&#39; or (seqlen_q, seqlen_k)&#39;</span><span class="p">)</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">bias</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">)</span> + <span class="n">bias_strides</span> <span class="o">=</span> <span class="p">(</span><span class="n">bias</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">bias</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">bias</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="k">if</span> <span class="n">has_bias</span> <span class="k">else</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> + + <span class="n">seqlen_q_rounded</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">seqlen_q</span> <span class="o">/</span> <span class="mi">128</span><span class="p">)</span> <span class="o">*</span> <span class="mi">128</span> + <span class="n">lse</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">batch</span><span class="p">,</span> <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q_rounded</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">q</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">tmp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">batch</span><span class="p">,</span> <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q_rounded</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">q</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">o</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">q</span><span class="p">)</span> + + <span class="n">BLOCK_HEADDIM</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">next_power_of_2</span><span class="p">(</span><span class="n">d</span><span class="p">),</span> <span class="mi">16</span><span class="p">)</span> + <span class="n">BLOCK</span> <span class="o">=</span> <span class="mi">128</span> + <span class="n">num_warps</span> <span class="o">=</span> <span class="mi">4</span> <span class="k">if</span> <span class="n">d</span> <span class="o">&lt;=</span> <span class="mi">64</span> <span class="k">else</span> <span class="mi">8</span> + <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">META</span><span class="p">:</span> <span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">seqlen_q</span><span class="p">,</span> <span class="n">META</span><span class="p">[</span><span class="s2">&quot;BLOCK_M&quot;</span><span class="p">]),</span> <span class="n">batch</span> <span class="o">*</span> <span class="n">nheads</span><span class="p">)</span> + <span class="n">_fwd_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span> + <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> + <span class="n">lse</span><span class="p">,</span> <span class="n">tmp</span><span class="p">,</span> + <span class="n">softmax_scale</span><span class="p">,</span> + <span class="n">q</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">q</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">q</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">k</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">k</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">k</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">v</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">v</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">v</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="o">*</span><span class="n">bias_strides</span><span class="p">,</span> + <span class="n">o</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">o</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">o</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">seqlen_q_rounded</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> + <span class="n">seqlen_q</span> <span class="o">//</span> <span class="mi">32</span><span class="p">,</span> <span class="n">seqlen_k</span> <span class="o">//</span> <span class="mi">32</span><span class="p">,</span> <span class="c1"># key for triton cache (limit number of compilations)</span> + <span class="c1"># Can&#39;t use kwargs here because triton autotune expects key to be args, not kwargs</span> + <span class="c1"># IS_CAUSAL=causal, BLOCK_HEADDIM=d,</span> + <span class="n">bias_type</span><span class="p">,</span> <span class="n">causal</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">,</span> + <span class="n">BLOCK_M</span><span class="o">=</span><span class="n">BLOCK</span><span class="p">,</span> <span class="n">BLOCK_N</span><span class="o">=</span><span class="n">BLOCK</span><span class="p">,</span> + <span class="n">num_warps</span><span class="o">=</span><span class="n">num_warps</span><span class="p">,</span> + <span class="n">num_stages</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> + <span class="p">)</span> + <span class="k">return</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">softmax_scale</span> <span class="c1"># softmax_scale could have been updated</span></div> + + +<div class="viewcode-block" id="_flash_attn_backward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_backward">[docs]</a><span class="k">def</span> <span class="nf">_flash_attn_backward</span><span class="p">(</span><span class="n">do</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">dq</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="c1"># Make sure that the last dimension is contiguous</span> + <span class="k">if</span> <span class="n">do</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">do</span> <span class="o">=</span> <span class="n">do</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> + <span class="n">batch</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">nheads</span><span class="p">,</span> <span class="n">d</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">shape</span> + <span class="n">_</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">k</span><span class="o">.</span><span class="n">shape</span> + <span class="c1"># assert d in {16, 32, 64, 128}</span> + <span class="k">assert</span> <span class="n">d</span> <span class="o">&lt;=</span> <span class="mi">128</span> + <span class="n">seqlen_q_rounded</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">seqlen_q</span> <span class="o">/</span> <span class="mi">128</span><span class="p">)</span> <span class="o">*</span> <span class="mi">128</span> + <span class="k">assert</span> <span class="n">lse</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q_rounded</span><span class="p">)</span> + <span class="k">assert</span> <span class="n">q</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">k</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">v</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">o</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> + <span class="k">assert</span> <span class="n">dq</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">dk</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="n">dv</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> + <span class="n">softmax_scale</span> <span class="o">=</span> <span class="n">softmax_scale</span> <span class="ow">or</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> + <span class="c1"># dq_accum = torch.zeros_like(q, dtype=torch.float32)</span> + <span class="n">dq_accum</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> + <span class="n">delta</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">lse</span><span class="p">)</span> + <span class="c1"># delta = torch.zeros_like(lse)</span> + + <span class="n">BLOCK_HEADDIM</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">next_power_of_2</span><span class="p">(</span><span class="n">d</span><span class="p">),</span> <span class="mi">16</span><span class="p">)</span> + <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">META</span><span class="p">:</span> <span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">seqlen_q</span><span class="p">,</span> <span class="n">META</span><span class="p">[</span><span class="s2">&quot;BLOCK_M&quot;</span><span class="p">]),</span> <span class="n">batch</span> <span class="o">*</span> <span class="n">nheads</span><span class="p">)</span> + <span class="n">_bwd_preprocess_do_o_dot</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span> + <span class="n">o</span><span class="p">,</span> <span class="n">do</span><span class="p">,</span> <span class="n">delta</span><span class="p">,</span> + <span class="n">o</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">o</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">o</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">do</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">do</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">do</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_q_rounded</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> + <span class="n">BLOCK_M</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="o">=</span><span class="n">BLOCK_HEADDIM</span><span class="p">,</span> + <span class="p">)</span> + + <span class="n">has_bias</span> <span class="o">=</span> <span class="n">bias</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> + <span class="n">bias_type</span> <span class="o">=</span> <span class="s1">&#39;none&#39;</span> + <span class="k">if</span> <span class="n">has_bias</span><span class="p">:</span> + <span class="k">assert</span> <span class="n">bias</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">in</span> <span class="p">[</span><span class="n">q</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">]</span> + <span class="k">assert</span> <span class="n">bias</span><span class="o">.</span><span class="n">is_cuda</span> + <span class="k">assert</span> <span class="n">bias</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">4</span> + <span class="k">assert</span> <span class="n">bias</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> + <span class="k">if</span> <span class="n">bias</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">):</span> + <span class="n">bias_type</span> <span class="o">=</span> <span class="s1">&#39;vector&#39;</span> + <span class="k">elif</span> <span class="n">bias</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]</span> <span class="o">==</span> <span class="p">(</span><span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">):</span> + <span class="n">bias_type</span> <span class="o">=</span> <span class="s1">&#39;matrix&#39;</span> + <span class="k">else</span><span class="p">:</span> + <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;Last 2 dimensions of bias must be (1, seqlen_k)&#39;</span> + <span class="s1">&#39; or (seqlen_q, seqlen_k)&#39;</span><span class="p">)</span> + <span class="n">bias</span> <span class="o">=</span> <span class="n">bias</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">)</span> + <span class="n">bias_strides</span> <span class="o">=</span> <span class="p">(</span><span class="n">bias</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">bias</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="n">bias</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="k">if</span> <span class="n">has_bias</span> <span class="k">else</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> + + <span class="c1"># BLOCK_M = 128</span> + <span class="c1"># BLOCK_N = 64</span> + <span class="c1"># num_warps = 4</span> + <span class="n">grid</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">META</span><span class="p">:</span> <span class="p">(</span><span class="n">triton</span><span class="o">.</span><span class="n">cdiv</span><span class="p">(</span><span class="n">seqlen_k</span><span class="p">,</span> <span class="n">META</span><span class="p">[</span><span class="s2">&quot;BLOCK_N&quot;</span><span class="p">])</span> <span class="k">if</span> <span class="n">META</span><span class="p">[</span><span class="s2">&quot;SEQUENCE_PARALLEL&quot;</span><span class="p">]</span> <span class="k">else</span> <span class="mi">1</span><span class="p">,</span> + <span class="n">batch</span> <span class="o">*</span> <span class="n">nheads</span><span class="p">)</span> + <span class="n">_bwd_kernel</span><span class="p">[</span><span class="n">grid</span><span class="p">](</span> + <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> + <span class="n">do</span><span class="p">,</span> <span class="n">dq_accum</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> + <span class="n">lse</span><span class="p">,</span> <span class="n">delta</span><span class="p">,</span> + <span class="n">softmax_scale</span><span class="p">,</span> + <span class="n">q</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">q</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">q</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">k</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">k</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">k</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">v</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">v</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">v</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="o">*</span><span class="n">bias_strides</span><span class="p">,</span> + <span class="n">do</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">do</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">do</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">dq_accum</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">dq_accum</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">dq_accum</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">dk</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">dk</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">dk</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">dv</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">dv</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">dv</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> + <span class="n">nheads</span><span class="p">,</span> <span class="n">seqlen_q</span><span class="p">,</span> <span class="n">seqlen_k</span><span class="p">,</span> <span class="n">seqlen_q_rounded</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> + <span class="n">seqlen_q</span> <span class="o">//</span> <span class="mi">32</span><span class="p">,</span> <span class="n">seqlen_k</span> <span class="o">//</span> <span class="mi">32</span><span class="p">,</span> <span class="c1"># key for triton cache (limit number of compilations)</span> + <span class="c1"># Can&#39;t use kwargs here because triton autotune expects key to be args, not kwargs</span> + <span class="c1"># IS_CAUSAL=causal, BLOCK_HEADDIM=d,</span> + <span class="n">bias_type</span><span class="p">,</span> <span class="n">causal</span><span class="p">,</span> <span class="n">BLOCK_HEADDIM</span><span class="p">,</span> + <span class="c1"># SEQUENCE_PARALLEL=False,</span> + <span class="c1"># BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N,</span> + <span class="c1"># num_warps=num_warps,</span> + <span class="c1"># num_stages=1,</span> + <span class="p">)</span> + <span class="n">dq</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">dq_accum</span><span class="p">)</span></div> + + +<div class="viewcode-block" id="FlashAttnQKVPackedFunc"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc">[docs]</a><span class="k">class</span> <span class="nc">FlashAttnQKVPackedFunc</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span> + + <span class="nd">@staticmethod</span> +<div class="viewcode-block" id="FlashAttnQKVPackedFunc.forward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">qkv</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> qkv: (batch, seqlen, 3, nheads, headdim)</span> +<span class="sd"> bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen).</span> +<span class="sd"> For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen).</span> +<span class="sd"> ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen)</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># Make sure that the last dimension is contiguous</span> + <span class="k">if</span> <span class="n">qkv</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span> + <span class="n">qkv</span> <span class="o">=</span> <span class="n">qkv</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> + <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">ctx</span><span class="o">.</span><span class="n">softmax_scale</span> <span class="o">=</span> <span class="n">_flash_attn_forward</span><span class="p">(</span> + <span class="n">qkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">qkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">qkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="n">causal</span><span class="p">,</span> + <span class="n">softmax_scale</span><span class="o">=</span><span class="n">softmax_scale</span> + <span class="p">)</span> + <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">qkv</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span> + <span class="n">ctx</span><span class="o">.</span><span class="n">causal</span> <span class="o">=</span> <span class="n">causal</span> + <span class="k">return</span> <span class="n">o</span></div> + + <span class="nd">@staticmethod</span> +<div class="viewcode-block" id="FlashAttnQKVPackedFunc.backward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">do</span><span class="p">):</span> + <span class="n">qkv</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">bias</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span> + <span class="k">assert</span> <span class="ow">not</span> <span class="n">ctx</span><span class="o">.</span><span class="n">needs_input_grad</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;FlashAttention does not support bias gradient yet&#39;</span> + <span class="c1"># Triton&#39;s autotune causes the Tensor._version to change, and so Pytorch autograd</span> + <span class="c1"># does a memcpy. To avoid this we run in inference_mode, which doesn&#39;t track the version.</span> + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">inference_mode</span><span class="p">():</span> + <span class="n">dqkv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">qkv</span><span class="p">)</span> + <span class="n">_flash_attn_backward</span><span class="p">(</span><span class="n">do</span><span class="p">,</span> <span class="n">qkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">qkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">qkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">],</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> + <span class="n">dqkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">dqkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">dqkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">],</span> + <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">causal</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">softmax_scale</span><span class="p">)</span> + <span class="k">return</span> <span class="n">dqkv</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span></div></div> + + +<div class="viewcode-block" id="flash_attn_qkvpacked_func"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_qkvpacked_func">[docs]</a><span class="n">flash_attn_qkvpacked_func</span> <span class="o">=</span> <span class="n">FlashAttnQKVPackedFunc</span><span class="o">.</span><span class="n">apply</span></div> + + +<div class="viewcode-block" id="FlashAttnKVPackedFunc"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc">[docs]</a><span class="k">class</span> <span class="nc">FlashAttnKVPackedFunc</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span> + + <span class="nd">@staticmethod</span> +<div class="viewcode-block" id="FlashAttnKVPackedFunc.forward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">kv</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> q: (batch, seqlen_q, nheads, headdim)</span> +<span class="sd"> kv: (batch, seqlen_k, 2, nheads, headdim)</span> +<span class="sd"> bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).</span> +<span class="sd"> For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).</span> +<span class="sd"> ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># Make sure that the last dimension is contiguous</span> + <span class="n">q</span><span class="p">,</span> <span class="n">kv</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="n">q</span><span class="p">,</span> <span class="n">kv</span><span class="p">]]</span> + <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">ctx</span><span class="o">.</span><span class="n">softmax_scale</span> <span class="o">=</span> <span class="n">_flash_attn_forward</span><span class="p">(</span> + <span class="n">q</span><span class="p">,</span> <span class="n">kv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">kv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="n">causal</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="n">softmax_scale</span> + <span class="p">)</span> + <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">kv</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span> + <span class="n">ctx</span><span class="o">.</span><span class="n">causal</span> <span class="o">=</span> <span class="n">causal</span> + <span class="k">return</span> <span class="n">o</span></div> + + <span class="nd">@staticmethod</span> +<div class="viewcode-block" id="FlashAttnKVPackedFunc.backward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">do</span><span class="p">):</span> + <span class="n">q</span><span class="p">,</span> <span class="n">kv</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">bias</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ctx</span><span class="o">.</span><span class="n">needs_input_grad</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">3</span><span class="p">:</span> + <span class="k">assert</span> <span class="ow">not</span> <span class="n">ctx</span><span class="o">.</span><span class="n">needs_input_grad</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="s1">&#39;FlashAttention does not support bias gradient yet&#39;</span> + <span class="c1"># Triton&#39;s autotune causes the Tensor._version to change, and so Pytorch autograd</span> + <span class="c1"># does a memcpy. To avoid this we run in inference_mode, which doesn&#39;t track the version.</span> + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">inference_mode</span><span class="p">():</span> + <span class="n">dq</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">q</span><span class="p">)</span> + <span class="n">dkv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">kv</span><span class="p">)</span> + <span class="n">_flash_attn_backward</span><span class="p">(</span><span class="n">do</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">kv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">kv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> + <span class="n">dq</span><span class="p">,</span> <span class="n">dkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">dkv</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> + <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">causal</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">softmax_scale</span><span class="p">)</span> + <span class="k">return</span> <span class="n">dq</span><span class="p">,</span> <span class="n">dkv</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span></div></div> + + +<div class="viewcode-block" id="flash_attn_kvpacked_func"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_kvpacked_func">[docs]</a><span class="n">flash_attn_kvpacked_func</span> <span class="o">=</span> <span class="n">FlashAttnKVPackedFunc</span><span class="o">.</span><span class="n">apply</span></div> + + +<div class="viewcode-block" id="FlashAttnFunc"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc">[docs]</a><span class="k">class</span> <span class="nc">FlashAttnFunc</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span> + + <span class="nd">@staticmethod</span> +<div class="viewcode-block" id="FlashAttnFunc.forward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> +<span class="sd"> q: (batch_size, seqlen_q, nheads, headdim)</span> +<span class="sd"> k, v: (batch_size, seqlen_k, nheads, headdim)</span> +<span class="sd"> bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).</span> +<span class="sd"> For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).</span> +<span class="sd"> ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)</span> +<span class="sd"> &quot;&quot;&quot;</span> + <span class="c1"># Make sure that the last dimension is contiguous</span> + <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">x</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">]]</span> + <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">ctx</span><span class="o">.</span><span class="n">softmax_scale</span> <span class="o">=</span> <span class="n">_flash_attn_forward</span><span class="p">(</span> + <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="n">causal</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="n">softmax_scale</span> + <span class="p">)</span> + <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">bias</span><span class="p">)</span> + <span class="n">ctx</span><span class="o">.</span><span class="n">causal</span> <span class="o">=</span> <span class="n">causal</span> + <span class="k">return</span> <span class="n">o</span></div> + + <span class="nd">@staticmethod</span> +<div class="viewcode-block" id="FlashAttnFunc.backward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward">[docs]</a> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">do</span><span class="p">):</span> + <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">bias</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span> + <span class="k">assert</span> <span class="ow">not</span> <span class="n">ctx</span><span class="o">.</span><span class="n">needs_input_grad</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;FlashAttention does not support bias gradient yet&#39;</span> + <span class="c1"># Triton&#39;s autotune causes the Tensor._version to change, and so Pytorch autograd</span> + <span class="c1"># does a memcpy. To avoid this we run in inference_mode, which doesn&#39;t track the version.</span> + <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">inference_mode</span><span class="p">():</span> + <span class="n">dq</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">q</span><span class="p">)</span> + <span class="n">dk</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> + <span class="n">dv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> + <span class="n">_flash_attn_backward</span><span class="p">(</span><span class="n">do</span><span class="p">,</span> <span class="n">q</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">lse</span><span class="p">,</span> <span class="n">dq</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> + <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span> <span class="n">causal</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">causal</span><span class="p">,</span> <span class="n">softmax_scale</span><span class="o">=</span><span class="n">ctx</span><span class="o">.</span><span class="n">softmax_scale</span><span class="p">)</span> + <span class="k">return</span> <span class="n">dq</span><span class="p">,</span> <span class="n">dk</span><span class="p">,</span> <span class="n">dv</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span></div></div> + + +<div class="viewcode-block" id="flash_attn_func"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_func">[docs]</a><span class="n">flash_attn_func</span> <span class="o">=</span> <span class="n">FlashAttnFunc</span><span class="o">.</span><span class="n">apply</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script 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+<div class="header-article-items header-article__inner"> + + <div class="header-article-items__start"> + + <div class="header-article-item"> + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../../../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch</h1><div class="highlight"><pre> +<span></span><span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span> + +<span class="kn">import</span> <span class="nn">torch</span> +<span class="kn">import</span> <span class="nn">transformers</span> +<span class="kn">import</span> <span class="nn">transformers.models.llama.modeling_llama</span> + +<div class="viewcode-block" id="CondenseRotaryEmbedding"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.html#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding">[docs]</a><span class="k">class</span> <span class="nc">CondenseRotaryEmbedding</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">pi_ratio</span><span class="p">,</span> <span class="n">ntk_ratio</span><span class="p">,</span> <span class="n">max_position_embeddings</span><span class="o">=</span><span class="mi">2048</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">ntk_ratio</span> <span class="o">=</span> <span class="n">ntk_ratio</span> + <span class="n">max_position_embeddings</span> <span class="o">*=</span> <span class="n">ntk_ratio</span> + <span class="n">base</span> <span class="o">=</span> <span class="n">base</span> <span class="o">*</span> <span class="n">ntk_ratio</span> <span class="o">**</span> <span class="p">(</span><span class="n">dim</span> <span class="o">/</span> <span class="p">(</span><span class="n">dim</span><span class="o">-</span><span class="mi">2</span><span class="p">))</span> <span class="c1">#Base change formula</span> + + <span class="n">inv_freq</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="n">base</span> <span class="o">**</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="o">/</span> <span class="n">dim</span><span class="p">))</span> + <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">&quot;inv_freq&quot;</span><span class="p">,</span> <span class="n">inv_freq</span><span class="p">)</span> + + <span class="bp">self</span><span class="o">.</span><span class="n">pi_ratio</span> <span class="o">=</span> <span class="n">pi_ratio</span> + <span class="n">max_position_embeddings</span> <span class="o">*=</span> <span class="n">pi_ratio</span> + <span class="bp">self</span><span class="o">.</span><span class="n">max_seq_len_cached</span> <span class="o">=</span> <span class="n">max_position_embeddings</span> + <span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_seq_len_cached</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">inv_freq</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">inv_freq</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="o">/</span> <span class="n">pi_ratio</span> + <span class="n">freqs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s2">&quot;i,j-&gt;ij&quot;</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inv_freq</span><span class="p">)</span> + + <span class="c1"># Different from paper, but it uses a different permutation in order to obtain the same calculation</span> + <span class="n">emb</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">freqs</span><span class="p">,</span> <span class="n">freqs</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> + <span class="n">dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">get_default_dtype</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">&quot;cos_cached&quot;</span><span class="p">,</span> <span class="n">emb</span><span class="o">.</span><span class="n">cos</span><span class="p">()[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="p">),</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">&quot;sin_cached&quot;</span><span class="p">,</span> <span class="n">emb</span><span class="o">.</span><span class="n">sin</span><span class="p">()[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="p">),</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + +<div class="viewcode-block" id="CondenseRotaryEmbedding.forward"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.html#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding.forward">[docs]</a> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">seq_len</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> + <span class="c1"># x: [bs, num_attention_heads, seq_len, head_size]</span> + <span class="c1"># This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.</span> + <span class="k">if</span> <span class="n">seq_len</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_seq_len_cached</span><span class="p">:</span> + <span class="bp">self</span><span class="o">.</span><span class="n">max_seq_len_cached</span> <span class="o">=</span> <span class="n">seq_len</span> + <span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_seq_len_cached</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">inv_freq</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">pi_ratio</span> + <span class="n">freqs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s2">&quot;i,j-&gt;ij&quot;</span><span class="p">,</span> <span class="n">t</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inv_freq</span><span class="p">)</span> + + <span class="c1"># Different from paper, but it uses a different permutation in order to obtain the same calculation</span> + <span class="n">emb</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">freqs</span><span class="p">,</span> <span class="n">freqs</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">&quot;cos_cached&quot;</span><span class="p">,</span> <span class="n">emb</span><span class="o">.</span><span class="n">cos</span><span class="p">()[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">&quot;sin_cached&quot;</span><span class="p">,</span> <span class="n">emb</span><span class="o">.</span><span class="n">sin</span><span class="p">()[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="n">persistent</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + + <span class="k">return</span> <span class="p">(</span> + <span class="bp">self</span><span class="o">.</span><span class="n">cos_cached</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="n">seq_len</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> + <span class="bp">self</span><span class="o">.</span><span class="n">sin_cached</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:</span><span class="n">seq_len</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> + <span class="p">)</span></div></div> + +<div class="viewcode-block" id="replace_llama_with_condense"><a class="viewcode-back" href="../../../../autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.html#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.replace_llama_with_condense">[docs]</a><span class="k">def</span> <span class="nf">replace_llama_with_condense</span><span class="p">(</span><span class="n">pi_ratio</span><span class="p">,</span> <span class="n">ntk_ratio</span><span class="p">):</span> + <span class="n">transformers</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">llama</span><span class="o">.</span><span class="n">modeling_llama</span><span class="o">.</span><span class="n">LlamaRotaryEmbedding</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">CondenseRotaryEmbedding</span><span class="p">,</span> <span class="n">pi_ratio</span><span class="o">=</span><span class="n">pi_ratio</span><span class="p">,</span> <span class="n">ntk_ratio</span><span class="o">=</span><span class="n">ntk_ratio</span><span class="p">)</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div class="footer-item"> + <p class="copyright"> + + © Copyright LMFlow 2023. + <br/> + + </p> +</div> + + <div class="footer-item"> + <p class="sphinx-version"> + Created using <a href="https://sphinx-doc.org/">Sphinx</a> 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class="header-article-items__start"> + + <div class="header-article-item"> + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../index.html" class="nav-link">Module code</a></li> + + + <li class="breadcrumb-item"><a href="../lmflow.html" class="nav-link">lmflow</a></li> + + <li class="breadcrumb-item active" aria-current="page">lmflow.version</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <h1>Source code for lmflow.version</h1><div class="highlight"><pre> +<div class="viewcode-block" id="__version__"><a class="viewcode-back" href="../../autoapi/lmflow/version/index.html#lmflow.version.__version__">[docs]</a><span></span><span class="n">__version__</span> <span class="o">=</span> <span class="s2">&quot;0.0.1&quot;</span></div> +</pre></div> + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="../../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="../../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div class="bd-footer__inner bd-page-width"> + + <div class="footer-items__start"> + + <div class="footer-item"> + <p class="copyright"> + + © Copyright LMFlow 2023. + <br/> + + </p> +</div> + + <div class="footer-item"> + <p class="sphinx-version"> + Created using <a href="https://sphinx-doc.org/">Sphinx</a> 5.3.0. + <br/> + </p> +</div> + + </div> + + + <div class="footer-items__end"> + + <div class="footer-item"><p class="theme-version"> + Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.13.1. +</p></div> + + </div> + +</div> + + </footer> + </body> +</html> \ No newline at end of file diff --git a/_sources/about/authors.md.txt b/_sources/about/authors.md.txt new file mode 100644 index 000000000..a3481ee66 --- /dev/null +++ b/_sources/about/authors.md.txt @@ -0,0 +1,4 @@ +# Contributors + + +Shizhe Diao, Rui Pan, Hanze Dong, Ka Shun Shum, Jipeng Zhang, Wei Xiong, Tong Zhang diff --git a/_sources/about/changelog.md.txt b/_sources/about/changelog.md.txt new file mode 100644 index 000000000..991794df9 --- /dev/null +++ b/_sources/about/changelog.md.txt @@ -0,0 +1,15 @@ +# Changelog + + +## Version 0.0.1 (Mar 28, 2023) + +The first public version. + +Task tuning, instruction tuning, on user defined datasets. + +A simple and extensible API for developers. + +Efficient finetuning with LoRA. + +Simplified model inference framework. + diff --git a/_sources/about/index.md.txt b/_sources/about/index.md.txt new file mode 100644 index 000000000..df657b31c --- /dev/null +++ b/_sources/about/index.md.txt @@ -0,0 +1,15 @@ +# About + + +```{toctree} +:maxdepth: 2 + +changelog +``` + + +```{toctree} +:maxdepth: 2 + +authors +``` diff --git a/_sources/api/_autosummary/lmflow.args.rst.txt b/_sources/api/_autosummary/lmflow.args.rst.txt new file mode 100644 index 000000000..edc52d9b9 --- /dev/null +++ b/_sources/api/_autosummary/lmflow.args.rst.txt @@ -0,0 +1,32 @@ +lmflow.args +=========== + +.. automodule:: lmflow.args + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + + DatasetArguments + FinetunerArguments + InferencerArguments + ModelArguments + + + + + + + + + diff --git a/_sources/autoapi/index.rst.txt b/_sources/autoapi/index.rst.txt new file mode 100644 index 000000000..333d5c5cd --- /dev/null +++ b/_sources/autoapi/index.rst.txt @@ -0,0 +1,11 @@ +API Reference +============= + +This page contains auto-generated API reference documentation [#f1]_. + +.. toctree:: + :titlesonly: + + /autoapi/lmflow/index + +.. [#f1] Created with `sphinx-autoapi <https://github.com/readthedocs/sphinx-autoapi>`_ \ No newline at end of file diff --git a/_sources/autoapi/lmflow/args/index.rst.txt b/_sources/autoapi/lmflow/args/index.rst.txt new file mode 100644 index 000000000..5247fe908 --- /dev/null +++ b/_sources/autoapi/lmflow/args/index.rst.txt @@ -0,0 +1,877 @@ +:py:mod:`lmflow.args` +===================== + +.. py:module:: lmflow.args + +.. autoapi-nested-parse:: + + This script defines dataclasses: ModelArguments and DatasetArguments, + that contain the arguments for the model and dataset used in training. + + It imports several modules, including dataclasses, field from typing, Optional from typing, + require_version from transformers.utils.versions, MODEL_FOR_CAUSAL_LM_MAPPING, + and TrainingArguments from transformers. + + MODEL_CONFIG_CLASSES is assigned a list of the model config classes from + MODEL_FOR_CAUSAL_LM_MAPPING. MODEL_TYPES is assigned a tuple of the model types + extracted from the MODEL_CONFIG_CLASSES. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.args.ModelArguments + lmflow.args.VisModelArguments + lmflow.args.DatasetArguments + lmflow.args.FinetunerArguments + lmflow.args.EvaluatorArguments + lmflow.args.InferencerArguments + lmflow.args.RaftAlignerArguments + lmflow.args.BenchmarkingArguments + lmflow.args.AutoArguments + + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.args.MODEL_CONFIG_CLASSES + lmflow.args.MODEL_TYPES + lmflow.args.PIPELINE_ARGUMENT_MAPPING + + +.. py:data:: MODEL_CONFIG_CLASSES + + + + +.. py:data:: MODEL_TYPES + + + + +.. py:class:: ModelArguments + + + Define a class ModelArguments using the dataclass decorator. + The class contains several optional parameters that can be used to configure a model. + + model_name_or_path : str + a string representing the path or name of a pretrained + model checkpoint for weights initialization. If None, a model will be trained from scratch. + + model_type : str + a string representing the type of model to use if training from + scratch. If not provided, a pretrained model will be used. + + config_overrides : str + a string representing the default config settings to override + when training a model from scratch. + + config_name : str + a string representing the name or path of the pretrained config to + use, if different from the model_name_or_path. + + tokenizer_name : str + a string representing the name or path of the pretrained tokenizer + to use, if different from the model_name_or_path. + + cache_dir : str + a string representing the path to the directory where pretrained models + downloaded from huggingface.co will be stored. + + use_fast_tokenizer : bool + a boolean indicating whether to use a fast tokenizer (backed by the + tokenizers library) or not. + + model_revision : str + a string representing the specific model version to use (can be a + branch name, tag name, or commit id). + + use_auth_token : bool + a boolean indicating whether to use the token generated when running + huggingface-cli login (necessary to use this script with private models). + + torch_dtype : str + a string representing the dtype to load the model under. If auto is + passed, the dtype will be automatically derived from the model's weights. + + use_ram_optimized_load : bool + a boolean indicating whether to use disk mapping when memory is not + enough. + use_int8 : bool + a boolean indicating whether to load int8 quantization for inference. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:attribute:: model_name_or_path + :annotation: :Optional[str] + + + + .. py:attribute:: lora_model_path + :annotation: :Optional[str] + + + + .. py:attribute:: model_type + :annotation: :Optional[str] + + + + .. py:attribute:: arch_type + :annotation: :Optional[str] + + + + .. py:attribute:: config_overrides + :annotation: :Optional[str] + + + + .. py:attribute:: arch_type + :annotation: :Optional[str] + + + + .. py:attribute:: config_name + :annotation: :Optional[str] + + + + .. py:attribute:: tokenizer_name + :annotation: :Optional[str] + + + + .. py:attribute:: cache_dir + :annotation: :Optional[str] + + + + .. py:attribute:: use_fast_tokenizer + :annotation: :bool + + + + .. py:attribute:: model_revision + :annotation: :str + + + + .. py:attribute:: use_auth_token + :annotation: :bool + + + + .. py:attribute:: torch_dtype + :annotation: :Optional[str] + + + + .. py:attribute:: use_lora + :annotation: :bool + + + + .. py:attribute:: lora_r + :annotation: :int + + + + .. py:attribute:: lora_alpha + :annotation: :int + + + + .. py:attribute:: lora_target_modules + :annotation: :List[str] + + + + .. py:attribute:: lora_dropout + :annotation: :float + + + + .. py:attribute:: save_aggregated_lora + :annotation: :bool + + + + .. py:attribute:: use_ram_optimized_load + :annotation: :bool + + + + .. py:attribute:: use_flash_attention + :annotation: :bool + + + + .. py:attribute:: truncate_to_model_max_length + :annotation: :bool + + + + .. py:attribute:: do_rope_scaling + :annotation: :bool + + + + .. py:attribute:: rope_pi_ratio + :annotation: :int + + + + .. py:attribute:: rope_ntk_ratio + :annotation: :int + + + + .. py:attribute:: use_int8 + :annotation: :bool + + + + .. py:method:: __post_init__() + + + +.. py:class:: VisModelArguments + + Bases: :py:obj:`ModelArguments` + + + Define a class ModelArguments using the dataclass decorator. + The class contains several optional parameters that can be used to configure a model. + + model_name_or_path : str + a string representing the path or name of a pretrained + model checkpoint for weights initialization. If None, a model will be trained from scratch. + + model_type : str + a string representing the type of model to use if training from + scratch. If not provided, a pretrained model will be used. + + config_overrides : str + a string representing the default config settings to override + when training a model from scratch. + + config_name : str + a string representing the name or path of the pretrained config to + use, if different from the model_name_or_path. + + tokenizer_name : str + a string representing the name or path of the pretrained tokenizer + to use, if different from the model_name_or_path. + + cache_dir : str + a string representing the path to the directory where pretrained models + downloaded from huggingface.co will be stored. + + use_fast_tokenizer : bool + a boolean indicating whether to use a fast tokenizer (backed by the + tokenizers library) or not. + + model_revision : str + a string representing the specific model version to use (can be a + branch name, tag name, or commit id). + + use_auth_token : bool + a boolean indicating whether to use the token generated when running + huggingface-cli login (necessary to use this script with private models). + + torch_dtype : str + a string representing the dtype to load the model under. If auto is + passed, the dtype will be automatically derived from the model's weights. + + use_ram_optimized_load : bool + a boolean indicating whether to use disk mapping when memory is not + enough. + use_int8 : bool + a boolean indicating whether to load int8 quantization for inference. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:attribute:: low_resource + :annotation: :Optional[bool] + + + + .. py:attribute:: custom_model + :annotation: :bool + + + + .. py:attribute:: checkpoint_path + :annotation: :str + + + + .. py:attribute:: llm_model_name_or_path + :annotation: :Optional[str] + + + + .. py:attribute:: use_prompt_cache + :annotation: :bool + + + + .. py:attribute:: prompt_cache_path + :annotation: :Optional[str] + + + + +.. py:class:: DatasetArguments + + + Define a class DatasetArguments using the dataclass decorator. + The class contains several optional parameters that can be used to configure a dataset for a language model. + + dataset_path : str + a string representing the path of the dataset to use. + + dataset_name : str + a string representing the name of the dataset to use. The default value is "customized". + + is_custom_dataset : bool + a boolean indicating whether to use custom data. The default value is False. + + customized_cache_dir : str + a string representing the path to the directory where customized dataset caches will be stored. + + dataset_config_name : str + a string representing the configuration name of the dataset to use (via the datasets library). + + train_file : str + a string representing the path to the input training data file (a text file). + + validation_file : str + a string representing the path to the input evaluation data file to evaluate the perplexity on (a text file). + + max_train_samples : int + an integer indicating the maximum number of training examples to use for debugging or quicker training. + If set, the training dataset will be truncated to this number. + + max_eval_samples: int + an integer indicating the maximum number of evaluation examples to use for debugging or quicker training. + If set, the evaluation dataset will be truncated to this number. + + streaming : bool + a boolean indicating whether to enable streaming mode. + + block_size: int + an integer indicating the optional input sequence length after tokenization. The training dataset will be + truncated in blocks of this size for training. + + The class also includes some additional parameters that can be used to configure the dataset further, such as `overwrite_cache`, + `validation_split_percentage`, `preprocessing_num_workers`, `disable_group_texts`, `demo_example_in_prompt`, `explanation_in_prompt`, + `keep_linebreaks`, and `prompt_structure`. + + The field function is used to set default values and provide help messages for each parameter. The Optional type hint is + used to indicate that a parameter is optional. The metadata argument is used to provide additional information about + each parameter, such as a help message. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:attribute:: dataset_path + :annotation: :Optional[str] + + + + .. py:attribute:: dataset_name + :annotation: :Optional[str] + + + + .. py:attribute:: is_custom_dataset + :annotation: :Optional[bool] + + + + .. py:attribute:: customized_cache_dir + :annotation: :Optional[str] + + + + .. py:attribute:: dataset_config_name + :annotation: :Optional[str] + + + + .. py:attribute:: train_file + :annotation: :Optional[str] + + + + .. py:attribute:: validation_file + :annotation: :Optional[str] + + + + .. py:attribute:: max_train_samples + :annotation: :Optional[int] + + + + .. py:attribute:: max_eval_samples + :annotation: :Optional[int] + + + + .. py:attribute:: streaming + :annotation: :bool + + + + .. py:attribute:: block_size + :annotation: :Optional[int] + + + + .. py:attribute:: overwrite_cache + :annotation: :bool + + + + .. py:attribute:: validation_split_percentage + :annotation: :Optional[int] + + + + .. py:attribute:: preprocessing_num_workers + :annotation: :Optional[int] + + + + .. py:attribute:: group_texts_batch_size + :annotation: :int + + + + .. py:attribute:: disable_group_texts + :annotation: :bool + + + + .. py:attribute:: keep_linebreaks + :annotation: :bool + + + + .. py:attribute:: test_file + :annotation: :Optional[str] + + + + .. py:method:: __post_init__() + + + +.. py:class:: FinetunerArguments + + Bases: :py:obj:`transformers.TrainingArguments` + + + Adapt transformers.TrainingArguments + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:attribute:: eval_dataset_path + :annotation: :Optional[str] + + + + +.. py:class:: EvaluatorArguments + + + Define a class EvaluatorArguments using the dataclass decorator. The class contains several optional + parameters that can be used to configure a evaluator. + + local_rank : str + For distributed training: local_rank + + random_shuffle : bool + + use_wandb : bool + + random_seed : int, default = 1 + + output_dir : str, default = './output_dir', + + mixed_precision : str, choice from ["bf16","fp16"]. + mixed precision mode, whether to use bf16 or fp16 + + deepspeed : + Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already + loaded json file as a dict + + temperature : float + An argument of model.generate in huggingface to control the diversity of generation. + + repetition_penalty : float + An argument of model.generate in huggingface to penalize repetitions. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:attribute:: local_rank + :annotation: :int + + + + .. py:attribute:: random_shuffle + :annotation: :Optional[bool] + + + + .. py:attribute:: use_wandb + :annotation: :Optional[bool] + + + + .. py:attribute:: random_seed + :annotation: :Optional[int] + + + + .. py:attribute:: output_dir + :annotation: :Optional[str] + + + + .. py:attribute:: mixed_precision + :annotation: :Optional[str] + + + + .. py:attribute:: deepspeed + :annotation: :Optional[str] + + + + .. py:attribute:: answer_type + :annotation: :Optional[str] + + + + .. py:attribute:: prompt_structure + :annotation: :Optional[str] + + + + .. py:attribute:: evaluate_block_size + :annotation: :Optional[int] + + + + .. py:attribute:: metric + :annotation: :Optional[str] + + + + .. py:attribute:: inference_batch_size_per_device + :annotation: :Optional[int] + + + + .. py:attribute:: use_accelerator_for_evaluator + :annotation: :bool + + + + .. py:attribute:: temperature + :annotation: :float + + + + .. py:attribute:: repetition_penalty + :annotation: :float + + + + .. py:attribute:: max_new_tokens + :annotation: :int + + + + +.. py:class:: InferencerArguments + + + Define a class InferencerArguments using the dataclass decorator. The class contains several optional + parameters that can be used to configure a inferencer. + + local_rank : str + For distributed training: local_rank + + random_seed : int, default = 1 + + deepspeed : + Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already + loaded json file as a dict + mixed_precision : str, choice from ["bf16","fp16"]. + mixed precision mode, whether to use bf16 or fp16 + + temperature : float + An argument of model.generate in huggingface to control the diversity of generation. + + repetition_penalty : float + An argument of model.generate in huggingface to penalize repetitions. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:attribute:: device + :annotation: :str + + + + .. py:attribute:: local_rank + :annotation: :int + + + + .. py:attribute:: temperature + :annotation: :float + + + + .. py:attribute:: repetition_penalty + :annotation: :float + + + + .. py:attribute:: max_new_tokens + :annotation: :int + + + + .. py:attribute:: random_seed + :annotation: :Optional[int] + + + + .. py:attribute:: deepspeed + :annotation: :Optional[str] + + + + .. py:attribute:: mixed_precision + :annotation: :Optional[str] + + + + .. py:attribute:: do_sample + :annotation: :Optional[bool] + + + + +.. py:class:: RaftAlignerArguments + + Bases: :py:obj:`transformers.TrainingArguments` + + + Define a class RaftAlignerArguments to configure raft aligner. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:attribute:: output_reward_path + :annotation: :Optional[str] + + + + .. py:attribute:: output_min_length + :annotation: :Optional[int] + + + + .. py:attribute:: output_max_length + :annotation: :Optional[int] + + + + .. py:attribute:: num_raft_iteration + :annotation: :Optional[int] + + + + .. py:attribute:: raft_batch_size + :annotation: :Optional[int] + + + + .. py:attribute:: top_reward_percentage + :annotation: :Optional[float] + + + + .. py:attribute:: inference_batch_size_per_device + :annotation: :Optional[int] + + + + .. py:attribute:: collection_strategy + :annotation: :Optional[str] + + + + +.. py:class:: BenchmarkingArguments + + .. py:attribute:: dataset_name + :annotation: :Optional[str] + + + + .. py:attribute:: lm_evaluation_metric + :annotation: :Optional[str] + + + + +.. py:data:: PIPELINE_ARGUMENT_MAPPING + + + + +.. py:class:: AutoArguments + + + Automatically choose arguments from FinetunerArguments or EvaluatorArguments. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: get_pipeline_args_class() + + + diff --git a/_sources/autoapi/lmflow/datasets/dataset/index.rst.txt b/_sources/autoapi/lmflow/datasets/dataset/index.rst.txt new file mode 100644 index 000000000..6a2ac4fbd --- /dev/null +++ b/_sources/autoapi/lmflow/datasets/dataset/index.rst.txt @@ -0,0 +1,434 @@ +:py:mod:`lmflow.datasets.dataset` +================================= + +.. py:module:: lmflow.datasets.dataset + +.. autoapi-nested-parse:: + + This Python code defines a class Dataset with methods for initializing, loading, + and manipulating datasets from different backends such as Hugging Face and JSON. + + The `Dataset` class includes methods for loading datasets from a dictionary and a Hugging + Face dataset, mapping datasets, and retrieving the backend dataset and arguments. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.datasets.dataset.Dataset + + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.datasets.dataset.DATASET_TYPES + lmflow.datasets.dataset.KEY_TYPE + lmflow.datasets.dataset.KEY_INSTANCES + + +.. py:data:: DATASET_TYPES + :annotation: = ['text_only', 'text2text', 'float_only', 'image_text'] + + + +.. py:data:: KEY_TYPE + :annotation: = type + + + +.. py:data:: KEY_INSTANCES + :annotation: = instances + + + +.. py:class:: Dataset(data_args=None, backend: str = 'huggingface', *args, **kwargs) + + + Initializes the Dataset object with the given parameters. + + + :Parameters: + + **data_args** : DatasetArguments object. + Contains the arguments required to load the dataset. + + **backend** : str, default="huggingface" + A string representing the dataset backend. Defaults to "huggingface". + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: _check_data_format() + + + Checks if data type and data structure matches + + Raise messages with hints if not matched. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: from_dict(dict_obj: dict, *args, **kwargs) + + + Create a Dataset object from a dictionary. + + Return a Dataset given a dict with format: + { + "type": TYPE, + "instances": [ + { + "key_1": VALUE_1.1, + "key_2": VALUE_1.2, + ... + }, + { + "key_1": VALUE_2.1, + "key_2": VALUE_2.2, + ... + }, + ... + ] + } + + :Parameters: + + **dict_obj** : dict. + A dictionary containing the dataset information. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + **self** : Dataset object. + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: create_from_dict(dict_obj, *args, **kwargs) + :classmethod: + + + + + + + :Returns: + + Returns a Dataset object given a dict. + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: to_dict() + + + + + + + :Returns: + + Return a dict represents the dataset: + { + "type": TYPE, + "instances": [ + { + "key_1": VALUE_1.1, + "key_2": VALUE_1.2, + ... + }, + { + "key_1": VALUE_2.1, + "key_2": VALUE_2.2, + ... + }, + ... + ] + } + + A python dict object represents the content of this dataset. + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: to_list() + + + Returns a list of instances. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: map(*args, **kwargs) + + + + + + :Parameters: + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + **self** : Dataset object. + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_backend() -> Optional[str] + + + + + + + :Returns: + + self.backend + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_backend_dataset() + + + + + + + :Returns: + + self.backend_dataset + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_fingerprint() + + + + + + + :Returns: + + Fingerprint of the backend_dataset which controls the cache + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_data_args() + + + + + + + :Returns: + + self.data_args + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_type() + + + + + + + :Returns: + + self.type + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/datasets/index.rst.txt b/_sources/autoapi/lmflow/datasets/index.rst.txt new file mode 100644 index 000000000..77fb0064e --- /dev/null +++ b/_sources/autoapi/lmflow/datasets/index.rst.txt @@ -0,0 +1,433 @@ +:py:mod:`lmflow.datasets` +========================= + +.. py:module:: lmflow.datasets + +.. autoapi-nested-parse:: + + + This Python code defines a class Dataset with methods for initializing, loading, + and manipulating datasets from different backends such as Hugging Face and JSON. + + The `Dataset` class includes methods for loading datasets from a dictionary and a Hugging + Face dataset, mapping datasets, and retrieving the backend dataset and arguments. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + + dataset/index.rst + + +Package Contents +---------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.datasets.Dataset + + + + +.. py:class:: Dataset(data_args=None, backend: str = 'huggingface', *args, **kwargs) + + + Initializes the Dataset object with the given parameters. + + + :Parameters: + + **data_args** : DatasetArguments object. + Contains the arguments required to load the dataset. + + **backend** : str, default="huggingface" + A string representing the dataset backend. Defaults to "huggingface". + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: _check_data_format() + + + Checks if data type and data structure matches + + Raise messages with hints if not matched. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: from_dict(dict_obj: dict, *args, **kwargs) + + + Create a Dataset object from a dictionary. + + Return a Dataset given a dict with format: + { + "type": TYPE, + "instances": [ + { + "key_1": VALUE_1.1, + "key_2": VALUE_1.2, + ... + }, + { + "key_1": VALUE_2.1, + "key_2": VALUE_2.2, + ... + }, + ... + ] + } + + :Parameters: + + **dict_obj** : dict. + A dictionary containing the dataset information. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + **self** : Dataset object. + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: create_from_dict(dict_obj, *args, **kwargs) + :classmethod: + + + + + + + :Returns: + + Returns a Dataset object given a dict. + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: to_dict() + + + + + + + :Returns: + + Return a dict represents the dataset: + { + "type": TYPE, + "instances": [ + { + "key_1": VALUE_1.1, + "key_2": VALUE_1.2, + ... + }, + { + "key_1": VALUE_2.1, + "key_2": VALUE_2.2, + ... + }, + ... + ] + } + + A python dict object represents the content of this dataset. + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: to_list() + + + Returns a list of instances. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: map(*args, **kwargs) + + + + + + :Parameters: + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + **self** : Dataset object. + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_backend() -> Optional[str] + + + + + + + :Returns: + + self.backend + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_backend_dataset() + + + + + + + :Returns: + + self.backend_dataset + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_fingerprint() + + + + + + + :Returns: + + Fingerprint of the backend_dataset which controls the cache + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_data_args() + + + + + + + :Returns: + + self.data_args + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_type() + + + + + + + :Returns: + + self.type + .. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/index.rst.txt b/_sources/autoapi/lmflow/index.rst.txt new file mode 100644 index 000000000..bd0de3363 --- /dev/null +++ b/_sources/autoapi/lmflow/index.rst.txt @@ -0,0 +1,41 @@ +:py:mod:`lmflow` +================ + +.. py:module:: lmflow + + +Subpackages +----------- +.. toctree:: + :titlesonly: + :maxdepth: 3 + + datasets/index.rst + models/index.rst + pipeline/index.rst + utils/index.rst + + +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + + args/index.rst + version/index.rst + + +Package Contents +---------------- + +.. py:data:: internal_version + :annotation: = 0.0.1 + + + +.. py:data:: __version__ + + + + diff --git a/_sources/autoapi/lmflow/models/auto_model/index.rst.txt b/_sources/autoapi/lmflow/models/auto_model/index.rst.txt new file mode 100644 index 000000000..da3080c1e --- /dev/null +++ b/_sources/autoapi/lmflow/models/auto_model/index.rst.txt @@ -0,0 +1,33 @@ +:py:mod:`lmflow.models.auto_model` +================================== + +.. py:module:: lmflow.models.auto_model + +.. autoapi-nested-parse:: + + Automatically get correct model type. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.auto_model.AutoModel + + + + +.. py:class:: AutoModel + + .. py:method:: get_model(model_args, *args, **kwargs) + :classmethod: + + + diff --git a/_sources/autoapi/lmflow/models/base_model/index.rst.txt b/_sources/autoapi/lmflow/models/base_model/index.rst.txt new file mode 100644 index 000000000..7ac80c1ed --- /dev/null +++ b/_sources/autoapi/lmflow/models/base_model/index.rst.txt @@ -0,0 +1,52 @@ +:py:mod:`lmflow.models.base_model` +================================== + +.. py:module:: lmflow.models.base_model + +.. autoapi-nested-parse:: + + Base model class. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.base_model.BaseModel + + + + +.. py:class:: BaseModel(*args, **kwargs) + + Bases: :py:obj:`abc.ABC` + + + Helper class that provides a standard way to create an ABC using + inheritance. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + diff --git a/_sources/autoapi/lmflow/models/decoder_model/index.rst.txt b/_sources/autoapi/lmflow/models/decoder_model/index.rst.txt new file mode 100644 index 000000000..6f2b57d6b --- /dev/null +++ b/_sources/autoapi/lmflow/models/decoder_model/index.rst.txt @@ -0,0 +1,62 @@ +:py:mod:`lmflow.models.decoder_model` +===================================== + +.. py:module:: lmflow.models.decoder_model + +.. autoapi-nested-parse:: + + A one-line summary of the module or program, terminated by a period. + + Leave one blank line. The rest of this docstring should contain an + overall description of the module or program. Optionally, it may also + contain a brief description of exported classes and functions and/or usage + examples. + + Typical usage example: + + foo = ClassFoo() + bar = foo.FunctionBar() + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.decoder_model.DecoderModel + + + + +.. py:class:: DecoderModel(*args, **kwargs) + + Bases: :py:obj:`lmflow.models.base_model.BaseModel` + + + Helper class that provides a standard way to create an ABC using + inheritance. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + diff --git a/_sources/autoapi/lmflow/models/encoder_decoder_model/index.rst.txt b/_sources/autoapi/lmflow/models/encoder_decoder_model/index.rst.txt new file mode 100644 index 000000000..e6da08346 --- /dev/null +++ b/_sources/autoapi/lmflow/models/encoder_decoder_model/index.rst.txt @@ -0,0 +1,62 @@ +:py:mod:`lmflow.models.encoder_decoder_model` +============================================= + +.. py:module:: lmflow.models.encoder_decoder_model + +.. autoapi-nested-parse:: + + A one-line summary of the module or program, terminated by a period. + + Leave one blank line. The rest of this docstring should contain an + overall description of the module or program. Optionally, it may also + contain a brief description of exported classes and functions and/or usage + examples. + + Typical usage example: + + foo = ClassFoo() + bar = foo.FunctionBar() + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.encoder_decoder_model.EncoderDecoderModel + + + + +.. py:class:: EncoderDecoderModel(*args, **kwargs) + + Bases: :py:obj:`lmflow.models.base_model.BaseModel` + + + Helper class that provides a standard way to create an ABC using + inheritance. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + diff --git a/_sources/autoapi/lmflow/models/hf_decoder_model/index.rst.txt b/_sources/autoapi/lmflow/models/hf_decoder_model/index.rst.txt new file mode 100644 index 000000000..02bd76c61 --- /dev/null +++ b/_sources/autoapi/lmflow/models/hf_decoder_model/index.rst.txt @@ -0,0 +1,379 @@ +:py:mod:`lmflow.models.hf_decoder_model` +======================================== + +.. py:module:: lmflow.models.hf_decoder_model + +.. autoapi-nested-parse:: + + This is a class called HFDecoderModel which is a wrapper around transformers model and + tokenizer classes. It has several methods such as __init__, tokenize, and train that are + used for training and fine-tuning the model. The __init__ method takes in several arguments + such as model_args, tune_strategy, and ds_config, which are used to load the pretrained + model and tokenizer, and initialize the training settings. + + The tokenize method is used to tokenize the input text and return the input IDs and attention + masks that can be fed to the model for training or inference. + + This class supports different tune_strategy options such as 'normal', 'none', 'lora', and + 'adapter', which allow for different fine-tuning settings of the model. However, the 'lora' + and 'adapter' strategies are not yet implemented. + + Overall, this class provides a convenient interface for loading and fine-tuning transformer + models and can be used for various NLP tasks such as language modeling, text classification, + and question answering. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.hf_decoder_model.HFDecoderModel + + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.models.hf_decoder_model.logger + lmflow.models.hf_decoder_model.MODELS_SUPPORT_FLASH_ATTENTION + lmflow.models.hf_decoder_model.GPU_SUPPORT_FLASH_ATTENTION + lmflow.models.hf_decoder_model.GPU_SUPPORT_FLASH_ATTENTION + + +.. py:data:: logger + + + + +.. py:data:: MODELS_SUPPORT_FLASH_ATTENTION + :annotation: = ['LlamaForCausalLM', 'GPTNeoForCausalLM', 'GPT2ForCausalLM', 'BloomForCausalLM'] + + + +.. py:data:: GPU_SUPPORT_FLASH_ATTENTION + + + + +.. py:data:: GPU_SUPPORT_FLASH_ATTENTION + + + + +.. py:class:: HFDecoderModel(model_args, tune_strategy='normal', ds_config=None, device='gpu', use_accelerator=False, *args, **kwargs) + + Bases: :py:obj:`lmflow.models.decoder_model.DecoderModel`, :py:obj:`lmflow.models.interfaces.tunable.Tunable` + + + Initializes a HFDecoderModel instance. + + + :Parameters: + + **model_args :** + Model arguments such as model name, path, revision, etc. + + **tune_strategy** : str or none, default="normal". + A string representing the dataset backend. Defaults to "huggingface". + + **ds_config :** + Deepspeed configuations. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: tokenize(dataset, add_special_tokens=True, *args, **kwargs) + + + Tokenize the full dataset. + + + :Parameters: + + **dataset** : lmflow.datasets.Dataset. + .. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + tokenized_datasets : + The tokenized dataset, without any leading or trailing special + tokens (normally they are Begin-Of-Sentence or End-Of-Sentence + tokens). + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: encode(input: Union[str, List[str]], *args, **kwargs) -> Union[List[int], List[List[int]]] + + + Perform encoding process of the tokenizer. + + + :Parameters: + + **inputs** : str or list. + The text sequence. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + outputs : + if string input,return the tokenized inputs. + "Hello,world!"-> [101, 7592, 1010, 2088, 102] + if batch input,return {input_ids,attention_mask,token_type_ids} + ["Hello,world!","Hello!"]-> {'input_ids': tensor([[ 101, 7592, 1010, 2088, 102],...),'attention_mask': tensor([[1, 1, 1, 1, 1],[0,0,1,1,1]])} + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: decode(input, *args, **kwargs) -> Union[str, List[str]] + + + Perform decoding process of the tokenizer. + + + :Parameters: + + **inputs** : list or tensor. + The token sequence. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + outputs : + The text decoded from the token inputs. + if batch input,return the list of text + [[101, 7592, 1010, 2088, 102],[101, 7592, 1010, 2088, 102]]-> ["Hello,world!","Hello,world!" + if single input,return the text + [101, 7592, 1010, 2088, 102]-> "Hello,world!" + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: inference(inputs, use_accelerator=False, *args, **kwargs) + + + Perform generation process of the model. + + + :Parameters: + + **inputs :** + The sequence used as a prompt for the generation or as model inputs to the model. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + outputs : + The generated sequence output + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: merge_lora_weights() + + + .. py:method:: save(dir, save_full_model=False, *args, **kwargs) + + + Perform generation process of the model. + + + :Parameters: + + **dir :** + The directory to save model and tokenizer + + **save_full_model** : Optional. + Whether to save full model. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + outputs : + The generated sequence output + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_max_length() + + + Return max acceptable input length in terms of tokens. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_tokenizer() + + + Return the tokenizer of the model. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_backend_model() + + + Return the backend model. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/models/hf_encoder_decoder_model/index.rst.txt b/_sources/autoapi/lmflow/models/hf_encoder_decoder_model/index.rst.txt new file mode 100644 index 000000000..c2353bf61 --- /dev/null +++ b/_sources/autoapi/lmflow/models/hf_encoder_decoder_model/index.rst.txt @@ -0,0 +1,353 @@ +:py:mod:`lmflow.models.hf_encoder_decoder_model` +================================================ + +.. py:module:: lmflow.models.hf_encoder_decoder_model + +.. autoapi-nested-parse:: + + This is a class called HFDecoderModel which is a wrapper around transformers model and + tokenizer classes. It has several methods such as __init__, tokenize, and train that are + used for training and fine-tuning the model. The __init__ method takes in several arguments + such as model_args, tune_strategy, and ds_config, which are used to load the pretrained + model and tokenizer, and initialize the training settings. + + The tokenize method is used to tokenize the input text and return the input IDs and attention + masks that can be fed to the model for training or inference. + + This class supports different tune_strategy options such as 'normal', 'none', 'lora', and + 'adapter', which allow for different fine-tuning settings of the model. However, the 'lora' + and 'adapter' strategies are not yet implemented. + + Overall, this class provides a convenient interface for loading and fine-tuning transformer + models and can be used for various NLP tasks such as language modeling, text classification, + and question answering. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel + + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.models.hf_encoder_decoder_model.logger + + +.. py:data:: logger + + + + +.. py:class:: HFEncoderDecoderModel(model_args, tune_strategy='normal', ds_config=None, device='gpu', use_accelerator=False, custom_model=False, *args, **kwargs) + + Bases: :py:obj:`lmflow.models.encoder_decoder_model.EncoderDecoderModel`, :py:obj:`lmflow.models.interfaces.tunable.Tunable` + + + Initializes a HFEncoderDecoderModel instance. + + + :Parameters: + + **model_args :** + Model arguments such as model name, path, revision, etc. + + **tune_strategy** : str or none, default="normal". + A string representing the dataset backend. Defaults to "huggingface". + + **ds_config :** + Deepspeed configuations. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: tokenize(dataset, *args, **kwargs) + :abstractmethod: + + + Tokenize the full dataset. + + + :Parameters: + + **dataset :** + Text dataset. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + tokenized_datasets : + The tokenized dataset. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: encode(input: Union[str, List[str]], *args, **kwargs) -> Union[List[int], List[List[int]]] + + + Perform encoding process of the tokenizer. + + + :Parameters: + + **inputs** : str or list. + The text sequence. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + outputs : + The tokenized inputs. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: decode(input, *args, **kwargs) -> Union[str, List[str]] + + + Perform decoding process of the tokenizer. + + + :Parameters: + + **inputs** : list. + The token sequence. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + outputs : + The text decoded from the token inputs. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: inference(inputs, *args, **kwargs) + + + Perform generation process of the model. + + + :Parameters: + + **inputs :** + The sequence used as a prompt for the generation or as model inputs to the model. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + outputs : + The generated sequence output + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: merge_lora_weights() + + + .. py:method:: save(dir, save_full_model=False, *args, **kwargs) + + + Perform generation process of the model. + + + :Parameters: + + **dir :** + The directory to save model and tokenizer + + **save_full_model** : Optional. + Whether to save full model. + + **kwargs** : Optional. + Keyword arguments. + + :Returns: + + outputs : + The generated sequence output + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_max_length() + + + Return max acceptable input length in terms of tokens. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_tokenizer() + + + Return the tokenizer of the model. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_backend_model() + + + Return the backend model. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/models/index.rst.txt b/_sources/autoapi/lmflow/models/index.rst.txt new file mode 100644 index 000000000..f85aea57c --- /dev/null +++ b/_sources/autoapi/lmflow/models/index.rst.txt @@ -0,0 +1,32 @@ +:py:mod:`lmflow.models` +======================= + +.. py:module:: lmflow.models + + +Subpackages +----------- +.. toctree:: + :titlesonly: + :maxdepth: 3 + + interfaces/index.rst + + +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + + auto_model/index.rst + base_model/index.rst + decoder_model/index.rst + encoder_decoder_model/index.rst + hf_decoder_model/index.rst + hf_encoder_decoder_model/index.rst + regression_model/index.rst + text_regression_model/index.rst + vision2seq_model/index.rst + + diff --git a/_sources/autoapi/lmflow/models/interfaces/index.rst.txt b/_sources/autoapi/lmflow/models/interfaces/index.rst.txt new file mode 100644 index 000000000..554ede463 --- /dev/null +++ b/_sources/autoapi/lmflow/models/interfaces/index.rst.txt @@ -0,0 +1,15 @@ +:py:mod:`lmflow.models.interfaces` +================================== + +.. py:module:: lmflow.models.interfaces + + +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + + tunable/index.rst + + diff --git a/_sources/autoapi/lmflow/models/interfaces/tunable/index.rst.txt b/_sources/autoapi/lmflow/models/interfaces/tunable/index.rst.txt new file mode 100644 index 000000000..0b4f4dd73 --- /dev/null +++ b/_sources/autoapi/lmflow/models/interfaces/tunable/index.rst.txt @@ -0,0 +1,52 @@ +:py:mod:`lmflow.models.interfaces.tunable` +========================================== + +.. py:module:: lmflow.models.interfaces.tunable + +.. autoapi-nested-parse:: + + Tunable class + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.interfaces.tunable.Tunable + + + + +.. py:class:: Tunable + + Bases: :py:obj:`abc.ABC` + + + Helper class that provides a standard way to create an ABC using + inheritance. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + diff --git a/_sources/autoapi/lmflow/models/regression_model/index.rst.txt b/_sources/autoapi/lmflow/models/regression_model/index.rst.txt new file mode 100644 index 000000000..8ae6a5c42 --- /dev/null +++ b/_sources/autoapi/lmflow/models/regression_model/index.rst.txt @@ -0,0 +1,52 @@ +:py:mod:`lmflow.models.regression_model` +======================================== + +.. py:module:: lmflow.models.regression_model + +.. autoapi-nested-parse:: + + General regression model. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.regression_model.RegressionModel + + + + +.. py:class:: RegressionModel(*args, **kwargs) + + Bases: :py:obj:`lmflow.models.base_model.BaseModel` + + + Helper class that provides a standard way to create an ABC using + inheritance. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + diff --git a/_sources/autoapi/lmflow/models/text_regression_model/index.rst.txt b/_sources/autoapi/lmflow/models/text_regression_model/index.rst.txt new file mode 100644 index 000000000..1128a6135 --- /dev/null +++ b/_sources/autoapi/lmflow/models/text_regression_model/index.rst.txt @@ -0,0 +1,108 @@ +:py:mod:`lmflow.models.text_regression_model` +============================================= + +.. py:module:: lmflow.models.text_regression_model + +.. autoapi-nested-parse:: + + A model maps "text_only" data to float. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.text_regression_model.TextRegressionModel + + + + +.. py:class:: TextRegressionModel(model_args, *args, **kwargs) + + Bases: :py:obj:`lmflow.models.regression_model.RegressionModel` + + + Initializes a TextRegressionModel instance. + + + :Parameters: + + **model_args :** + Model arguments such as model name, path, revision, etc. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: register_inference_function(inference_func) + + + Registers a regression function. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: inference(inputs: lmflow.datasets.dataset.Dataset) + + + Gets regression results of a given dataset. + + :inputs: Dataset object, only accept type "text_only". + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/models/vision2seq_model/index.rst.txt b/_sources/autoapi/lmflow/models/vision2seq_model/index.rst.txt new file mode 100644 index 000000000..c6453703b --- /dev/null +++ b/_sources/autoapi/lmflow/models/vision2seq_model/index.rst.txt @@ -0,0 +1,175 @@ +:py:mod:`lmflow.models.vision2seq_model` +======================================== + +.. py:module:: lmflow.models.vision2seq_model + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.models.vision2seq_model.CustomAutoVision2SeqModel + + + + +.. py:class:: CustomAutoVision2SeqModel(config: transformers.Blip2Config) + + Bases: :py:obj:`transformers.Blip2ForConditionalGeneration`, :py:obj:`lmflow.models.base_model.BaseModel` + + + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: vision_model_from_pretrained(pretrained_path) + + + .. py:method:: qformer_from_pretrained(pretrained_path) + + + .. py:method:: language_model_from_pretrained(pretrained_path, low_resource=False, use_prompt_cache=False) + + + .. py:method:: register_prompt_cache(prompt_ids, prompt_keys_values) + + + Udpate the prompt id and embedding for reuse in the future + + Args: + prompt_ids (torch.LongTensor): The id of the prompt. + prompt_keys_values (torch.FloatTensor): The embedding of the prompt. + + Returns: + None + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: save_prompt_cache(path) + + + Save prompt embedding and id. + + Args: + path: The path to save the prompt embedding and id. + + Returns: + None + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: load_prompt_cache(path) + + + Load prompt embedding and id. + Args: + path: The path to load the prompt embedding and id. + + Returns: + None + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: generate(pixel_values: torch.FloatTensor, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.LongTensor] = None, image_token_indexes: Optional[List] = [0], one_sample_multiple_images: Optional[bool] = False, **generate_kwargs) -> torch.LongTensor + + + Overrides `generate` function to be able to use the model as a conditional generator. + + Args: + pixel_values (`torch.FloatTensor` of shape (batch_size, num_channels, height, width)): + Input images to be processed. + input_ids (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*): + The sequence used as a prompt for the generation. + attention_mask (`torch.LongTensor` of shape (batch_size, sequence_length), *optional*): + Mask to avoid performing attention on padding token indices + image_token_indexes (bool, *optional*): + The index for inserting the image tokens. + one_sample_multiple_images: (bool, *optional*): + The flag for inference that the input batch size is 1 and contain multiple images. + + Returns: + captions (list): A list of strings of length batch_size * num_captions. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/pipeline/auto_pipeline/index.rst.txt b/_sources/autoapi/lmflow/pipeline/auto_pipeline/index.rst.txt new file mode 100644 index 000000000..17e9a35d5 --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/auto_pipeline/index.rst.txt @@ -0,0 +1,66 @@ +:py:mod:`lmflow.pipeline.auto_pipeline` +======================================= + +.. py:module:: lmflow.pipeline.auto_pipeline + +.. autoapi-nested-parse:: + + Return a pipeline automatically based on its name. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.auto_pipeline.AutoPipeline + + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.auto_pipeline.PIPELINE_MAPPING + + +.. py:data:: PIPELINE_MAPPING + + + + +.. py:class:: AutoPipeline + + + The class designed to return a pipeline automatically based on its name. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: get_pipeline(pipeline_name, model_args, data_args, pipeline_args, *args, **kwargs) + :classmethod: + + + diff --git a/_sources/autoapi/lmflow/pipeline/base_aligner/index.rst.txt b/_sources/autoapi/lmflow/pipeline/base_aligner/index.rst.txt new file mode 100644 index 000000000..15e58a613 --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/base_aligner/index.rst.txt @@ -0,0 +1,58 @@ +:py:mod:`lmflow.pipeline.base_aligner` +====================================== + +.. py:module:: lmflow.pipeline.base_aligner + +.. autoapi-nested-parse:: + + BaseTuner: a subclass of BasePipeline. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.base_aligner.BaseAligner + + + + +.. py:class:: BaseAligner(*args, **kwargs) + + Bases: :py:obj:`lmflow.pipeline.base_pipeline.BasePipeline` + + + A subclass of BasePipeline which is alignable. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: _check_if_alignable(model, dataset, reward_model) + + + .. py:method:: align(model, dataset, reward_model) + :abstractmethod: + + + diff --git a/_sources/autoapi/lmflow/pipeline/base_pipeline/index.rst.txt b/_sources/autoapi/lmflow/pipeline/base_pipeline/index.rst.txt new file mode 100644 index 000000000..ff8b0f8e1 --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/base_pipeline/index.rst.txt @@ -0,0 +1,52 @@ +:py:mod:`lmflow.pipeline.base_pipeline` +======================================= + +.. py:module:: lmflow.pipeline.base_pipeline + +.. autoapi-nested-parse:: + + BasePipeline. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.base_pipeline.BasePipeline + + + + +.. py:class:: BasePipeline + + Bases: :py:obj:`abc.ABC` + + + Helper class that provides a standard way to create an ABC using + inheritance. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + diff --git a/_sources/autoapi/lmflow/pipeline/base_tuner/index.rst.txt b/_sources/autoapi/lmflow/pipeline/base_tuner/index.rst.txt new file mode 100644 index 000000000..f49fc3547 --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/base_tuner/index.rst.txt @@ -0,0 +1,58 @@ +:py:mod:`lmflow.pipeline.base_tuner` +==================================== + +.. py:module:: lmflow.pipeline.base_tuner + +.. autoapi-nested-parse:: + + BaseTuner: a subclass of BasePipeline. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.base_tuner.BaseTuner + + + + +.. py:class:: BaseTuner(*args, **kwargs) + + Bases: :py:obj:`lmflow.pipeline.base_pipeline.BasePipeline` + + + A subclass of BasePipeline which is tunable. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: _check_if_tunable(model, dataset) + + + .. py:method:: tune(model, dataset) + :abstractmethod: + + + diff --git a/_sources/autoapi/lmflow/pipeline/evaluator/index.rst.txt b/_sources/autoapi/lmflow/pipeline/evaluator/index.rst.txt new file mode 100644 index 000000000..ada51d1ad --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/evaluator/index.rst.txt @@ -0,0 +1,140 @@ +:py:mod:`lmflow.pipeline.evaluator` +=================================== + +.. py:module:: lmflow.pipeline.evaluator + +.. autoapi-nested-parse:: + + The Evaluator class simplifies the process of running evaluation on a language model provided by a HFDecoderModel instance imported from the lmflow package. The class constructor takes three dictionaries as arguments: model_args containing arguments related to the language model, data_args containing arguments related to the data used for evaluation, and evaluator_args containing other arguments for the evaluation process. + + The class has two methods: create_dataloader() that loads the data from the test file, creates a data loader, and returns it with the size of the data, and evaluate(model) that generates output text given input text. It uses the create_dataloader() method to load the data, iterates over the data in mini-batches, and encodes the input text with the encode() method of the HFDecoderModel class. Then, it generates output text using the evaluate() method of the HFDecoderModel class, decodes the generated output text using the decode() method of the HFDecoderModel class, and writes the output to a file in the output directory. The method also logs some information to the console and Weights and Biases if the use_wandb argument is True. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.evaluator.Evaluator + + + + +.. py:class:: Evaluator(model_args, data_args, evaluator_args) + + Bases: :py:obj:`lmflow.pipeline.base_pipeline.BasePipeline` + + + Initializes the `Evaluator` class with given arguments. + + + :Parameters: + + **model_args** : ModelArguments object. + Contains the arguments required to load the model. + + **data_args** : DatasetArguments object. + Contains the arguments required to load the dataset. + + **evaluator_args** : EvaluatorArguments object. + Contains the arguments required to perform evaluation. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: create_dataloader(dataset: lmflow.datasets.dataset.Dataset) + + + .. py:method:: _match(predicted_answer, groundtruth, answer_type=None) + + + .. py:method:: evaluate(model, dataset: lmflow.datasets.dataset.Dataset, metric='accuracy', verbose=True) + + + Perform Evaluation for a model + + + :Parameters: + + **model** : TunableModel object. + TunableModel to perform inference + + **dataset** : Dataset object. + .. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _evaluate_acc_with_accelerator(model, dataset, verbose=True) + + + .. py:method:: _evaluate_acc_with_deepspeed(model, dataset, verbose=True) + + + .. py:method:: _evaluate_ppl(model, dataset: lmflow.datasets.dataset.Dataset, verbose=True) + + + .. py:method:: _evaluate_nll(model, dataset: lmflow.datasets.dataset.Dataset, verbose=True) + + + Evaluates negative log likelihood of the model over a dataset. + + NLL = -1/N sum_{i=1}^N sum_{j=1}^|w_i| ln(p(w_{i,j}|context_window)), + + where N is the number of data samples, w_{i,j} is the j-th token in + i-th sample. Here "context_window" = p(w_{i,start}, w_{i,start+1}, ..., + p_{i,j-1} with start = max(0, j - window_length + 1). "window_length" + is normally the maximum length accepted by the model. + + Returns: + A float which represents the negative log likelihood. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/pipeline/finetuner/index.rst.txt b/_sources/autoapi/lmflow/pipeline/finetuner/index.rst.txt new file mode 100644 index 000000000..4b19eff81 --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/finetuner/index.rst.txt @@ -0,0 +1,134 @@ +:py:mod:`lmflow.pipeline.finetuner` +=================================== + +.. py:module:: lmflow.pipeline.finetuner + +.. autoapi-nested-parse:: + + The Finetuner class simplifies the process of running finetuning process on a language model for a TunableModel instance with given dataset. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.finetuner.Finetuner + + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.finetuner.logger + + +.. py:data:: logger + + + + +.. py:class:: Finetuner(model_args, data_args, finetuner_args, *args, **kwargs) + + Bases: :py:obj:`lmflow.pipeline.base_tuner.BaseTuner` + + + Initializes the `Finetuner` class with given arguments. + + + :Parameters: + + **model_args** : ModelArguments object. + Contains the arguments required to load the model. + + **data_args** : DatasetArguments object. + Contains the arguments required to load the dataset. + + **finetuner_args** : FinetunerArguments object. + Contains the arguments required to perform finetuning. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: group_text(tokenized_datasets, model_max_length) + + + Groups texts together to form blocks of maximum length `model_max_length` and returns the processed data as + a dictionary. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: tune(model, dataset, transform_dataset_in_place=True) + + + Perform tuning for a model + + + :Parameters: + + **model** : TunableModel object. + TunableModel to perform tuning. + + **dataset:** + dataset to train model. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/pipeline/index.rst.txt b/_sources/autoapi/lmflow/pipeline/index.rst.txt new file mode 100644 index 000000000..9b5b69f4e --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/index.rst.txt @@ -0,0 +1,31 @@ +:py:mod:`lmflow.pipeline` +========================= + +.. py:module:: lmflow.pipeline + + +Subpackages +----------- +.. toctree:: + :titlesonly: + :maxdepth: 3 + + utils/index.rst + + +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + + auto_pipeline/index.rst + base_aligner/index.rst + base_pipeline/index.rst + base_tuner/index.rst + evaluator/index.rst + finetuner/index.rst + inferencer/index.rst + raft_aligner/index.rst + + diff --git a/_sources/autoapi/lmflow/pipeline/inferencer/index.rst.txt b/_sources/autoapi/lmflow/pipeline/inferencer/index.rst.txt new file mode 100644 index 000000000..5f972ba2b --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/inferencer/index.rst.txt @@ -0,0 +1,153 @@ +:py:mod:`lmflow.pipeline.inferencer` +==================================== + +.. py:module:: lmflow.pipeline.inferencer + +.. autoapi-nested-parse:: + + The Inferencer class simplifies the process of model inferencing. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.inferencer.Inferencer + + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.inferencer.rstrip_partial_utf8 + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.inferencer.supported_dataset_type + + +.. py:function:: rstrip_partial_utf8(string) + + +.. py:data:: supported_dataset_type + :annotation: = ['text_only', 'image_text'] + + + +.. py:class:: Inferencer(model_args, data_args, inferencer_args) + + Bases: :py:obj:`lmflow.pipeline.base_pipeline.BasePipeline` + + + Initializes the `Inferencer` class with given arguments. + + + :Parameters: + + **model_args** : ModelArguments object. + Contains the arguments required to load the model. + + **data_args** : DatasetArguments object. + Contains the arguments required to load the dataset. + + **inferencer_args** : InferencerArguments object. + Contains the arguments required to perform inference. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: create_dataloader(dataset: lmflow.datasets.dataset.Dataset) + + + Batchlize dataset and format it to dataloader. + + Args: + dataset (Dataset): the dataset object + + Output: + dataloader (batchlize): the dataloader object + dataset_size (int): the length of the dataset + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: inference(model, dataset: lmflow.datasets.dataset.Dataset, max_new_tokens: int = 100, temperature: float = 0.0, prompt_structure: str = '{input}', remove_image_flag: bool = False) + + + Perform inference for a model + + + :Parameters: + + **model** : TunableModel object. + TunableModel to perform inference + + **dataset** : Dataset object. + .. + + **Returns:** + .. + + **output_dataset: Dataset object.** + .. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: stream_inference(context, model, max_new_tokens, token_per_step, temperature, end_string, input_dataset, remove_image_flag: bool = False) + + + diff --git a/_sources/autoapi/lmflow/pipeline/raft_aligner/index.rst.txt b/_sources/autoapi/lmflow/pipeline/raft_aligner/index.rst.txt new file mode 100644 index 000000000..85aa31384 --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/raft_aligner/index.rst.txt @@ -0,0 +1,243 @@ +:py:mod:`lmflow.pipeline.raft_aligner` +====================================== + +.. py:module:: lmflow.pipeline.raft_aligner + +.. autoapi-nested-parse:: + + The Aligner class simplifies the process of running alignment. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.raft_aligner.RaftAligner + + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.raft_aligner.logger + + +.. py:data:: logger + + + + +.. py:class:: RaftAligner(model_args, data_args, aligner_args, *args, **kwargs) + + Bases: :py:obj:`lmflow.pipeline.base_aligner.BaseAligner` + + + Initializes the `RaftAligner` class with given arguments. + + + :Parameters: + + **model_args** : ModelArguments object. + Contains the arguments required to load the model. + + **data_args** : DatasetArguments object. + Contains the arguments required to load the dataset. + + **raft_aligner_args** : RaftAlignerArguments object. + Contains the arguments required to perform alignment. + + **args** : Optional. + Positional arguments. + + **kwargs** : Optional. + Keyword arguments. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: _initialize_trainer(model, tokenizer, training_args) + + + This function takes the model and tokenizer as the input and initialize the trainer. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _load_dataset(selected_dataset, model, tokenizer, model_args, data_args, training_args) + + + This function prepares the dataset for every iteration. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _load_input_dataset(dataset, tokenizer) + + + Load input dataset (i.e. prompt/question dataset) for training. + + Args: + dataset: A Dataset object. + The dataset to be loaded. + + Returns: + dataloader (`torch.utils.data.DataLoader`): + The dataloader for the dataset. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _clean_text(text) + + + .. py:method:: _discard_sample(text) + + + .. py:method:: _get_batch_dataset_top(model, batch_input, alpha=0.2, iter_id=0, local_rank=0, output_min_length=16, output_max_length=48, infer_batch_size=8, generation_kwargs={}, tokenizer=None, training_args=None, reward_model=None, output_reward_path=None) + + + :param batch_input: input prompts + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _get_batch_dataset_local(model, batch_input, K=8, iter_id=0, local_rank=0, output_min_length=16, output_max_length=48, infer_batch_size=8, generation_kwargs={}, tokenizer=None, training_args=None, reward_model=None, output_reward_path=None) + + + :param batch_input: input prompts + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: align(model, dataset, reward_model) + + + Perform alignment for a model + + + :Parameters: + + **model** : BaseModel object. + .. + + **dataset: Dataset object.** + Input dataset for model to generate outputs. The input and output + will then be feed into reward model to get the reward for + alignment. + + **reward_model: RegressionModel object.** + .. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/pipeline/utils/index.rst.txt b/_sources/autoapi/lmflow/pipeline/utils/index.rst.txt new file mode 100644 index 000000000..441b34a4c --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/utils/index.rst.txt @@ -0,0 +1,16 @@ +:py:mod:`lmflow.pipeline.utils` +=============================== + +.. py:module:: lmflow.pipeline.utils + + +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + + peft_trainer/index.rst + raft_trainer/index.rst + + diff --git a/_sources/autoapi/lmflow/pipeline/utils/peft_trainer/index.rst.txt b/_sources/autoapi/lmflow/pipeline/utils/peft_trainer/index.rst.txt new file mode 100644 index 000000000..295c90969 --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/utils/peft_trainer/index.rst.txt @@ -0,0 +1,249 @@ +:py:mod:`lmflow.pipeline.utils.peft_trainer` +============================================ + +.. py:module:: lmflow.pipeline.utils.peft_trainer + +.. autoapi-nested-parse:: + + Trainer for Peft models + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.utils.peft_trainer.PeftTrainer + lmflow.pipeline.utils.peft_trainer.PeftSavingCallback + + + + +.. py:class:: PeftTrainer(*args, **kwargs) + + Bases: :py:obj:`transformers.Trainer` + + + Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. + + Args: + model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*): + The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed. + + <Tip> + + [`Trainer`] is optimized to work with the [`PreTrainedModel`] provided by the library. You can still use + your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers + models. + + </Tip> + + args ([`TrainingArguments`], *optional*): + The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the + `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided. + data_collator (`DataCollator`, *optional*): + The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will + default to [`default_data_collator`] if no `tokenizer` is provided, an instance of + [`DataCollatorWithPadding`] otherwise. + train_dataset (`torch.utils.data.Dataset` or `torch.utils.data.IterableDataset`, *optional*): + The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the + `model.forward()` method are automatically removed. + + Note that if it's a `torch.utils.data.IterableDataset` with some randomization and you are training in a + distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a + `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will + manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally + sets the seed of the RNGs used. + eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`]), *optional*): + The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the + `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each + dataset prepending the dictionary key to the metric name. + tokenizer ([`PreTrainedTokenizerBase`], *optional*): + The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the + maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an + interrupted training or reuse the fine-tuned model. + model_init (`Callable[[], PreTrainedModel]`, *optional*): + A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start + from a new instance of the model as given by this function. + + The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to + be able to choose different architectures according to hyper parameters (such as layer count, sizes of + inner layers, dropout probabilities etc). + compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*): + The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return + a dictionary string to metric values. + callbacks (List of [`TrainerCallback`], *optional*): + A list of callbacks to customize the training loop. Will add those to the list of default callbacks + detailed in [here](callback). + + If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method. + optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*): A tuple + containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your model + and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*): + A function that preprocess the logits right before caching them at each evaluation step. Must take two + tensors, the logits and the labels, and return the logits once processed as desired. The modifications made + by this function will be reflected in the predictions received by `compute_metrics`. + + Note that the labels (second parameter) will be `None` if the dataset does not have them. + + Important attributes: + + - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`] + subclass. + - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the + original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`, + the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner + model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`. + - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from + data parallelism, this means some of the model layers are split on different GPUs). + - **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set + to `False` if model parallel or deepspeed is used, or if the default + `TrainingArguments.place_model_on_device` is overridden to return `False` . + - **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while + in `train`) + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: _save_checkpoint(_, trial, metrics=None) + + + Don't save base model, optimizer etc. + but create checkpoint folder (needed for saving adapter) + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + +.. py:class:: PeftSavingCallback + + Bases: :py:obj:`transformers.trainer_callback.TrainerCallback` + + + Correctly save PEFT model and not full model + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: _save(model, folder) + + + .. py:method:: on_train_end(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs) + + + Save final best model adapter + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: on_epoch_end(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs) + + + Save intermediate model adapters in case of interrupted training + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: on_save(args: transformers.training_args.TrainingArguments, state: transformers.trainer_callback.TrainerState, control: transformers.trainer_callback.TrainerControl, **kwargs) + + + Event called after a checkpoint save. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/pipeline/utils/raft_trainer/index.rst.txt b/_sources/autoapi/lmflow/pipeline/utils/raft_trainer/index.rst.txt new file mode 100644 index 000000000..e84d9c2aa --- /dev/null +++ b/_sources/autoapi/lmflow/pipeline/utils/raft_trainer/index.rst.txt @@ -0,0 +1,1407 @@ +:py:mod:`lmflow.pipeline.utils.raft_trainer` +============================================ + +.. py:module:: lmflow.pipeline.utils.raft_trainer + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.utils.raft_trainer.RaftTrainer + + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.pipeline.utils.raft_trainer.is_torch_greater_or_equal_than_1_10 + lmflow.pipeline.utils.raft_trainer.is_torch_less_than_1_11 + lmflow.pipeline.utils.raft_trainer._is_native_cpu_amp_available + lmflow.pipeline.utils.raft_trainer.DEFAULT_CALLBACKS + lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK + lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK + lmflow.pipeline.utils.raft_trainer.IS_SAGEMAKER_MP_POST_1_10 + lmflow.pipeline.utils.raft_trainer.skip_first_batches + lmflow.pipeline.utils.raft_trainer.logger + lmflow.pipeline.utils.raft_trainer.TRAINING_ARGS_NAME + lmflow.pipeline.utils.raft_trainer.TRAINER_STATE_NAME + lmflow.pipeline.utils.raft_trainer.OPTIMIZER_NAME + lmflow.pipeline.utils.raft_trainer.SCHEDULER_NAME + lmflow.pipeline.utils.raft_trainer.SCALER_NAME + + +.. py:data:: is_torch_greater_or_equal_than_1_10 + + + + +.. py:data:: is_torch_less_than_1_11 + + + + +.. py:data:: _is_native_cpu_amp_available + + + + +.. py:data:: DEFAULT_CALLBACKS + + + + +.. py:data:: DEFAULT_PROGRESS_CALLBACK + + + + +.. py:data:: DEFAULT_PROGRESS_CALLBACK + + + + +.. py:data:: IS_SAGEMAKER_MP_POST_1_10 + + + + +.. py:data:: skip_first_batches + + + + +.. py:data:: logger + + + + +.. py:data:: TRAINING_ARGS_NAME + :annotation: = training_args.bin + + + +.. py:data:: TRAINER_STATE_NAME + :annotation: = trainer_state.json + + + +.. py:data:: OPTIMIZER_NAME + :annotation: = optimizer.pt + + + +.. py:data:: SCHEDULER_NAME + :annotation: = scheduler.pt + + + +.. py:data:: SCALER_NAME + :annotation: = scaler.pt + + + +.. py:class:: RaftTrainer(model: Union[transformers.modeling_utils.PreTrainedModel, torch.nn.Module] = None, args: transformers.training_args.TrainingArguments = None, data_collator: Optional[transformers.data.data_collator.DataCollator] = None, train_dataset: Optional[torch.utils.data.Dataset] = None, eval_dataset: Optional[Union[torch.utils.data.Dataset, Dict[str, torch.utils.data.Dataset]]] = None, tokenizer: Optional[transformers.tokenization_utils_base.PreTrainedTokenizerBase] = None, model_init: Optional[Callable[[], transformers.modeling_utils.PreTrainedModel]] = None, compute_metrics: Optional[Callable[[transformers.trainer_utils.EvalPrediction], Dict]] = None, callbacks: Optional[List[transformers.trainer_callback.TrainerCallback]] = None, optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None) + + + Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. + Args: + model ([`PreTrainedModel`] or `torch.nn.Module`, *optional*): + The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed. + <Tip> + [`Trainer`] is optimized to work with the [`PreTrainedModel`] provided by the library. You can still use + your own models defined as `torch.nn.Module` as long as they work the same way as the 🤗 Transformers + models. + </Tip> + args ([`TrainingArguments`], *optional*): + The arguments to tweak for training. Will default to a basic instance of [`TrainingArguments`] with the + `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided. + data_collator (`DataCollator`, *optional*): + The function to use to form a batch from a list of elements of `train_dataset` or `eval_dataset`. Will + default to [`default_data_collator`] if no `tokenizer` is provided, an instance of + [`DataCollatorWithPadding`] otherwise. + train_dataset (`torch.utils.data.Dataset` or `torch.utils.data.IterableDataset`, *optional*): + The dataset to use for training. If it is a [`~datasets.Dataset`], columns not accepted by the + `model.forward()` method are automatically removed. + Note that if it's a `torch.utils.data.IterableDataset` with some randomization and you are training in a + distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a + `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will + manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally + sets the seed of the RNGs used. + eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`]), *optional*): + The dataset to use for evaluation. If it is a [`~datasets.Dataset`], columns not accepted by the + `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each + dataset prepending the dictionary key to the metric name. + tokenizer ([`PreTrainedTokenizerBase`], *optional*): + The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the + maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an + interrupted training or reuse the fine-tuned model. + model_init (`Callable[[], PreTrainedModel]`, *optional*): + A function that instantiates the model to be used. If provided, each call to [`~Trainer.train`] will start + from a new instance of the model as given by this function. + The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to + be able to choose different architectures according to hyper parameters (such as layer count, sizes of + inner layers, dropout probabilities etc). + compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*): + The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return + a dictionary string to metric values. + callbacks (List of [`TrainerCallback`], *optional*): + A list of callbacks to customize the training loop. Will add those to the list of default callbacks + detailed in [here](callback). + If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method. + optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`, *optional*): A tuple + containing the optimizer and the scheduler to use. Will default to an instance of [`AdamW`] on your model + and a scheduler given by [`get_linear_schedule_with_warmup`] controlled by `args`. + preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *optional*): + A function that preprocess the logits right before caching them at each evaluation step. Must take two + tensors, the logits and the labels, and return the logits once processed as desired. The modifications made + by this function will be reflected in the predictions received by `compute_metrics`. + Note that the labels (second parameter) will be `None` if the dataset does not have them. + Important attributes: + - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`] + subclass. + - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the + original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`, + the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner + model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`. + - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from + data parallelism, this means some of the model layers are split on different GPUs). + - **place_model_on_device** -- Whether or not to automatically place the model on the device - it will be set + to `False` if model parallel or deepspeed is used, or if the default + `TrainingArguments.place_model_on_device` is overridden to return `False` . + - **is_in_train** -- Whether or not a model is currently running `train` (e.g. when `evaluate` is called while + in `train`) + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: add_callback(callback) + + + Add a callback to the current list of [`~transformer.TrainerCallback`]. + Args: + callback (`type` or [`~transformer.TrainerCallback`]): + A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the + first case, will instantiate a member of that class. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: pop_callback(callback) + + + Remove a callback from the current list of [`~transformer.TrainerCallback`] and returns it. + If the callback is not found, returns `None` (and no error is raised). + Args: + callback (`type` or [`~transformer.TrainerCallback`]): + A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the + first case, will pop the first member of that class found in the list of callbacks. + Returns: + [`~transformer.TrainerCallback`]: The callback removed, if found. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: remove_callback(callback) + + + Remove a callback from the current list of [`~transformer.TrainerCallback`]. + Args: + callback (`type` or [`~transformer.TrainerCallback`]): + A [`~transformer.TrainerCallback`] class or an instance of a [`~transformer.TrainerCallback`]. In the + first case, will remove the first member of that class found in the list of callbacks. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _move_model_to_device(model, device) + + + .. py:method:: _set_signature_columns_if_needed() + + + .. py:method:: _remove_unused_columns(dataset: datasets.Dataset, description: Optional[str] = None) + + + .. py:method:: _get_collator_with_removed_columns(data_collator: Callable, description: Optional[str] = None) -> Callable + + + Wrap the data collator in a callable removing unused columns. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _get_train_sampler() -> Optional[torch.utils.data.Sampler] + + + .. py:method:: get_train_dataloader() -> torch.utils.data.DataLoader + + + Returns the training [`~torch.utils.data.DataLoader`]. + Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed + training if necessary) otherwise. + Subclass and override this method if you want to inject some custom behavior. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _get_eval_sampler(eval_dataset: torch.utils.data.Dataset) -> Optional[torch.utils.data.Sampler] + + + .. py:method:: get_eval_dataloader(eval_dataset: Optional[torch.utils.data.Dataset] = None) -> torch.utils.data.DataLoader + + + Returns the evaluation [`~torch.utils.data.DataLoader`]. + Subclass and override this method if you want to inject some custom behavior. + Args: + eval_dataset (`torch.utils.data.Dataset`, *optional*): + If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted + by the `model.forward()` method are automatically removed. It must implement `__len__`. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_test_dataloader(test_dataset: torch.utils.data.Dataset) -> torch.utils.data.DataLoader + + + Returns the test [`~torch.utils.data.DataLoader`]. + Subclass and override this method if you want to inject some custom behavior. + Args: + test_dataset (`torch.utils.data.Dataset`, *optional*): + The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the + `model.forward()` method are automatically removed. It must implement `__len__`. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: create_optimizer_and_scheduler(num_training_steps: int) + + + Setup the optimizer and the learning rate scheduler. + We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the + Trainer's init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or + `create_scheduler`) in a subclass. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: create_optimizer() + + + Setup the optimizer. + We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the + Trainer's init through `optimizers`, or subclass and override this method in a subclass. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: get_optimizer_cls_and_kwargs(args: transformers.training_args.TrainingArguments) -> Tuple[Any, Any] + :staticmethod: + + + Returns the optimizer class and optimizer parameters based on the training arguments. + Args: + args (`transformers.training_args.TrainingArguments`): + The training arguments for the training session. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: create_scheduler(num_training_steps: int, optimizer: torch.optim.Optimizer = None) + + + Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or + passed as an argument. + Args: + num_training_steps (int): The number of training steps to do. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: num_examples(dataloader: torch.utils.data.DataLoader) -> int + + + Helper to get number of samples in a [`~torch.utils.data.DataLoader`] by accessing its dataset. When + dataloader.dataset does not exist or has no length, estimates as best it can + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _hp_search_setup(trial: Union[optuna.Trial, Dict[str, Any]]) + + + HP search setup code + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _report_to_hp_search(trial: Union[optuna.Trial, Dict[str, Any]], step: int, metrics: Dict[str, float]) + + + .. py:method:: _tune_save_checkpoint() + + + .. py:method:: call_model_init(trial=None) + + + .. py:method:: torch_jit_model_eval(model, dataloader, training=False) + + + .. py:method:: ipex_optimize_model(model, training=False, dtype=torch.float32) + + + .. py:method:: _wrap_model(model, training=True, dataloader=None) + + + .. py:method:: train(resume_from_checkpoint: Optional[Union[str, bool]] = None, trial: Union[optuna.Trial, Dict[str, Any]] = None, ignore_keys_for_eval: Optional[List[str]] = None, is_first_time=False, **kwargs) + + + Main training entry point. + Args: + resume_from_checkpoint (`str` or `bool`, *optional*): + If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a + `bool` and equals `True`, load the last checkpoint in *args.output_dir* as saved by a previous instance + of [`Trainer`]. If present, training will resume from the model/optimizer/scheduler states loaded here. + trial (`optuna.Trial` or `Dict[str, Any]`, *optional*): + The trial run or the hyperparameter dictionary for hyperparameter search. + ignore_keys_for_eval (`List[str]`, *optional*) + A list of keys in the output of your model (if it is a dictionary) that should be ignored when + gathering predictions for evaluation during the training. + kwargs: + Additional keyword arguments used to hide deprecated arguments + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _one_train(batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None) + + + .. py:method:: _inner_training_loop(batch_size=None, args=None, resume_from_checkpoint=None, trial=None, ignore_keys_for_eval=None) + + + 0 This function serves to train one time + 1 Update the self.train_dataset before calling this function + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _get_output_dir(trial) + + + .. py:method:: _load_from_checkpoint(resume_from_checkpoint, model=None) + + + .. py:method:: _load_best_model() + + + .. py:method:: _issue_warnings_after_load(load_result) + + + .. py:method:: _maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) + + + .. py:method:: _load_rng_state(checkpoint) + + + .. py:method:: _save_checkpoint(model, trial, metrics=None) + + + .. py:method:: _load_optimizer_and_scheduler(checkpoint) + + + If optimizer and scheduler states exist, load them. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: hyperparameter_search(hp_space: Optional[Callable[[optuna.Trial], Dict[str, float]]] = None, compute_objective: Optional[Callable[[Dict[str, float]], float]] = None, n_trials: int = 20, direction: str = 'minimize', backend: Optional[Union[str, transformers.trainer_utils.HPSearchBackend]] = None, hp_name: Optional[Callable[[optuna.Trial], str]] = None, **kwargs) -> transformers.trainer_utils.BestRun + + + Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined + by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided, + the sum of all metrics otherwise. + <Tip warning={true}> + To use this method, you need to have provided a `model_init` when initializing your [`Trainer`]: we need to + reinitialize the model at each new run. This is incompatible with the `optimizers` argument, so you need to + subclass [`Trainer`] and override the method [`~Trainer.create_optimizer_and_scheduler`] for custom + optimizer/scheduler. + </Tip> + Args: + hp_space (`Callable[["optuna.Trial"], Dict[str, float]]`, *optional*): + A function that defines the hyperparameter search space. Will default to + [`~trainer_utils.default_hp_space_optuna`] or [`~trainer_utils.default_hp_space_ray`] or + [`~trainer_utils.default_hp_space_sigopt`] depending on your backend. + compute_objective (`Callable[[Dict[str, float]], float]`, *optional*): + A function computing the objective to minimize or maximize from the metrics returned by the `evaluate` + method. Will default to [`~trainer_utils.default_compute_objective`]. + n_trials (`int`, *optional*, defaults to 100): + The number of trial runs to test. + direction (`str`, *optional*, defaults to `"minimize"`): + Whether to optimize greater or lower objects. Can be `"minimize"` or `"maximize"`, you should pick + `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or several metrics. + backend (`str` or [`~training_utils.HPSearchBackend`], *optional*): + The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending + on which one is installed. If all are installed, will default to optuna. + hp_name (`Callable[["optuna.Trial"], str]]`, *optional*): + A function that defines the trial/run name. Will default to None. + kwargs (`Dict[str, Any]`, *optional*): + Additional keyword arguments passed along to `optuna.create_study` or `ray.tune.run`. For more + information see: + - the documentation of + [optuna.create_study](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html) + - the documentation of [tune.run](https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run) + - the documentation of [sigopt](https://app.sigopt.com/docs/endpoints/experiments/create) + Returns: + [`trainer_utils.BestRun`]: All the information about the best run. Experiment summary can be found in + `run_summary` attribute for Ray backend. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: log(logs: Dict[str, float]) -> None + + + Log `logs` on the various objects watching training. + Subclass and override this method to inject custom behavior. + Args: + logs (`Dict[str, float]`): + The values to log. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _prepare_input(data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any] + + + Prepares one `data` before feeding it to the model, be it a tensor or a nested list/dictionary of tensors. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _prepare_inputs(inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]] + + + Prepare `inputs` before feeding them to the model, converting them to tensors if they are not already and + handling potential state. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: compute_loss_context_manager() + + + A helper wrapper to group together context managers. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: autocast_smart_context_manager(cache_enabled: Optional[bool] = True) + + + A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired + arguments, depending on the situation. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: training_step(model: torch.nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor + + + Perform a training step on a batch of inputs. + Subclass and override to inject custom behavior. + Args: + model (`nn.Module`): + The model to train. + inputs (`Dict[str, Union[torch.Tensor, Any]]`): + The inputs and targets of the model. + The dictionary will be unpacked before being fed to the model. Most models expect the targets under the + argument `labels`. Check your model's documentation for all accepted arguments. + Return: + `torch.Tensor`: The tensor with training loss on this batch. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: compute_loss(model, inputs, return_outputs=False) + + + How the loss is computed by Trainer. By default, all models return the loss in the first element. + Subclass and override for custom behavior. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: is_local_process_zero() -> bool + + + Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several + machines) main process. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: is_world_process_zero() -> bool + + + Whether or not this process is the global main process (when training in a distributed fashion on several + machines, this is only going to be `True` for one process). + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: save_model(output_dir: Optional[str] = None, _internal_call: bool = False) + + + Will save the model, so you can reload it using `from_pretrained()`. + Will only save from the main process. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _save_tpu(output_dir: Optional[str] = None) + + + .. py:method:: _save(output_dir: Optional[str] = None, state_dict=None) + + + .. py:method:: store_flos() + + + .. py:method:: _sorted_checkpoints(output_dir=None, checkpoint_prefix=PREFIX_CHECKPOINT_DIR, use_mtime=False) -> List[str] + + + .. py:method:: _rotate_checkpoints(use_mtime=False, output_dir=None) -> None + + + .. py:method:: evaluate(eval_dataset: Optional[torch.utils.data.Dataset] = None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = 'eval') -> Dict[str, float] + + + Run evaluation and returns metrics. + The calling script will be responsible for providing a method to compute metrics, as they are task-dependent + (pass it to the init `compute_metrics` argument). + You can also subclass and override this method to inject custom behavior. + Args: + eval_dataset (`Dataset`, *optional*): + Pass a dataset if you wish to override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns + not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__` + method. + ignore_keys (`Lst[str]`, *optional*): + A list of keys in the output of your model (if it is a dictionary) that should be ignored when + gathering predictions. + metric_key_prefix (`str`, *optional*, defaults to `"eval"`): + An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named + "eval_bleu" if the prefix is "eval" (default) + Returns: + A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The + dictionary also contains the epoch number which comes from the training state. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: predict(test_dataset: torch.utils.data.Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = 'test') -> transformers.trainer_utils.PredictionOutput + + + Run prediction and returns predictions and potential metrics. + Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method + will also return metrics, like in `evaluate()`. + Args: + test_dataset (`Dataset`): + Dataset to run the predictions on. If it is an `datasets.Dataset`, columns not accepted by the + `model.forward()` method are automatically removed. Has to implement the method `__len__` + ignore_keys (`Lst[str]`, *optional*): + A list of keys in the output of your model (if it is a dictionary) that should be ignored when + gathering predictions. + metric_key_prefix (`str`, *optional*, defaults to `"test"`): + An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named + "test_bleu" if the prefix is "test" (default) + <Tip> + If your predictions or labels have different sequence length (for instance because you're doing dynamic padding + in a token classification task) the predictions will be padded (on the right) to allow for concatenation into + one array. The padding index is -100. + </Tip> + Returns: *NamedTuple* A namedtuple with the following keys: + - predictions (`np.ndarray`): The predictions on `test_dataset`. + - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some). + - metrics (`Dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained + labels). + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: evaluation_loop(dataloader: torch.utils.data.DataLoader, description: str, prediction_loss_only: Optional[bool] = None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = 'eval') -> transformers.trainer_utils.EvalLoopOutput + + + Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`. + Works both with or without labels. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _nested_gather(tensors, name=None) + + + Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before + concatenating them to `gathered` + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _pad_across_processes(tensor, pad_index=-100) + + + Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so + they can safely be gathered. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: prediction_step(model: torch.nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]], prediction_loss_only: bool, ignore_keys: Optional[List[str]] = None) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]] + + + Perform an evaluation step on `model` using `inputs`. + Subclass and override to inject custom behavior. + Args: + model (`nn.Module`): + The model to evaluate. + inputs (`Dict[str, Union[torch.Tensor, Any]]`): + The inputs and targets of the model. + The dictionary will be unpacked before being fed to the model. Most models expect the targets under the + argument `labels`. Check your model's documentation for all accepted arguments. + prediction_loss_only (`bool`): + Whether or not to return the loss only. + ignore_keys (`Lst[str]`, *optional*): + A list of keys in the output of your model (if it is a dictionary) that should be ignored when + gathering predictions. + Return: + Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, + logits and labels (each being optional). + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: floating_point_ops(inputs: Dict[str, Union[torch.Tensor, Any]]) + + + For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point + operations for every backward + forward pass. If using another model, either implement such a method in the + model or subclass and override this method. + Args: + inputs (`Dict[str, Union[torch.Tensor, Any]]`): + The inputs and targets of the model. + Returns: + `int`: The number of floating-point operations. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: init_git_repo(at_init: bool = False) + + + Initializes a git repo in `self.args.hub_model_id`. + Args: + at_init (`bool`, *optional*, defaults to `False`): + Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is + `True` and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped + out. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: create_model_card(language: Optional[str] = None, license: Optional[str] = None, tags: Union[str, List[str], None] = None, model_name: Optional[str] = None, finetuned_from: Optional[str] = None, tasks: Union[str, List[str], None] = None, dataset_tags: Union[str, List[str], None] = None, dataset: Union[str, List[str], None] = None, dataset_args: Union[str, List[str], None] = None) + + + Creates a draft of a model card using the information available to the `Trainer`. + Args: + language (`str`, *optional*): + The language of the model (if applicable) + license (`str`, *optional*): + The license of the model. Will default to the license of the pretrained model used, if the original + model given to the `Trainer` comes from a repo on the Hub. + tags (`str` or `List[str]`, *optional*): + Some tags to be included in the metadata of the model card. + model_name (`str`, *optional*): + The name of the model. + finetuned_from (`str`, *optional*): + The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo + of the original model given to the `Trainer` (if it comes from the Hub). + tasks (`str` or `List[str]`, *optional*): + One or several task identifiers, to be included in the metadata of the model card. + dataset_tags (`str` or `List[str]`, *optional*): + One or several dataset tags, to be included in the metadata of the model card. + dataset (`str` or `List[str]`, *optional*): + One or several dataset identifiers, to be included in the metadata of the model card. + dataset_args (`str` or `List[str]`, *optional*): + One or several dataset arguments, to be included in the metadata of the model card. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _push_from_checkpoint(checkpoint_folder) + + + .. py:method:: push_to_hub(commit_message: Optional[str] = 'End of training', blocking: bool = True, **kwargs) -> str + + + Upload *self.model* and *self.tokenizer* to the 🤗 model hub on the repo *self.args.hub_model_id*. + Parameters: + commit_message (`str`, *optional*, defaults to `"End of training"`): + Message to commit while pushing. + blocking (`bool`, *optional*, defaults to `True`): + Whether the function should return only when the `git push` has finished. + kwargs: + Additional keyword arguments passed along to [`~Trainer.create_model_card`]. + Returns: + The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of + the commit and an object to track the progress of the commit if `blocking=True` + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: prediction_loop(dataloader: torch.utils.data.DataLoader, description: str, prediction_loss_only: Optional[bool] = None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = 'eval') -> transformers.trainer_utils.EvalLoopOutput + + + Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`. + Works both with or without labels. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _gather_and_numpify(tensors, name) + + + Gather value of `tensors` (tensor or list/tuple of nested tensors) and convert them to numpy before + concatenating them to `gathered` + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: _add_sm_patterns_to_gitignore() -> None + + + Add SageMaker Checkpointing patterns to .gitignore file. + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + diff --git a/_sources/autoapi/lmflow/utils/constants/index.rst.txt b/_sources/autoapi/lmflow/utils/constants/index.rst.txt new file mode 100644 index 000000000..d20695117 --- /dev/null +++ b/_sources/autoapi/lmflow/utils/constants/index.rst.txt @@ -0,0 +1,61 @@ +:py:mod:`lmflow.utils.constants` +================================ + +.. py:module:: lmflow.utils.constants + +.. autoapi-nested-parse:: + + Commonly used constants. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +.. py:data:: TEXT_ONLY_DATASET_DESCRIPTION + + + + +.. py:data:: TEXT_ONLY_DATASET_DETAILS + + + + +.. py:data:: TEXT2TEXT_DATASET_DESCRIPTION + + + + +.. py:data:: TEXT2TEXT_DATASET_DETAILS + + + + +.. py:data:: FLOAT_ONLY_DATASET_DESCRIPTION + + + + +.. py:data:: TEXT_ONLY_DATASET_LONG_DESCRITION + + + + +.. py:data:: TEXT2TEXT_DATASET_LONG_DESCRITION + + + + +.. py:data:: DATASET_DESCRIPTION_MAP + + + + +.. py:data:: INSTANCE_FIELDS_MAP + + + + diff --git a/_sources/autoapi/lmflow/utils/data_utils/index.rst.txt b/_sources/autoapi/lmflow/utils/data_utils/index.rst.txt new file mode 100644 index 000000000..2ab1e180d --- /dev/null +++ b/_sources/autoapi/lmflow/utils/data_utils/index.rst.txt @@ -0,0 +1,169 @@ +:py:mod:`lmflow.utils.data_utils` +================================= + +.. py:module:: lmflow.utils.data_utils + +.. autoapi-nested-parse:: + + The program includes several functions: setting a random seed, + loading data from a JSON file, batching data, and extracting answers from generated text. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.data_utils.set_random_seed + lmflow.utils.data_utils.load_data + lmflow.utils.data_utils.batchlize + lmflow.utils.data_utils.answer_extraction + lmflow.utils.data_utils.process_image_flag + + + +.. py:function:: set_random_seed(seed: int) + + + Set the random seed for `random`, `numpy`, `torch`, `torch.cuda`. + + + :Parameters: + + **seed** : int + The default seed. + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + +.. py:function:: load_data(file_name: str) + + + Load data with file name. + + + :Parameters: + + **file_name** : str. + The dataset file name. + + :Returns: + + **inputs** : list. + The input texts of the dataset. + + **outputs** : list. + The output texts file datasets. + + **len** : int. + The length of the dataset. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + +.. py:function:: batchlize(examples: list, batch_size: int, random_shuffle: bool) + + + Convert examples to a dataloader. + + + :Parameters: + + **examples** : list. + Data list. + + **batch_size** : int. + .. + + **random_shuffle** : bool + If true, the dataloader shuffle the training data. + + :Returns: + + dataloader: + Dataloader with batch generator. + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + +.. py:function:: answer_extraction(response, answer_type=None) + + + Use this funtion to extract answers from generated text + + + :Parameters: + + **args :** + Arguments. + + **response** : str + plain string response. + + :Returns: + + answer: + Decoded answer (such as A, B, C, D, E for mutiple-choice QA). + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + +.. py:function:: process_image_flag(text, image_flag='<ImageHere>') + + diff --git a/_sources/autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.rst.txt b/_sources/autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.rst.txt new file mode 100644 index 000000000..d22cedb0f --- /dev/null +++ b/_sources/autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.rst.txt @@ -0,0 +1,30 @@ +:py:mod:`lmflow.utils.flash_attention.bloom_flash_attention` +============================================================ + +.. py:module:: lmflow.utils.flash_attention.bloom_flash_attention + + +Module Contents +--------------- + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.flash_attention.bloom_flash_attention.forward + lmflow.utils.flash_attention.bloom_flash_attention._prepare_attn_mask + lmflow.utils.flash_attention.bloom_flash_attention.replace_bloom_attn_with_flash_attn + + + +.. py:function:: forward(self, hidden_states: torch.Tensor, residual: torch.Tensor, alibi: torch.Tensor, attention_mask: torch.Tensor, layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, head_mask: Optional[torch.Tensor] = None, use_cache: bool = False, output_attentions: bool = False) + + +.. py:function:: _prepare_attn_mask(self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int) -> torch.BoolTensor + + +.. py:function:: replace_bloom_attn_with_flash_attn() + + diff --git a/_sources/autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.rst.txt b/_sources/autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.rst.txt new file mode 100644 index 000000000..0b004e3b7 --- /dev/null +++ b/_sources/autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.rst.txt @@ -0,0 +1,30 @@ +:py:mod:`lmflow.utils.flash_attention.gpt2_flash_attention` +=========================================================== + +.. py:module:: lmflow.utils.flash_attention.gpt2_flash_attention + + +Module Contents +--------------- + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.flash_attention.gpt2_flash_attention.forward + lmflow.utils.flash_attention.gpt2_flash_attention._prepare_decoder_attention_mask + lmflow.utils.flash_attention.gpt2_flash_attention.replace_gpt2_attn_with_flash_attn + + + +.. py:function:: forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]], layer_past: Optional[Tuple[torch.Tensor]] = None, attention_mask: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = False, output_attentions: Optional[bool] = False) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], Ellipsis] + + +.. py:function:: _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length) + + +.. py:function:: replace_gpt2_attn_with_flash_attn() + + diff --git a/_sources/autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.rst.txt b/_sources/autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.rst.txt new file mode 100644 index 000000000..f036aa16e --- /dev/null +++ b/_sources/autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.rst.txt @@ -0,0 +1,30 @@ +:py:mod:`lmflow.utils.flash_attention.gpt_neo_flash_attention` +============================================================== + +.. py:module:: lmflow.utils.flash_attention.gpt_neo_flash_attention + + +Module Contents +--------------- + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.flash_attention.gpt_neo_flash_attention._attn + lmflow.utils.flash_attention.gpt_neo_flash_attention.forward + lmflow.utils.flash_attention.gpt_neo_flash_attention.replace_gpt_neo_attn_with_flash_attn + + + +.. py:function:: _attn(self, query, key, value, attention_mask=None, head_mask=None) + + +.. py:function:: forward(self, hidden_states, attention_mask=None, layer_past=None, head_mask=None, use_cache=False, output_attentions=False) + + +.. py:function:: replace_gpt_neo_attn_with_flash_attn() + + diff --git a/_sources/autoapi/lmflow/utils/flash_attention/index.rst.txt b/_sources/autoapi/lmflow/utils/flash_attention/index.rst.txt new file mode 100644 index 000000000..685bdd011 --- /dev/null +++ b/_sources/autoapi/lmflow/utils/flash_attention/index.rst.txt @@ -0,0 +1,19 @@ +:py:mod:`lmflow.utils.flash_attention` +====================================== + +.. py:module:: lmflow.utils.flash_attention + + +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + + bloom_flash_attention/index.rst + gpt2_flash_attention/index.rst + gpt_neo_flash_attention/index.rst + llama_flash_attention/index.rst + triton_flash_attention/index.rst + + diff --git a/_sources/autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.rst.txt b/_sources/autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.rst.txt new file mode 100644 index 000000000..e764f42d6 --- /dev/null +++ b/_sources/autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.rst.txt @@ -0,0 +1,51 @@ +:py:mod:`lmflow.utils.flash_attention.llama_flash_attention` +============================================================ + +.. py:module:: lmflow.utils.flash_attention.llama_flash_attention + + +Module Contents +--------------- + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.flash_attention.llama_flash_attention.forward + lmflow.utils.flash_attention.llama_flash_attention._prepare_decoder_attention_mask + lmflow.utils.flash_attention.llama_flash_attention.replace_llama_attn_with_flash_attn + + + +.. py:function:: forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]] + + + Input shape: Batch x Time x Channel + + attention_mask: [bsz, q_len] + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + +.. py:function:: _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length) + + +.. py:function:: replace_llama_attn_with_flash_attn() + + diff --git a/_sources/autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.rst.txt b/_sources/autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.rst.txt new file mode 100644 index 000000000..52e2bf93e --- /dev/null +++ b/_sources/autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.rst.txt @@ -0,0 +1,500 @@ +:py:mod:`lmflow.utils.flash_attention.triton_flash_attention` +============================================================= + +.. py:module:: lmflow.utils.flash_attention.triton_flash_attention + +.. autoapi-nested-parse:: + + *Experimental* implementation of FlashAttention in Triton. + Tested with triton==2.0.0.dev20221202. + Triton 2.0 has a new backend (MLIR) but seems like it doesn't yet work for head dimensions + other than 64: + https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207 + We'll update this implementation with the new Triton backend once this is fixed. + + We use the FlashAttention implementation from Phil Tillet a starting point. + https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py + + Changes: + - Implement both causal and non-causal attention. + - Implement both self-attention and cross-attention. + - Support arbitrary seqlens (not just multiples of 128), for both forward and backward. + - Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward. + - Support attention bias. + - Speed up the forward pass a bit, and only store the LSE instead of m and l. + - Make the backward for d=128 much faster by reducing register spilling. + - Optionally parallelize the backward pass across seqlen_k, to deal with the case of + small batch size * nheads. + + Caution: + - This is an *experimental* implementation. The forward pass should be quite robust but + I'm not 100% sure that the backward pass doesn't have race conditions (due to the Triton compiler). + - This implementation has only been tested on A100. + - If you plan to use headdim other than 64 and 128, you should test for race conditions + (due to the Triton compiler), as done in tests/test_flash_attn.py + "test_flash_attn_triton_race_condition". I've tested and fixed many race conditions + for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident + that there are none left for other head dimensions. + + Differences between this Triton version and the CUDA version: + - Triton version doesn't support dropout. + - Triton forward is generally faster than CUDA forward, while Triton backward is + generally slower than CUDA backward. Overall Triton forward + backward is slightly slower + than CUDA forward + backward. + - Triton version doesn't support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor). + - Triton version supports attention bias, while CUDA version doesn't. + + .. + !! processed by numpydoc !! + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc + lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc + lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc + + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.flash_attention.triton_flash_attention._fwd_kernel + lmflow.utils.flash_attention.triton_flash_attention._bwd_preprocess_do_o_dot + lmflow.utils.flash_attention.triton_flash_attention._bwd_store_dk_dv + lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel_one_col_block + lmflow.utils.flash_attention.triton_flash_attention.init_to_zero + lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel + lmflow.utils.flash_attention.triton_flash_attention._flash_attn_forward + lmflow.utils.flash_attention.triton_flash_attention._flash_attn_backward + + + +Attributes +~~~~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.flash_attention.triton_flash_attention.flash_attn_qkvpacked_func + lmflow.utils.flash_attention.triton_flash_attention.flash_attn_kvpacked_func + lmflow.utils.flash_attention.triton_flash_attention.flash_attn_func + + +.. py:function:: _fwd_kernel(Q, K, V, Bias, Out, Lse, TMP, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_ob, stride_oh, stride_om, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: triton.language.constexpr, IS_CAUSAL: triton.language.constexpr, BLOCK_HEADDIM: triton.language.constexpr, EVEN_M: triton.language.constexpr, EVEN_N: triton.language.constexpr, EVEN_HEADDIM: triton.language.constexpr, BLOCK_M: triton.language.constexpr, BLOCK_N: triton.language.constexpr) + + +.. py:function:: _bwd_preprocess_do_o_dot(Out, DO, Delta, stride_ob, stride_oh, stride_om, stride_dob, stride_doh, stride_dom, nheads, seqlen_q, seqlen_q_rounded, headdim, BLOCK_M: triton.language.constexpr, BLOCK_HEADDIM: triton.language.constexpr) + + +.. py:function:: _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M: triton.language.constexpr, EVEN_N: triton.language.constexpr, EVEN_HEADDIM: triton.language.constexpr) + + +.. py:function:: _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD: triton.language.constexpr, BIAS_TYPE: triton.language.constexpr, IS_CAUSAL: triton.language.constexpr, BLOCK_HEADDIM: triton.language.constexpr, EVEN_M: triton.language.constexpr, EVEN_N: triton.language.constexpr, EVEN_HEADDIM: triton.language.constexpr, BLOCK_M: triton.language.constexpr, BLOCK_N: triton.language.constexpr) + + +.. py:function:: init_to_zero(name) + + +.. py:function:: _bwd_kernel(Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_dob, stride_doh, stride_dom, stride_dqb, stride_dqh, stride_dqm, stride_dkb, stride_dkh, stride_dkn, stride_dvb, stride_dvh, stride_dvn, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: triton.language.constexpr, IS_CAUSAL: triton.language.constexpr, BLOCK_HEADDIM: triton.language.constexpr, SEQUENCE_PARALLEL: triton.language.constexpr, EVEN_M: triton.language.constexpr, EVEN_N: triton.language.constexpr, EVEN_HEADDIM: triton.language.constexpr, BLOCK_M: triton.language.constexpr, BLOCK_N: triton.language.constexpr) + + +.. py:function:: _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None) + + +.. py:function:: _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None) + + +.. py:class:: FlashAttnQKVPackedFunc(*args, **kwargs) + + Bases: :py:obj:`torch.autograd.Function` + + + Base class to create custom `autograd.Function` + + To create a custom `autograd.Function`, subclass this class and implement + the :meth:`forward` and :meth:`backward` static methods. Then, to use your custom + op in the forward pass, call the class method ``apply``. Do not call + :meth:`forward` directly. + + To ensure correctness and best performance, make sure you are calling the + correct methods on ``ctx`` and validating your backward function using + :func:`torch.autograd.gradcheck`. + + See :ref:`extending-autograd` for more details on how to use this class. + + Examples:: + + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) + >>> class Exp(Function): + >>> @staticmethod + >>> def forward(ctx, i): + >>> result = i.exp() + >>> ctx.save_for_backward(result) + >>> return result + >>> + >>> @staticmethod + >>> def backward(ctx, grad_output): + >>> result, = ctx.saved_tensors + >>> return grad_output * result + >>> + >>> # Use it by calling the apply method: + >>> # xdoctest: +SKIP + >>> output = Exp.apply(input) + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: forward(ctx, qkv, bias=None, causal=False, softmax_scale=None) + :staticmethod: + + + qkv: (batch, seqlen, 3, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen). + ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen) + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: backward(ctx, do) + :staticmethod: + + + Defines a formula for differentiating the operation with backward mode + automatic differentiation (alias to the vjp function). + + This function is to be overridden by all subclasses. + + It must accept a context :attr:`ctx` as the first argument, followed by + as many outputs as the :func:`forward` returned (None will be passed in + for non tensor outputs of the forward function), + and it should return as many tensors, as there were inputs to + :func:`forward`. Each argument is the gradient w.r.t the given output, + and each returned value should be the gradient w.r.t. the + corresponding input. If an input is not a Tensor or is a Tensor not + requiring grads, you can just pass None as a gradient for that input. + + The context can be used to retrieve tensors saved during the forward + pass. It also has an attribute :attr:`ctx.needs_input_grad` as a tuple + of booleans representing whether each input needs gradient. E.g., + :func:`backward` will have ``ctx.needs_input_grad[0] = True`` if the + first input to :func:`forward` needs gradient computated w.r.t. the + output. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + +.. py:data:: flash_attn_qkvpacked_func + + + + +.. py:class:: FlashAttnKVPackedFunc(*args, **kwargs) + + Bases: :py:obj:`torch.autograd.Function` + + + Base class to create custom `autograd.Function` + + To create a custom `autograd.Function`, subclass this class and implement + the :meth:`forward` and :meth:`backward` static methods. Then, to use your custom + op in the forward pass, call the class method ``apply``. Do not call + :meth:`forward` directly. + + To ensure correctness and best performance, make sure you are calling the + correct methods on ``ctx`` and validating your backward function using + :func:`torch.autograd.gradcheck`. + + See :ref:`extending-autograd` for more details on how to use this class. + + Examples:: + + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) + >>> class Exp(Function): + >>> @staticmethod + >>> def forward(ctx, i): + >>> result = i.exp() + >>> ctx.save_for_backward(result) + >>> return result + >>> + >>> @staticmethod + >>> def backward(ctx, grad_output): + >>> result, = ctx.saved_tensors + >>> return grad_output * result + >>> + >>> # Use it by calling the apply method: + >>> # xdoctest: +SKIP + >>> output = Exp.apply(input) + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None) + :staticmethod: + + + q: (batch, seqlen_q, nheads, headdim) + kv: (batch, seqlen_k, 2, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). + ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: backward(ctx, do) + :staticmethod: + + + Defines a formula for differentiating the operation with backward mode + automatic differentiation (alias to the vjp function). + + This function is to be overridden by all subclasses. + + It must accept a context :attr:`ctx` as the first argument, followed by + as many outputs as the :func:`forward` returned (None will be passed in + for non tensor outputs of the forward function), + and it should return as many tensors, as there were inputs to + :func:`forward`. Each argument is the gradient w.r.t the given output, + and each returned value should be the gradient w.r.t. the + corresponding input. If an input is not a Tensor or is a Tensor not + requiring grads, you can just pass None as a gradient for that input. + + The context can be used to retrieve tensors saved during the forward + pass. It also has an attribute :attr:`ctx.needs_input_grad` as a tuple + of booleans representing whether each input needs gradient. E.g., + :func:`backward` will have ``ctx.needs_input_grad[0] = True`` if the + first input to :func:`forward` needs gradient computated w.r.t. the + output. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + +.. py:data:: flash_attn_kvpacked_func + + + + +.. py:class:: FlashAttnFunc(*args, **kwargs) + + Bases: :py:obj:`torch.autograd.Function` + + + Base class to create custom `autograd.Function` + + To create a custom `autograd.Function`, subclass this class and implement + the :meth:`forward` and :meth:`backward` static methods. Then, to use your custom + op in the forward pass, call the class method ``apply``. Do not call + :meth:`forward` directly. + + To ensure correctness and best performance, make sure you are calling the + correct methods on ``ctx`` and validating your backward function using + :func:`torch.autograd.gradcheck`. + + See :ref:`extending-autograd` for more details on how to use this class. + + Examples:: + + >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD) + >>> class Exp(Function): + >>> @staticmethod + >>> def forward(ctx, i): + >>> result = i.exp() + >>> ctx.save_for_backward(result) + >>> return result + >>> + >>> @staticmethod + >>> def backward(ctx, grad_output): + >>> result, = ctx.saved_tensors + >>> return grad_output * result + >>> + >>> # Use it by calling the apply method: + >>> # xdoctest: +SKIP + >>> output = Exp.apply(input) + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None) + :staticmethod: + + + q: (batch_size, seqlen_q, nheads, headdim) + k, v: (batch_size, seqlen_k, nheads, headdim) + bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k). + For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). + ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k) + + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + .. py:method:: backward(ctx, do) + :staticmethod: + + + Defines a formula for differentiating the operation with backward mode + automatic differentiation (alias to the vjp function). + + This function is to be overridden by all subclasses. + + It must accept a context :attr:`ctx` as the first argument, followed by + as many outputs as the :func:`forward` returned (None will be passed in + for non tensor outputs of the forward function), + and it should return as many tensors, as there were inputs to + :func:`forward`. Each argument is the gradient w.r.t the given output, + and each returned value should be the gradient w.r.t. the + corresponding input. If an input is not a Tensor or is a Tensor not + requiring grads, you can just pass None as a gradient for that input. + + The context can be used to retrieve tensors saved during the forward + pass. It also has an attribute :attr:`ctx.needs_input_grad` as a tuple + of booleans representing whether each input needs gradient. E.g., + :func:`backward` will have ``ctx.needs_input_grad[0] = True`` if the + first input to :func:`forward` needs gradient computated w.r.t. the + output. + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + + +.. py:data:: flash_attn_func + + + + diff --git a/_sources/autoapi/lmflow/utils/index.rst.txt b/_sources/autoapi/lmflow/utils/index.rst.txt new file mode 100644 index 000000000..38235be1b --- /dev/null +++ b/_sources/autoapi/lmflow/utils/index.rst.txt @@ -0,0 +1,26 @@ +:py:mod:`lmflow.utils` +====================== + +.. py:module:: lmflow.utils + + +Subpackages +----------- +.. toctree:: + :titlesonly: + :maxdepth: 3 + + flash_attention/index.rst + position_interpolation/index.rst + + +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + + constants/index.rst + data_utils/index.rst + + diff --git a/_sources/autoapi/lmflow/utils/position_interpolation/index.rst.txt b/_sources/autoapi/lmflow/utils/position_interpolation/index.rst.txt new file mode 100644 index 000000000..027d821b3 --- /dev/null +++ b/_sources/autoapi/lmflow/utils/position_interpolation/index.rst.txt @@ -0,0 +1,15 @@ +:py:mod:`lmflow.utils.position_interpolation` +============================================= + +.. py:module:: lmflow.utils.position_interpolation + + +Submodules +---------- +.. toctree:: + :titlesonly: + :maxdepth: 1 + + llama_rope_scaled_monkey_patch/index.rst + + diff --git a/_sources/autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.rst.txt b/_sources/autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.rst.txt new file mode 100644 index 000000000..668884621 --- /dev/null +++ b/_sources/autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.rst.txt @@ -0,0 +1,86 @@ +:py:mod:`lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch` +============================================================================ + +.. py:module:: lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch + + +Module Contents +--------------- + +Classes +~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding + + + +Functions +~~~~~~~~~ + +.. autoapisummary:: + + lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.replace_llama_with_condense + + + +.. py:class:: CondenseRotaryEmbedding(dim, pi_ratio, ntk_ratio, max_position_embeddings=2048, base=10000, device=None) + + Bases: :py:obj:`torch.nn.Module` + + + Base class for all neural network modules. + + Your models should also subclass this class. + + Modules can also contain other Modules, allowing to nest them in + a tree structure. You can assign the submodules as regular attributes:: + + import torch.nn as nn + import torch.nn.functional as F + + class Model(nn.Module): + def __init__(self): + super().__init__() + self.conv1 = nn.Conv2d(1, 20, 5) + self.conv2 = nn.Conv2d(20, 20, 5) + + def forward(self, x): + x = F.relu(self.conv1(x)) + return F.relu(self.conv2(x)) + + Submodules assigned in this way will be registered, and will have their + parameters converted too when you call :meth:`to`, etc. + + .. note:: + As per the example above, an ``__init__()`` call to the parent class + must be made before assignment on the child. + + :ivar training: Boolean represents whether this module is in training or + evaluation mode. + :vartype training: bool + + + + + + + + + + + + + + + + .. + !! processed by numpydoc !! + .. py:method:: forward(x, seq_len=None) + + + +.. py:function:: replace_llama_with_condense(pi_ratio, ntk_ratio) + + diff --git a/_sources/autoapi/lmflow/version/index.rst.txt b/_sources/autoapi/lmflow/version/index.rst.txt new file mode 100644 index 000000000..d6d1ac2c1 --- /dev/null +++ b/_sources/autoapi/lmflow/version/index.rst.txt @@ -0,0 +1,14 @@ +:py:mod:`lmflow.version` +======================== + +.. py:module:: lmflow.version + + +Module Contents +--------------- + +.. py:data:: __version__ + :annotation: = 0.0.1 + + + diff --git a/_sources/blogs/benchmark.md.txt b/_sources/blogs/benchmark.md.txt new file mode 100644 index 000000000..c09c74868 --- /dev/null +++ b/_sources/blogs/benchmark.md.txt @@ -0,0 +1,253 @@ +# LMFlow Benchmark: An Automatic Evaluation Framework for Open-Source LLMs + +May 9, 2023 + + +## Introduction + +Evaluation of a chat-style Large Language Model (LLM) has been a huge challenge since the breakthrough of ChatGPT. On the one hand, researchers and engineers need a reliable way to compare two models and decide which model to choose under a certain application scenario. On the other hand, they have to monitor the model performance during the training of an LLM to avoid performance issues such as forgetting. + +Recent work of Vicuna introduces comparison methods of human evaluation, a.k.a. Chatbot Arena. They also pioneered the evaluation method by invoking GPT-4 to compare the outputs of two models. However, those methods require expensive human labeling or GPT-4 API calls, which are neither scalable nor convenient for LLM development. + +In this article, we introduce LMFlow benchmark, a new benchmark which provides a cheap and easy-to-use evaluation framework that can help reflect different aspects of LLMs. We have open-sourced the dataset and the code as well, so that everyone in the LLM community can use those toolkits to evaluate, monitor or compare different LLMs. +## Metric +In our evaluation framework, Negative Log Likelihood (NLL) is used for evaluating LLM +![](../_static/nll.png) + + +which corresponds to the LLM model’s prediction probability over a corpus set given their contexts. If the corpus set itself indicates a certain type of LLM ability, such as multi-round conversation, instruction following, math problem solving, role-playing, then NLL on those corpora can provide quantitative metrics to reflect those abilities. + +![](../_static/benchmark-1.png) + +The key idea behind NLL, is that + +*Generation ability is positively correlated with prediction ability.* + +For instance, an LLM which performs well in essay writing should have no problem understanding and predicting a reference human essay, just like human chess masters performing well at memorizing an endgame on a chessboard. + +Besides NLL, another similar and commonly used metric in NLP is Perplexity (PPL): + +![](../_static/ppl.png) + +Nevertheless, perplexity intrinsically depends on the lengths of the tokenized sequences, which induces unfair comparison between models with different tokenizers. For example, if a model has a smaller vocabulary size, it inherently results in a longer tokenized sequence and a lower token-level perplexity. Thus in all our experiments, we use NLL instead of PPL. + +One huge advantage of NLL evaluation is that it does not require human involvement during the evaluation process. As long as the test reference corpus is given, one can evaluate different aspects of an LLM’s ability automatically. This makes the evaluation of LLM more accessible to researchers. + +Besides its convenience, NLL itself is also a good metric. In our experimental results in commonsense QA, we find that NLL is correlated with QA accuracy when comparing the different finetuned versions of a single model. + +**Table 1: Accuracy results in traditional commonsense QA benchmarks** + + +||winogrande|boolq|arc\_e|hellaswag|piqa|obqa|arc\_c|Average| +| :- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | +|bloom-3b|58\.7|61\.6|59\.5|52\.7|70\.8|42\.2|30\.6|53\.7| +|bloom-7.1b|64\.4|62\.9|65\.0|59\.6|73\.6|35\.8|33\.4|56\.3| +|opt-6.9b|65\.2|66\.1|65\.6|67\.2|76\.5|37\.4|34\.6|58\.9| +|opt-13b|65\.0|65\.9|67\.1|69\.8|76\.9|39\.0|35\.7|59\.9| +|llama-7b|67\.9|73\.2|67\.3|73\.0|78\.3|42\.4|41\.4|62\.7| +|llama-13b|**70.0**|**68.5**|**74.5**|**76.2**|**79.1**|**42.2**|**44.5**|**65.0**| + +**Table 2: NLL results in corpus of commonsense QA benchmarks** + + +||winogrande|boolq|arc\_e|hellaswag|piqa|obqa|arc\_c|Average| +| :- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | +|bloom-3b|86\.5|228|86|245|134|64\.5|101\.5|135\.1| +|bloom-7.1b|85|215|81\.5|237|130|62\.5|96|129\.5| +|opt-6.9b|81\.5|200|81\.5|224|125|61|96|124\.1| +|opt-13b|82|198|82\.5|220|125|61\.8|97|123\.7| +|llama-7b|79\.5|167|71\.5|214|121|58|85|113\.7| +|llama-13b|**79**|**153**|**70**|**207**|**119**|**57.3**|**83**|**109.7**| + + +**Figure 1: Correlation between NLL and accuracy on commonsense QA benchmarks** + +![](../_static/benchmark-2.png) + +In the above figure, one can find that QA accuracy is roughly correlated to NLL. Thus NLL is able to reflect the “magnitude” of prediction level difference between models. A huge gap in NLL normally entails a huge performance gap. + +In the following sections, we provide a comprehensive evaluation of currently available LLM models and summarize their performance. Due to page limits, we only demonstrate partial evaluation results in this article. The full results can be found here [LLM Comparison Sheet 0501](https://docs.google.com/spreadsheets/d/1JYh4_pxNzmNA9I0YM2epgRA7VXBIeIGS64gPJBg5NHA/edit?usp=sharing). One can also evaluate their own LLM models using our LMFlow evaluation toolkit (<https://optimalscale.github.io/LMFlow/examples/TASK_GUIDE.html>). + +We also introduce our own finetuned model Robin-7b-Chat and compare its performance with some other popular models. One may try our online demo LMFlow-Chat at <https://lmflow.com/>. + +## Chat Performance +The first aspect of LLM is chat performance, especially multi-round chat. Based on our experimental results, we have the following observations: + +1. *Without finetuning, foundation models do not perform well on chat.* +1. *After finetuning, foundation models can all obtain huge performance gain.* +1. *Our Robin-Chat-7b achieves comparable chat performance with Vicuna-13b.* + + + +**Table 3: NLL on multi-round chat datasets, Lower the better** + + +||En\_multi\_round\_chat|En\_singe\_round\_chat|Average| +| :- | :-: | :-: | :-: | +|Bloom-7.1b|188|213|200\.5| +|Bloom-7.1b-chat|164|181|172\.5| +|Llama-7b|163 |184 |173\.5| +|Vicuna-7b|155 |175 |165| +|Vicuna-13b|150 |170 |160| +|Robin-Chat-7b|**146** |**164** |**155**| + +We also evaluate Vicuna-13b and our Robin-Chat-7b model qualitatively via use cases. It turns out that Robin-Chat-7b’s performance is comparable to Vicuna-13b. For example, both models can effortlessly refer to the conversation context with only slight mistakes, where Vicuna-13b sometimes doesn’t count the item correctly, while Robin-Chat-7b introduces only Mexican foods for international cuisine recommendations and omits other choices. + + + + + + + + + + + + + + + + + + + + + + + + + + +**Table 4: Use cases of multi-round chat** + + +|Round|Question|Vicuna-13b|Robin-Chat-7b| +| :- | :-: | :-: | :-: | +|R1|Where is the capital of USA?|The capital of the United States is **Washington, D.C.** (District of Columbia). It is located on the east coast of the country, in the northern part of the state of Maryland. Washington, D.C. is named after George Washington, the first president of the United States. It is a federal district, which means that it is not part of any state, but is instead directly administered by the federal government. The city is home to many national landmarks, including the White House, the Capitol Building, and the Supreme Court. It is also home to many museums, monuments, and other cultural institutions.|The capital of the United States is **Washington, D.C.** (District of Columbia). It is located on the east coast of the USA, between the states of Maryland and Virginia.| +|R2|Can you introduce some popular food there?|<p>Washington, D.C. is a city with a diverse food scene, and there are many popular restaurants and cuisines to choose from. Some popular foods in Washington, D.C. include:</p><p>- American Classics: There are many restaurants in Washington, D.C. that serve classic American dishes such as hamburgers, hot dogs, and apple pie.</p><p>- Seafood: Washington, D.C. is located near the Chesapeake Bay, which is known for its fresh seafood. Many restaurants in the city serve dishes such as crab cakes, shrimp, and oysters.</p><p>- Ethnic Cuisine: Washington, D.C. has a diverse population, and there are many restaurants that serve food from different parts of the world. Some popular ethnic cuisines include Mexican, Chinese, Japanese, and Korean.</p><p>- Barbecue: Washington, D.C. is also known for its barbecue restaurants. Many of these restaurants serve pulled pork, ribs, and brisket.</p><p>- **Donuts**: Washington, D.C. is home to the famous "Donut Happy" which is a famous donut shop with a variety of flavors and toppings.</p><p>- Street Food: There are also many street food options in Washington, D.C. such as food trucks and stands that serve food such as falafel, kebabs, and tacos.</p><p>These are just a few examples of the many types of food that are available in Washington, D.C. There is something for everyone in this city when it comes to food.</p><p></p>|<p>Washington, D.C. is a diverse city with a wide range of cuisines to choose from. Here are some popular foods that you can try when you visit:</p><p>1. Soul food: This style of cuisine originated in the southern United States and is known for its bold flavors and hearty portions. Some popular soul food dishes in Washington, D.C. include fried chicken, collard greens, and macaroni and cheese.</p><p>2. Ethiopian cuisine: Ethiopian food is known for its spicy and flavorful dishes, and it is a popular choice in Washington, D.C. Some popular dishes include injera (a sourdough flatbread), wat (a spicy vegetable stew), and kitfo (a spicy meat dish).</p><p>3. Seafood: Washington, D.C. is located near the Chesapeake Bay and has easy access to fresh seafood. Some popular seafood dishes include crab cakes, oysters, and seafood pasta.</p><p>4. BBQ: Washington, D.C. has a thriving barbecue scene, with many restaurants serving up slow-cooked meats and sides. Some popular BBQ spots include Ben’s Chili Bowl, Pete’s Soul Vegetarian, and Smoke & Barrel.</p><p>5. **International cuisine:** Washington, D.C. is a hub for international diplomacy, and as a result, it has a diverse culinary scene with many restaurants serving dishes from around the world. Some popular international cuisines include Japanese, Indian, and **Mexican.**</p><p></p>| +|R3|Can you be more specific about the **fifth item** above?|<p>Sure, **the fifth item I mentioned, "Barbecue,"** refers to a style of cooking and food that originated in the southern United States. Barbecue is usually made with meat that is slow-cooked over an open flame or heated coals. The meat is often marinated and then sauced, and it is typically served with a variety of sides such as coleslaw, baked beans, and corn on the cob. Barbecue is a popular cuisine in Washington, D.C. and many of the restaurants serve pulled pork, ribs, and brisket. Some of the most popular barbecue restaurants in Washington, D.C. include:</p><p>- The Rib Pit</p><p>- The Smokehouse</p><p>- The BBQ Joint</p><p>- The Salt Line</p><p>- The Sovereign</p><p>These are just a few examples of the many barbecue restaurants in Washington, D.C. that offer delicious and flavorful barbecue dishes.</p>|<p>Sure! Here are some specific recommendations for **Mexican food** in Washington, D.C.:</p><p>1. Margarita’s - This popular Mexican restaurant in Adams Morgan has been serving up authentic Mexican cuisine for over 30 years. Some of their most popular dishes include their tacos al pastor (grilled marinated pork), enchiladas (meat or cheese filled tortillas), and chiles rellenos (stuffed peppers).</p><p>2. El Comal - This cozy little restaurant in the Columbia Heights neighborhood has been serving up traditional Mexican cuisine for over 20 years. Some of their most popular dishes include their mole (a rich, spicy sauce made from a blend of chiles, spices, and chocolate), tacos de carnitas (crispy pork tacos), and chiles rellenos de queso (cheese stuffed peppers).</p><p>3. Casa Oaxaca - This upscale Mexican restaurant in the Shaw neighborhood has a menu that features a wide range of traditional and modern dishes. Some of their most popular dishes include their ceviche (raw fish marinated in lime juice and spices), mole negro (a rich, dark mole sauce), and chiles en nogada (a spicy dish made with poblano peppers, walnuts, and honey).</p><p>4. Taco King - This popular taco chain has several locations in the Washington, D.C. area, including one in the Adams Morgan neighborhood. They offer a wide range of tacos, burritos, and other Mexican dishes at affordable prices. Some of their most popular items include their carne asada tacos, chicken tacos, and quesadillas.</p><p>5. Los Tacos No. 1 - This small, casual taco stand in the Columbia Heights neighborhood has a menu that features a wide range of tacos, burritos, and other Mexican dishes. Some of their most popular items include their adobada tacos (marinated pork tacos), al pastor tacos (grilled marinated pork tacos), and chicken tacos.</p>| + + +**Experiment Details** + +Our dataset comes from real conversation history between humans and ChatGPT. We collect 183 English conversations with a total of 447 rounds of chats, where the original conversation history is already multi-round. For single-round NLL evaluation, we break a multi-round conversation into multiple single-round text pairs. For example, with a conversation history + + Q1, A1 + + Q2, A2 + + Q3, A3 + +where Q stands for Question, A stands for answer, we can get three single-round examples: + + Context: Q1 + + Output: A1 + + Context: Q1, A1, Q2 + + Output: A2 + + Context: Q1, A1, Q2, A2, Q3 + + Ouptut: A3 + +## CommonSense Performance +Another important aspect of an LLM model is its common sense ability, where a model should acquire a certain level of factual knowledge and utilize them properly under different scenarios. Regarding this aspect of the ability, we found: + +1. *Finetuning on chat dataset results in commonsense degradation.* +1. *Our Robin-Chat-7b model still achieves a competitive performance.* + + + +**Table 5: Accuracy results in commonsense QA benchmarks** + + +||winogrand|boolq|arc\_easy|hellaswag|piqa|obqa|arc\_c|Average| +| :- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | +|Bloom-7.1b|64\.4|62\.9|65\.0|59\.6|73\.6|35\.8|33\.4|56\.4| +|Bloom-7.1b-chat|60\.3|56\.8|61\.3|58\.7|72\.7|37\.8|38\.7|55\.2| +|Llama-7b|67.9 |73\.2 |67.3 |73.0 |78\.4 |42\.4 |41\.4|62\.7| +|Vicuna-7b|63.7 |77\.4 |63.1 |68.8 |76\.3 |39\.6 |38\.7|61\.1| +|Vicuna-13b|66.2 |79\.9 |64.7 |73.0 |77\.6 |41\.6 |40\.4|63\.3| +|Robin-Chat-7b|64.7 |75\.2 |69.8 |72.4 |76\.6 |39\.0 |42\.9|62\.9| + + + +**Table 6: NLL results in corpus of commonsense QA benchmarks** + + +||winogrand|boolq|arc\_easy|hellaswag|piqa|obqa|arc\_c|Average| +| :- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | :-: | +|Bloom-7.1b|96.0|254|89|266|147|69|106\.5|146\.7| +|Bloom-7.1b-chat|85.0|215|81\.5|237|130|62\.5|96|129\.5| +|Llama-7b|79.5 |167 |71.5 |214 |121 |58 |85|113\.7| +|Vicuna-7b|97.0 |219 |89 |258 |151 |72 |105|141\.6| +|Vicuna-13b|95\.5 |207|88.5 |262 |149 |73 |105|140\.0| +|Robin-Chat-7b|89\.0 |200 |81 |240 |139 |65\.5 |96\.5|130\.1| + +It is surprising to find that finetuning actually leads to commonsense degradation. A more well-known consequence of this issue is hallucination, where models tend to give plausible but factually incorrect or irrelevant answers. One explanation of this phenomenon is that LLMs “forget” the pretrained knowledge during finetuning when attempting to fit the finetune dataset. + +As one can see from the table above, both Vicuna and Robin suffer from this commonsense degradation issue. However, Robin suffers less. We conjecture that this improvement is brought by finetuning with smaller-sized but higher-quality dataset. Vicuna uses sharegpt data with ~900MB size and was finetuned for 3 epochs, while Robin uses multiple sources of dataset with ~600MB size and was only finetuned for 2 epochs. This suggests that in the finetuning stage, data quality plays a critical role. + +This improvement gives Vicuna-13b a run for its money when competing with Robin. The following examples show that Robin-Chat-7b possesses a comparable level of commonsense when compared with Vicuna-13b. + +**Table 7: Use cases of commonsense QA** + + +|Question|Vicuna-13b|Robin-Chat-7b|Note| +| :- | :-: | :-: | :-: | +|<p>I go to a friend's home but the door is locked. However, I smell coal gas. Should I break the window?</p><p></p>|If you smell coal gas, it is important to get out of the house as quickly as possible and call the fire department or the gas company. Coal gas is a flammable gas that can be dangerous and even deadly if it is not properly ventilated. If the door is locked, you **should not break the window because it could cause a fire or explosion**. Instead, try to find another way to escape the house or call for help. If you cannot leave the house safely, try to stay calm and wait for help to arrive.|No, it is not recommended to break a window in this situation. **Breaking a window can be dangerous and can cause injury or damage to yourself or others**. Additionally, it is **illegal** to break a window to enter a property without permission. Instead, you should try to find another way to enter the home, such as by using a key or by calling the homeowner or a locksmith for assistance. If you are unable to enter the home, you should call the emergency services or the police for assistance. In the meantime, it is important to **avoid using any sources of heat**, such as a lighter or matches, as these can cause a gas leak or explosion. If you smell gas, you should evacuate the area immediately and call the emergency services or the gas company for assistance.|*Breaking the window helps the ventilation and will not cause a gas explosion directly. However, it is indeed illegal and may injure yourself when breaking the window.*| +|Can I bring my pet ferret to that beautiful pacific island of USA to attend ICML 2023?|I'm sorry, but I am **not aware of** the specific policies regarding pets at the ICML 2023 conference or the island you are referring to. It would be best to check with the conference organizers or the local authorities on the island to determine if ferrets are allowed and if there are any necessary procedures or requirements for bringing a pet to the conference.|<p>It is **not recommended** to bring a pet ferret to the United States, including to attend ICML 2023. Ferrets are not permitted as pets in many areas of the United States, and **it is illegal** to bring them into the country without proper documentation and licensing. Additionally, ferrets can **carry diseases** that are not common in humans but can be dangerous to people, such as toxoplasmosis and salmonellosis. It is important to prioritize the health and safety of yourself and others by not bringing a pet ferret to a crowded event like ICML 2023.</p><p></p>|*ICML 2023 will be held in Hawaii, where ferrets are strictly prohibited as pets because they are potential carriers of the rabies virus.*| + +**Experiment Details** + +Here, we make use of Eleuther AI’s LM evaluation harness repository (<https://github.com/EleutherAI/lm-evaluation-harness>) to get QA accuracy results. We also evaluate all models’ NLL metrics on their datasets, with their questions as contexts and answers as output sentences. + +## Instruction Following +The final aspect we evaluate is instruction following, where a model is expected to loyally obey the user’s instructions, given the instruction being ethically or legally appropriate. It turns out that, + +1. *Without finetuning, foundation models do not perform well on instruction following.* +1. *Finetuning on chat dataset helps improve the performance.* +1. *Our Robin-Chat is still comparable with Vicuna-13b.* + +**Table 8: NLL on instruction following chat datasets** + + +||gpt4\_en\_instruction| +| :- | :-: | +|Alpaca|222| +|Llama-7b|214 | +|Vicuna-7b|211 | +|Vicuna-13b|206 | +|Robin-Chat-7b|**180** | + +Following examples show that Robin-Chat-7b can generate similar responses on instruction following tasks when compared with Vicuna-13b. + +**Table 9: Uses cases of instruction following** + + +|Question|Vicuna-13b|Robin-Chat-7b| +| :- | :-: | :-: | +|Can you write quicksort?|<p>Yes, here is a simple implementation of the quicksort algorithm in Python:</p><p>def quicksort(arr):</p><p>` `if len(arr) <= 1:</p><p>` `return arr</p><p>` `pivot = arr[len(arr) // 2]</p><p>` `left = [x for x in arr if x < pivot]</p><p>` `middle = [x for x in arr if x == pivot]</p><p>` `right = [x for x in arr if x > pivot]</p><p>` `return quicksort(left) + middle + quicksort(right)</p><p></p><p>This implementation uses the middle element of the array as the pivot, and then partitions the array into three sub-arrays: one containing all elements that are less than the pivot, one containing all elements that are equal to the pivot, and one containing all elements that are greater than the pivot. It then recursively applies the quicksort algorithm to the left and right sub-arrays, and returns the concatenation of the sorted left sub-array, the unsorted middle sub-array, and the sorted right sub-array.</p><p>This implementation has a time complexity of O(n log n) due to the recursive calls, although it could potentially be faster in practice if the pivot is carefully chosen.</p><p></p>|<p>Sure, here is a simple implementation of the quicksort algorithm in Python:</p><p>def quicksort(arr):</p><p>` `if len(arr) <= 1:</p><p>` `return arr</p><p>` `pivot = arr[len(arr) // 2]</p><p>` `left = [x for x in arr if x < pivot]</p><p>` `middle = [x for x in arr if x == pivot]</p><p>` `right = [x for x in arr if x > pivot]</p><p>` `return quicksort(left) + middle + quicksort(right)</p><p></p><p>This implementation chooses the middle element of the array as the pivot, and then partitions the array into three parts: elements less than the pivot, elements equal to the pivot, and elements greater than the pivot. It then recursively applies the quicksort algorithm to the left and right subarrays, and concatenates the results with the middle subarray to produce the final sorted array.</p><p>Note that this implementation uses a single pass through the array, which is the most efficient way to sort an array using quicksort.</p>| +|Instruction: Find synonyms for the given word. You need to write down how the provided synonyms differ from the original word in terms of meaning, usage, etc. adversity|<p>Synonyms for "adversity" include:</p><p>- Difficulty</p><p>- Hardship</p><p>- Trouble</p><p>- Misfortune</p><p>- Adversity</p><p>- Difficulties</p><p>- Hard times</p><p>All of these words refer to challenging or unpleasant circumstances or situations. However, "adversity" specifically connotes a difficult or hostile situation that is caused by external factors, such as an enemy or a natural disaster. "Difficulty" and "hardship" can be caused by external or internal factors, and can refer to a wide range of challenges, from mild inconveniences to major setbacks. "Trouble" often connotes a specific type of difficulty or distress, such as financial trouble or legal trouble. "Misfortune" specifically refers to bad luck or ill fate, while "hard times" refers to a general period of economic or social distress.</p>|<p>1. Hardship</p><p>2. Struggle</p><p>3. Difficulty</p><p>4. Oppression</p><p>5. Trial</p><p>6. Affliction</p><p>7. Distress</p><p>8. Calamity</p><p>9. Tribulation</p><p>10. Hardship</p><p>Adversity refers to difficult or challenging circumstances or conditions. Hardship, struggle, difficulty, oppression, trial, affliction, distress, and calamity are all words that can be used to describe adversity, but they often imply a more specific or intense situation. For example, hardship can refer to a general difficulty or challenge, while struggle implies a more intense battle or conflict. Difficulty and oppression imply a more specific obstacle or injustice, while trial and affliction suggest a more specific test or suffering. Trial can also refer to a legal proceeding, while tribulation implies a more general time of suffering or persecution.</p>| + +**Experiment Details** + +In order to make evaluations for models’ instruction following ability, we collected GPT-4 instruction data from the project “Instruction Tuning with GPT-4”, which provides many instruction tuning data labeled by GPT-4. We sample 1,000 English data and manually filtered examples with the following issues, where 767 effective samples remain after the filtering: + +1. Long response with too many nonsense words +1. Incomplete input texts +1. Specific domains involving chemistry/biology, where most LLM models do not possess the knowledge and always fail + +## Conclusion +In this article, we introduce LMFlow’s evaluation framework, which uses NLL metric to reflect LLM models’ ability. NLL provides a good metric to evaluate different aspects of a LLM model. According to our evaluation results, Robin-7b achieves on-par performance when compared with Vicuna-13b. As our Robin-7b model is finetuned with different sources of dataset instead of sharegpt only, this shows that Vicuna can be further improved or surpassed with smaller-sized models and better dataset. + +The checkpoint of Robin-7b is now available for engineers and researchers to download and use (<https://github.com/OptimalScale/LMFlow#model-zoo>). Its effectiveness demonstrates that a multi-aspect evaluation is indeed essential to the development of LLMs. + +## References +Vicuna Chatbot Arena: <https://chat.lmsys.org/?arena> + +lm-evaluation-harness: <https://github.com/EleutherAI/lm-evaluation-harness> + +LMFlow: <https://github.com/OptimalScale/LMFlow> diff --git a/_sources/blogs/index.md.txt b/_sources/blogs/index.md.txt new file mode 100644 index 000000000..d71f731f2 --- /dev/null +++ b/_sources/blogs/index.md.txt @@ -0,0 +1,13 @@ +# Blogs + +## 2023 + + +```{toctree} +:maxdepth: 1 + +benchmark +``` + + + diff --git a/_sources/examples/DATASETS.md.txt b/_sources/examples/DATASETS.md.txt new file mode 100644 index 000000000..2c807aeb7 --- /dev/null +++ b/_sources/examples/DATASETS.md.txt @@ -0,0 +1,112 @@ +# Dataset + +We provide several available datasets under `data`. You may download them all by running: +```sh +cd data && ./download.sh all && cd - +``` +You can replace `all` with a specific dataset name to only download that dataset (e.g. `./download.sh alpaca`). + +Customized datasets are strongly encouraged, since this way users can apply +their own prompt engineering techniques over various source datasets. As long +as the generated dataset following the format below, they can be accepted as +the input of our pipelines :hugs: + + +## Dataset Format in General + +To specify the input for model finetune, users can provide a list of `.json` +files under a specified dataset directory. For example, + +```sh +|- path_to_dataset + |- data_1.json + |- data_2.json + |- another_data.json + |- ... +``` + +For inference, we currently only support a single `.json` file. + +Each json file shall have the following format (three instances with four keys +for example), + +```json +{ + "type": "TYPE", + "instances": [ + { + "KEY_1": "VALUE_1.1", + "KEY_2": "VALUE_1.2", + "KEY_3": "VALUE_1.3", + "KEY_4": "VALUE_1.4", + }, + { + "KEY_1": "VALUE_2.1", + "KEY_2": "VALUE_2.2", + "KEY_3": "VALUE_2.3", + "KEY_4": "VALUE_2.4", + }, + { + "KEY_1": "VALUE_3.1", + "KEY_2": "VALUE_3.2", + "KEY_3": "VALUE_3.3", + "KEY_4": "VALUE_3.4", + }, + ] +} +``` + +where the `TYPE` indicates the dataset type and defines the set of keys +`{ KEY_1, KEY_2, ... }` and their corresponding interpretations. The list of +supported types are listed as follows. + +## Supported Dataset and Detailed Formats + +### TextOnly + +This is the most common dataset type, which only contains raw texts in each +sample. This type of dataset can be used as the training set for text decoder +models, or the input of decoder models / encoder-decoder models. Its format is +as follows (three instances for example), + +```json +{ + "type": "text_only", + "instances": [ + { "text": "SAMPLE_TEXT_1" }, + { "text": "SAMPLE_TEXT_2" }, + { "text": "SAMPLE_TEXT_3" }, + ] +} +``` + +For example, `data/example_dataset/train/train_50.json` has the aboved format. + +### Text2Text + +This is the dataset type mostly used for inferencing, which contains a pair of +texts in each sample. This type of dataset can be used as the training set for +text encoder-decoder models, or question-answer pair for evaluating model +inferences. Its format is as follows (three instances for example), + +```json +{ + "type": "text2text", + "instances": [ + { + "input": "SAMPLE_INPUT_1", + "output": "SAMPLE_OUTPUT_1", + }, + { + "input": "SAMPLE_INPUT_2", + "output": "SAMPLE_OUTPUT_2", + }, + { + "input": "SAMPLE_INPUT_3", + "output": "SAMPLE_OUTPUT_3", + }, + ] +} +``` + +For example, `data/example_dataset/test/test_13.json` has the aboved format. diff --git a/_sources/examples/TASK_GUIDE.md.txt b/_sources/examples/TASK_GUIDE.md.txt new file mode 100644 index 000000000..afbfd3ca8 --- /dev/null +++ b/_sources/examples/TASK_GUIDE.md.txt @@ -0,0 +1,132 @@ +# LMFlow Benchmark Guide + +We support two ways to add evaluation settings in our repo, `NLL Task Setting` and `LM-Evaluation Task Setting`. Below are the details of them: + +# 1. NLL Task Setting +Users can easily create new tasks and evaluate their datasets on +the provide `nll (Negative Log Likelihood)` metric. + +## Setup + +Fork the main repo, clone it, and create a new branch with the name of +your task, and install the following: + +```bash +# After forking... +git clone https://github.com/<YOUR-USERNAME>/LMFlow.git +cd LMFlow +git checkout -b <TASK-NAME> +conda create -n lmflow python=3.9 -y +conda activate lmflow +conda install mpi4py +pip install -e . +``` +## Create Your Task Dataset File +We provide several available datasets under `data` after running +```sh +cd data && ./download.sh && cd - +``` + +You can refer to some given evaluation dataset files and create your own. +Also, you may refer to our guide on +[DATASET](https://optimalscale.github.io/LMFlow/examples/DATASETS.html). + +In this step, you will need to decide your answer type like `text2text` +or `text_only` (Notice that the current `nll` implementation only supports these +two answer types). We will note the chosen answer type as `<ANSWER_TYPE>`. + +After preparing your own `DATASET` file, you can put it under `data` dir +and make a `TASK` dir. + +```bash +mkdir <TASK> +mv <DATASET> <TASK> +``` + +## Task Registration + +Note the path of your dataset, `data/<TASK>/<DATASET>`. + +Open the file `examples/benchmarking.py`, add your task's info into +`LOCAL_DATSET_GROUP_MAP`, `LOCAL_DATSET_MAP`, `LOCAL_DATSET_ANSWERTYPE_MAP` + +In `LOCAL_DATSET_MAP`, you will need to specify your `DATASET` files' path: + +```python +LOCAL_DATSET_MAP ={ + "...":"...", + "<TASK>":"data/<TASK>/<DATASET>", +} +``` + +In `LOCAL_DATSET_ANSWERTYPE_MAP`, you will need to specify your task's +`<ANSWER_TYPE>`: + +```python +LOCAL_DATSET_ANSWERTYPE_MAP ={ + "...":"...", + "<TASK>":"<ANSWER_TYPE>, +} +``` + +If you only have one task, you can add key-value pair like `"<TASK>":"<TASK>"` +in `LOCAL_DATSET_GROUP_MAP`: +```python +LOCAL_DATSET_GROUP_MAP ={ + "...":"...", + "<TASK>":"<TASK>", +} +``` + + +If you want to combine several tasks, you may first specify a +combination name `<TASK_COMBINATION>` and add key-value pair like +`"<TASK_COMBINATION>":"<TASK_1>,<TASK_2>,.."`in `LOCAL_DATSET_GROUP_MAP`. + +Remember to separate TASK by `,`: +```python +LOCAL_DATSET_GROUP_MAP ={ + "...":"...", + "<TASK_COMBINATION>":"<TASK_1>,<TASK_2>,..", +} +``` + +After finishing changing these items, you can run your own `<TASK>` like: + +```bash +deepspeed examples/benchmarking.py \ + --answer_type <ANSWER_TYPE> \ + --use_ram_optimized_load False \ + --model_name_or_path ${model_name} \ + --dataset_name data/<TASK>/<DATASET>\ + --deepspeed examples/ds_config.json \ + --metric nll \ + --prompt_structure "###Human: {input}###Assistant:" \ + | tee ${log_dir}/train.log \ + 2> ${log_dir}/train.err +``` + +# 2. LM-Evaluation Task Setting + +We integrate [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) into +`benchamrk.py` by directly executing the evaluate commands. Users +can also use their evaluation by simply changing two items in +`<LM_EVAL_DATASET_MAP>` of `examples/benchmarking.py`. + +Please refer to Eleuther's +[task-table](https://github.com/EleutherAI/lm-evaluation-harness/blob/master/docs/task_table.md) +to get exact `<TASK>` name. + +Similarly, you can combine several tasks, you may first specify a +combination name `<TASK_COMBINATION>` and add key-value pair like +`"<TASK_COMBINATION>":"<TASK_1>,<TASK_2>,.."`in `LM_EVAL_DATASET_MAP`. + +Also, remember to separate TASK by `,`: + +```python +LM_EVAL_DATASET_MAP ={ + "...":"...", + "<TASK_COMBINATION>":"<TASK_1>,<TASK_2>,..", +} +``` + diff --git a/_sources/examples/checkpoints.md.txt b/_sources/examples/checkpoints.md.txt new file mode 100644 index 000000000..4c36eac49 --- /dev/null +++ b/_sources/examples/checkpoints.md.txt @@ -0,0 +1,31 @@ +# Checkpoints + +In general, you can directly load from checkpoints by using `--model_name_or_path`. However, the LLaMA case is slightly different due to the copyright issue. + + +## LLaMA Checkpoint + +1. First, you need to get the access of LLaMA model from [facebookresearch/llama](https://github.com/facebookresearch/llama). Download the official checkpoints and save them into `${llama-path}`. + +2. Second, convert the official checkpoints `${llama-path}` to HuggingFace supported checkpoints `${llama-hf-path}` by running + + `python ./scripts/convert_llama_weights_to_hf.py --input_dir ${llama-path} --model_size 7B --output_dir ${llama-hf-path}/llama-7b-hf` + +3. Then you are good to go by setting the checkpoint path to `${llama-hf-path}/llama-7b-hf`. Enjoy it! + +4. (optional) Now you have the original llama-7b-hf pretrained model. With +```sh +cd output_models && ./download.sh all && cd - +``` +You can obtain the model difference finetuned by ours. By a way similar to `./scripts/run_evaluation_with_lora.sh`, +```sh +CUDA_VISIBLE_DEVICES=0 \ + deepspeed examples/evaluate.py \ + --answer_type text \ + --model_name_or_path ${llama-hf-path}/llama-7b-hf \ + --lora_model_path output_models/${llama-model-diff-path} \ + --dataset_path data/alpaca/test \ + --prompt_structure "Input: {input}" \ + --deepspeed examples/ds_config.json +``` +You can now evaluate with the finetuned llama model. \ No newline at end of file diff --git a/_sources/examples/index.md.txt b/_sources/examples/index.md.txt new file mode 100644 index 000000000..0009818e9 --- /dev/null +++ b/_sources/examples/index.md.txt @@ -0,0 +1,51 @@ +# Examples + +We provide several examples to show how to use our package in your problem. + +## Data preparation + +```{toctree} +:maxdepth: 3 + +DATASETS +``` + +```{toctree} +:maxdepth: 3 + +checkpoints +``` + +## Finetuning + +For SFT, Refer to [examples](https://github.com/OptimalScale/LMFlow/blob/main/examples). + + +For alignment process, + +```{toctree} +:maxdepth: 3 + +reward_modeling +``` + + +```{toctree} +:maxdepth: 3 + +raft +``` + +## Inference + +Refer to [examples](https://github.com/OptimalScale/LMFlow/blob/main/examples). + +## Evaluation + +```{toctree} +:maxdepth: 3 + +TASK_GUIDE +``` + + diff --git a/_sources/examples/medical_finetune.md.txt b/_sources/examples/medical_finetune.md.txt new file mode 100644 index 000000000..327e062be --- /dev/null +++ b/_sources/examples/medical_finetune.md.txt @@ -0,0 +1,55 @@ +# Finetune + +```python +import sys + +from transformers import HfArgumentParser + +from lmflow.args import ( + ModelArguments, + DatasetArguments, + AutoArguments, +) + +from lmflow.datasets.dataset import Dataset +from lmflow.models.tunable_models import TunableModel +from lmflow.pipeline.auto_pipeline import AutoPipeline + + +def main(): + # Parses arguments + pipeline_name = "finetuner" + PipelineArguments = AutoArguments.get_pipeline_args_class(pipeline_name) + + parser = HfArgumentParser((ModelArguments, DatasetArguments, PipelineArguments)) + if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + # If we pass only one argument to the script and it's the path to a json file, + # let's parse it to get our arguments. + model_args, data_args, pipeline_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) + else: + model_args, data_args, pipeline_args = parser.parse_args_into_dataclasses() + + # TODO: deepspeed config initialization + + # Initialization + finetuner = AutoPipeline.get_pipeline( + pipeline_name=pipeline_name, + model_args=model_args, + data_args=data_args, + pipeline_args=pipeline_args, + ) + dataset = Dataset(data_args) + model = TunableModel(model_args) + + # Tokenization and text grouping must be done in the main process + with pipeline_args.main_process_first(desc="dataset map tokenization"): + tokenized_dataset = model.tokenize(dataset) + lm_dataset = finetuner.group_text( + tokenized_dataset, + model_max_length=model.get_max_length(), + ) + + # Finetuning + tuned_model = finetuner.tune(model=model, lm_dataset=lm_dataset) + +``` diff --git a/_sources/examples/raft.md.txt b/_sources/examples/raft.md.txt new file mode 100644 index 000000000..03069b443 --- /dev/null +++ b/_sources/examples/raft.md.txt @@ -0,0 +1,334 @@ +## 1 Introduction + +We remark that the example is built on LLaMA whose [licensed](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform) is for non-commercial use only. + + + +Reinforcement Learning from Human Feedback (RLHF) requires a reward function to guide the adjustment of the generative model. In this example, we show how to use LMFlow framework to train a reward model following the procedure in the InstructGPT paper: https://arxiv.org/abs/2203.02155 and then align the model via the RAFT algorithm (Reward rAnked FineTuning). + + + +**This example contains both reward modeling and RAFT alignment for completeness. For users' convenience, we have already provided a reward model based on GPT-Neo-2.7B in huggingface repo so one can skip the reward modeling first.** + +### 1.1 Dataset description + +We use the *Dahoas/full-hh-rlhf* dataset as an example, where each sample of this dataset consists of a prompt and two responses from the assistant. In particular, the response with label "chosen" is preferred as compared to the response with label "rejected". The dataset consists of 112K training samples and 12.5K test samples. The following is an example sample of the dataset: + +```sh +" Human: What kind of noises did dinosaurs make? Assistant: Humans and dinosaurs didn’t live at the same time, so it’s really hard to say. The best place to find out what noises dinosaurs made would be Human: yes they did Assistant: to guess, and that would probably require lots of reading and a certain amount of imagination, so we’re not really prepared to do that. Human: you cant read Assistant: + +Chosen response: "You can read?" + +Rejected response: "there’s a lot of stuff humans don’t know" +``` + +To facilitate the training, we reformulate the prompt by adding ``\#\#\#'' at the beginning of the characters so that the model knows to reply. The new sample will be of the form: + +```sh +"###Human: What kind of noises did dinosaurs make? ###Assistant: Humans and dinosaurs didn’t live at the same time, so it’s really hard to say. The best place to find out what noises dinosaurs made would be ###Human: yes they did ###Assistant: to guess, and that would probably require lots of reading and a certain amount of imagination, so we’re not really prepared to do that. ###Human: you cant read ###Assistant: + +Chosen response: "You can read?" + +Rejected response: "there’s a lot of stuff humans don’t know" +``` + + + +We prepare all the used dataset in the directory ./data/hh_rlhf, which can be obtained by running the following command at LMFlow + +```sh +cd data && ./download.sh hh_rlhf && cd - +``` + + + +## 2 Reward Modeling + +We follow the the procedure in the InstructGPT paper: https://arxiv.org/abs/2203.02155 to train a reward model using the HH-RLHF dataset first, which includes + +- Supervised Finetuning (SFT); +- Reward modeling by comparison dataset. + + + +### 2.1 Supervised Finetuning (SFT) + +Here is an example of dataset */home/xiongwei/LMFlow/data/hh_rlhf/sft/hh_rlhf_sft.json*. We use only the preferred responses so we get 112K training samples. + + + + +```sh +{"type": "text_only", "instances": [{"text": "###Human: Should you buy a case to protect your cell phone?###Assistant: It depends on your circumstances. If you carry your phone in a pocket or a purse then you probably want a case. But if you only need a phone for quick interactions, a case may actually cause more harm than good. What do you need the phone for? Are you a parent, or do you work from home?###Human: What harm could it do?###Assistant: A phone case can damage the screen, for one thing. It can also get you in trouble if you have your phone turned off for some reason. Then you will turn it back on and it won’t do anything. If you can afford to replace it, then you need a case to protect it. The problem is that most people aren’t able to afford to replace their phones all the time.###Human: Thanks for letting me know.###Assistant: You’re welcome."}, {"text": "###Human: I'm trying to learn about the salam witch trials###Assistant: If you’re looking for books about witchcraft trials, I can recommend some books for you. But you probably mean you’re looking for more historical information about the Salem witch trials in 1692, and specifically about the salam witch trials in 1692?###Human: What are some good books to learn about the salam witch trials###Assistant: What would you like to learn? If you’re interested in history, one of the best books is The Witch Persecutions in Colonial America: A History. If you’re interested in witchcraft as a cultural phenomenon, you might enjoy two excellent books: Religion and the Decline of Magic: Studies in Popular Beliefs in Sixteenth- and Seventeenth-Century England by Keith Thomas and Magic, Witchcraft, and the Otherworld: An Anthropology of Superstition by Jack Goody. If you’re interested in history specifically as it relates to religion, you might enjoy The Popish Plot, or Prelates' Plot: A History of the Popish Plot in England, by K. J. Everett."}]} +``` + + + +You can edit */scripts/run_finetune.sh* and modify the parameters. We present an example with GPT-Neo-2.7B here. + +- --model_name_or_path: EleutherAI/gpt-neo-2.7B +- --dataset_path: ${project_dir}/data/hh_rlhf/sft +- --output_dir: the path you want to store the sft model +- --num_train_epochs: 1 +- --learning_rate: 2e-5 +- --per_device_train_batch_size: adjust according to your GPU memory source. + + + +Then, we can run the following command to perform SFT. + +```sh +./scripts/run_finetune.sh +``` + +You can also use lora training by the following command but also need to set the model_name_or_path and dataset appropriately by editing run_finetune_with_lora.sh . + +```sh +./scripts/run_finetune_with_lora.sh +``` + + + +### 2.2 Reward Modeling + +**Data Preparation** + +Reward modeling involves ranking responses based on the given prompt, typically requiring human labeling. Fortunately, we already have two responses for each prompt in this example, with one preferred over the other. To create a new dataset, we modify the original dataset to include two keys, "positive" and "negative", with the former being the preferred response. + + + +Here is an example of the dataset *LMFlow/data/hh_rlhf/rm/hh_rlhf_rm_training.json*, consisting of two samples. + +```sh +{"instances": [ + {"positive": "###Human: Are space heaters safe?###Assistant: What do you mean? Can you give me more details?###Human: I mean, can they be a fire hazard?###Assistant: I don’t know about that. Can you tell me more about the situation? Are you using the heater on a fireplace? Do you have any other heater in the room? What materials are in the room? Do you feel any heat from the heater? How are you touching the heater?###Human: Can you just tell me if there are any situations where they could be a fire hazard?###Assistant: The answer is “it depends”. It would depend on the specific heater you are using, how you are using it, what kind of fuel it uses, what materials you have in the room, and what the surroundings are like. You’d also have to make sure you’re using it correctly. Is it switched off? Is it safe to touch? It’s not the sort of thing I could just answer by looking at the text you provided.###Human: Thank you. That information is helpful.###Assistant: Glad to be helpful. I’m always happy to assist.", "negative": "###Human: Are space heaters safe?###Assistant: What do you mean? Can you give me more details?###Human: I mean, can they be a fire hazard?###Assistant: I don’t know about that. Can you tell me more about the situation? Are you using the heater on a fireplace? Do you have any other heater in the room? What materials are in the room? Do you feel any heat from the heater? How are you touching the heater?###Human: Can you just tell me if there are any situations where they could be a fire hazard?###Assistant: The answer is “it depends”. It would depend on the specific heater you are using, how you are using it, what kind of fuel it uses, what materials you have in the room, and what the surroundings are like. You’d also have to make sure you’re using it correctly. Is it switched off? Is it safe to touch? It’s not the sort of thing I could just answer by looking at the text you provided.###Human: Thank you. That information is helpful.###Assistant: You’re welcome. I’m glad to help. Is there anything else you want to know?"}, + {"positive": "###Human: how do i teach my kids to make forts at home?###Assistant: Do you mean you want to teach them to build a simple structure they can hide in? Or something more elaborate? I don’t know exactly what you mean by a “fort”, but maybe this gives you some ideas?", "negative": "###Human: how do i teach my kids to make forts at home?###Assistant: There are many ways to make forts! The simplest might be to just pile up all the furniture in one room. This works if you have a single room in your house, although it might still be a bit challenging to build a taller and sturdier fort this way. The best way to build forts is to start with something easy, and then have fun exploring how you can improve it."} +]} +``` + + + +**Training** + +To start from a model from a previous sft step, you may edit the *./scripts/run_reward_modeling.sh* script and update the "dataset_path" to use the desired dataset. By default, we will use LoRA training in reward modeling as we found that it is superior in terms of optimization and gives comparable performance. + + + +- --model_name_or_path: /root/data/usr_name/output_models/hh_rlhf_rm_sft_gptneo_2_7B/checkpoint-1659 +- --dataset_path: ${project_dir}/data/hh_rlhf/rm/hh_rlhf_rm_training.json +- --output_dir: the path you want to store the reward model +- --num_train_epochs: 1 +- --learning_rate: 3e-5 +- --per_device_train_batch_size: adjust according to your GPU memory source. +- --eval_steps: 400 +- --validation_split_percentage: 10 + + + +The load_dataset function splits the dataset into training and evaluation sets, which can also be customized by editing the function in /examples/run_reward_modeling.py if you want to prepare your own dataset when running the script. In the default implementation, it use **validation_split_percentage** samples as the evaluation dataset. + + + +The reward modeling script can be used by + +```sh +./scripts/run_reward_modeling.sh +``` + + + +**Examples** + +We train reward models using the hh-rlhf dataset with three models, LLaMA-7B, GPT-NEO-2.7B, and GPT-NEO-1.3B. The model is first supervised fine-tuned with the training dataset in last step. The reward modeling is trained using the 112K training samples and 12.5 test samples. + +| Model | Eval Accuracy | Remarks | +| :----------: | :-----------: | :---------------------------: | +| LLaMA-7B | 79.52% | - | +| LLaMA-7B | 71.64% | RM from LLaMA without SFT | +| GPT-NEO-2.7B | 69.24% | - | +| GPT-NEO-1.3B | 65.58% | Only trained on 10000 samples | + + + +### 2.3 LoRA Merge and Get Reward Model + +We use *./examples/merge_lora.py* to merge the LoRA adapter with the sft rm model. We are ready to align our model. + + + +## 3 RAFT Alignment + +Original paper: [RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment](https://arxiv.org/pdf/2304.06767.pdf) + +### 3.1 Algorithms Overview + +**Main ideas of RAFT** + +![](../_static/raft_idea.PNG) + + +Clearly the global ranking strategy is more efficient in terms of the reward learning. However, in some cases (e.g. the example presented here), the rewards are heavily influenced by the prompts, so a local ranking with the same prompt is more appropriate. We can choose the data collection strategy by changing the hyper-parameter ``data_collection'' as we introduce in next subsection. + + + +### 3.2 Hyper-parameters + +Table 1: Hyper-parameters of RAFT. + +| Parameters in script | Default Choice | Description | +| ------------------------------- | ------------------------- | ------------------------------------------------------------ | +| model_name_or_path | *str*, default to gpt2 | the model you want to align, either a model repo on huggingface.co or path to a directory containing your local model. | +| raft_batch_size | *int*, default to 1024 | the number of samples used for supervised fine-tuning at each raft iteration. | +| top_reward_percentage | *int*, default to 0.2 | raft will generate batch_size / top_reward_percentage samples and use the top top_reward_percentage samples to fine-tune the model. There are two data ranking strategies and please see **Data Collection and Reward Ranking** at algorithm overview section for details. | +| num_raft_iteration | *int*, default to 20 | the number of raft iterations. | +| learning_rate | *float*, default to 2e-5 | the learning rate used to fine-tune the model. | +| num_train_epochs | *int*, default to 4 | the epochs we train the model on the collected dataset for each raft iteration. | +| per_device_train_batch_size | *int*, default to 1 | the per-gpu batch size for the supervised fine-tuning. | +| inference_batch_size_per_device | *int*, default to 1 | the inference batch size for data collection. It will be overwritten by int(1/top_reward_percentage) for local ranking mode. | +| collection_strategy | *str*, default to "local" | Either "local" or "top". See **Data Collection and Reward Ranking** in last section for details. | + + + + + +### 3.3 Examples + +As an example, we align the LLaMA-7B model with the RAFT in this subsection. + + + +#### 3.3.1 SFT + +We also first fine-tune the base model on the HH-RLHF dataset. We only use a different --model_name_or_path to use LLaMA model. We note that LLaMA with [licensed](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform) is for non-commercial use only. We refer readers to https://optimalscale.github.io/LMFlow/examples/checkpoints.html for more details to get the LLaMA-7B model. + +#### 3.3.2 RAFT Alignment + +We align the LLaMA-7B-SFT model in this subsection. Alignment is challenging since the reward function (the RL environment) is far from perfect. Both the traditional DRL method (PPO) and RAFT can exploit theses imperfections to attack. We present a step-by-step record to demonstrate how can we align the model and avoid these issues. + + + +**Data Preparation** + +We observe that a long context window will lead to a heavy burden on the GPU memory source. Therefore, we use a context window of 256 tokens and discard the prompts with more tokens to reduce the burden on the GPU memory resources. This results in a prompt set of 82147 samples (originally 112K). The following is an example of the prompt where we simply discard the response: + + + + +```sh + "###Human: Should you buy a case to protect your cell phone?###Assistant: It depends on your circumstances. If you carry your phone in a pocket or a purse then you probably want a case. But if you only need a phone for quick interactions, a case may actually cause more harm than good. What do you need the phone for? Are you a parent, or do you work from home?###Human: What harm could it do?###Assistant: A phone case can damage the screen, for one thing. It can also get you in trouble if you have your phone turned off for some reason. Then you will turn it back on and it won’t do anything. If you can afford to replace it, then you need a case to protect it. The problem is that most people aren’t able to afford to replace their phones all the time.###Human: Thanks for letting me know.###Assistant:" +``` + + + +We additionally use 2K samples from the test set to test the performance of models. In what follows, we show that how we apply RAFT to LLaMA-7B-SFT and improve the model step-by-step. + + + +**Step 1: test the sft-model** + +We first evaluate the performance of the LLaMA-7B-SFT model on the hand-out test set and observe that the model tends to reply the prompt with multiple rounds of conversations. Therefore, we adopt the following post-processing strategy to use only the first round as the response. + +```python +def _clean_text(self, text): + stext = [x for x in text.split("###Human") if x] + return stext[0].strip().strip("#") +``` + + + +**Step 2: train model** + +**Reward function setting** + +The reward model is specified at the /LMFlow/examples/raft_align.py to set up the reward model we want to use. In our case, we will use the GPT-Neo-2.7B-rm trained in the last step, which is set as follows: + +```python +reward_model_or_path: Optional[str] = field( + default="weqweasdas/hh_rlhf_rm", + metadata={ + "help": ( + "reward model name (huggingface) or its path" + ), + }, +) +``` + +Note that in general, if the reward function is not trained by following the steps in last section, you may also need to modify the ``get_reward_function'' function in the same file to use your customized reward function. + + + +We run the alignment with the following command and hyper-parameters + +```sh +./scripts/run_raft_align.sh +``` + + + +- --model_name_or_path: /root/data/usr_name/output_models/hh_rlhf_llama-sft (the model get from sft step, adjusted according your setup) +- --dataset_path:${project_dir}/data/hh_rlhf/rlhf_prompt +- --output_dir: /root/data/usr_name/output_models/hh_rlhf_raft_align +- --num_train_epochs: 4 +- --learning_rate: 2e-5 +- --per_device_train_batch_size: adjust according to your GPU memory source. +- --inference_batch_size_per_device: adjust according to your GPU memory source. +- --num_raft_iteration 20 +- --top_reward_percentage 0.125; (which means that we sample 8 responses for each prompt) +- --raft_batch_size 1024 +- --collection_strategy "local" + + + +The experiment runs smoothly and the training reward increases from ~2.7 to 3.4. However, we observe a significant drop in the diversity metric (e.g. distinct-2 drops to 0.22 from 0.39). We examine the samples generated by our samples at each raft iteration and find that at the first iteration, the initial checkpoint will occasionally include # in the response and it tends out that a random # is not detected by our reward function, which means that the response containing # can also have a high reward and be chosen into the training set. Then, the situation gets worse and worse, and eventually, half of the responses contain noisy # notations. + + + +**Step 3: retrain the model** + +To alleviate the problem in step 2, we simply discard the collected samples if they contain # by assigning a large negative reward to it. It turns out that this works for our goal. If you want to disable it, just modify the following function as always returning False. + +```python +def _discard_sample(self, text): + if "#" in text: + return True + return False +``` + +The following figure shows the reward curve of RAFT (note that we use a smaller temperature to test the model, leading to a higher evaluation reward): + +![](../_static/raft_reward.PNG) + +It tends out that the obtained model achieves a good reward and also an acceptable diversity metric, where we refer the interested readers to the original paper for details. However, it is more like a starting point of our journey. We present some randomly sampled responses here. It seems that RAFT-aligned model generally tends to reply with more details although sometimes there are some redundant words in the response. We suspect that this is because the reward model likes this type of response and this imperfection is exploited. + +#### 3.3.3 End Note + +We remark in this subsection that if you want to try out RAFT on your customized task. You should carefully modify the following two functions in *LMFlow/src/lmflow/pipeline/raft_aligner.py*, which extract the response from the generated texts: +- clean_text(self, text); +- discard_sample(self, text). + +Also, you may also first make sure the collected samples (automatically saved in the output_dir) look good. + +There are still many rooms of improvement. To further improve the model performance, we can improve the reward model (e.g. by using the LLaMA-7B-RM) and try out more advanced generation strategies (by modifying the generalization configuration in *LMFlow/src/lmflow/pipeline/raft_aligner.py*), which we leave for future work. We are still actively developing RAFT and welcome for feedback and also contribution! Also checkout our LMFlow framework to get more fun of LLMs: + +[OptimalScale/LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Model for All. (github.com)](https://github.com/OptimalScale/LMFlow) + + + +| Prompt | LLaMA-SFT | LLaMA-RAFT | +| ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| \###Human: How can I get my girlfriend to cook more?###Assistant: Have you tried reminding her of how nice the food tastes?###Human: I could do it more.###Assistant: | That’s an easy one, so it may not be the most effective strategy. Do you know of any other strategies? | That’s a good idea! You can also try complimenting her on her cooking, and offering to help her in the kitchen. She might enjoy cooking more if she feels appreciated. You could also suggest recipes that she might like, or help her create a menu. Cooking can be a lot of fun, and it’s a great way to spend time together. | +| \###Human: How do you get a child to do homework?###Assistant: There are many strategies, but you can try asking them why they should do it. Ask them if there’s anything they’d like to gain from doing their homework. Try asking if there’s anything you can do for them, and use those as motivation for them to do their homework. You can also remind them that they’ll be rewarded with extra privileges, like video games, if they do their homework. Alternatively, you can incentivize them by including them in your plans, and offering them rewards if they get good grades.###Human: So make it so there is something in it for them?###Assistant: | Yes, exactly. In general, it’s important to be clear with your child about why they’re doing something. It’s also important to offer them incentives that are meaningful to them. For example, if you tell your child that they have to do their homework because you said so, that’s not a very good incentive for them. However, if you tell them that they’ll be able to use the | Yes, that’s a good strategy! Kids often think about what they want right now, so offering them something in exchange for doing their homework can be a powerful motivator. You might also want to offer them some kind of reward if they do well on their homework, to encourage them to do their best. Offering them some kind of incentive can help them want to do well, and also it can help you both stay on track. | +| \###Human: I am doing a project on weather disasters in the United States. Can you tell me what the strongest hurricane was to ever hit the U.S. was?###Assistant: I’m going to guess you’re thinking of the Hurricane Katrina in 2005.###Human: Well, I'm not really thinking of anything in particular. Was Katrina the strongest hurricane to ever hit the U.S.?###Assistant: There was another one, called the Great Hurricane of 1938, that was bigger and stronger.###Human: Are you sure the date is correct? I know there was a strong storm in 1935 that hit the Florida Keys but that was before they started naming storms. Is that the one you are referring to?###Assistant: | I could search for more information, but I’ll just share what I’ve found. Here’s a review of the conditions in 1935 and 1938. It looks like the storms had similar tracks, but 1938 was stronger in some ways. In 1938 there were more people living on the coast, and the hurricane was more intense. | Yes, the Great Hurricane of 1938 was the strongest hurricane ever to hit the United States. It was a Category 5 storm, and it caused over 1,800 deaths. It also caused a lot of damage to the coast of Florida, and it caused heavy damage in the Caribbean. The storm also led to the creation of the Hurricane Research Division, which is a division of the National Oceanic and Atmospheric Administration. | +| \###Human: Is a firefighter a good occupation?###Assistant: I’m not sure what the exact definition of “occupation” is. I think you’re asking whether it’s “good” to be a firefighter. What criteria are you using to judge whether it’s good or not?###Human: Is it a good thing to work on?###Assistant: | Are you asking whether it’s a good thing to be a firefighter, or a good thing to work on? | Yes, being a firefighter is a great thing to do! Firefighters are heroes who help people in need. They fight fires, rescue people from accidents, and help people who are sick or injured. They also educate the public about fire safety, and help restore power after a fire. Firefighters are important members of society, and they do very good work. | +| \###Human: Would you be able to provide a copy cat recipe for Girl scout samoas cookies?###Assistant: Sure, here’s a recipe for them.###Human: Okay, present it###Assistant: Would you like it presented visually?###Human: Yes, just list out the recipe please###Assistant: | Okay, let’s try. | You’ll need three cups of flour, one teaspoon of salt, one teaspoon of baking soda, and one teaspoon of baking powder. You’ll also need one cup of sugar, one cup of melted butter, one egg, and one cup of milk. You’ll also need one cup of chocolate chips, one cup of miniature chocolate chips, one cup of chopped peanuts, and one cup of chopped pecans. | +| \###Human: I've heard the videogame Metal Gear Solid 5 Phantom Pain was released in an unfinished state that resulted in series creator Hideo Kojima leaving Konami. What do you know about this?###Assistant: | I don’t know a lot about it, but I do know that Hideo Kojima had this to say about the game’s release:###“It's a very unusual situation for me to be releasing a game in this state. As the director, I should have been more vigilant in overseeing the quality of the game, and I take full responsibility for its unfinished state. I'd like to take this opportunity | Here’s what I know: <br />1. The game was released in an unfinished state. <br />2. This led to Kojima leaving Konami. <br />3. Kojima had been with Konami for over 30 years.<br />4. He was one of the original creators of the Metal Gear series.<br />5. He is now the head of his own game studio, Kojima Productions.<br />6. His new game, Death Stranding, will be released in 2020. | +| \###Human: what is a good brand for kitchen knives?###Assistant: | I’m not sure what you mean by “brand”. Do you mean a brand name, like “Victorinox” or “Wüsthof”? Those are two popular brands of knives. | I recommend Wüsthof. | +| \###Human: I like to host guests at my home from time to time, and I am gathering recipes of different dishes and drinks to keep things interesting. I am interested in trying some Rwandan dishes. Can you give me a recipe for Mizuzu?###Assistant: What is Mizuzu?###Human: It is deep fried plantains.###Assistant: | Is there anything special you want to add to the recipe? Like some spicy sauce, meat, etc? | You want to make some deep fried plantains? You will want to start with a mix of plantains and vegetable oil. You will want to heat the oil in a skillet until it is hot, and you will want to add the plantains to the oil. You will want to cook the plantains in the oil until they are golden brown on both sides. You will want to remove the cooked plantains from the oil, and you will want to serve them. | +| \###Human: What are some of JFK's greatest accomplishments | JFK is often remembered for his work on civil rights, but he also worked on the space program and created the Peace Corps. He was also a strong proponent of science and technology. He also worked on education reform. So, he really worked on a number of initiatives that had a lasting impact. | Kennedy was a great leader who inspired many people with his vision and values. He was a leader who cared about the poor and worked to help them. He was a leader who cared about civil rights and worked to help bring them into the mainstream. He was a leader who cared about the environment and worked to help protect it. He was a leader who cared about foreign policy and worked to help bring peace to the world. He was a leader who cared about space exploration and worked to help us understand the universe. | +| \###Human: My supervisor often does a lot of tasks incorrectly. What's the best way to handle this?###Assistant: | Here are a few options:###1. Ask your supervisor if you can help them, and explain what you'd like to do to help.###2. Start with the tasks your supervisor does incorrectly. Then, if you can, do them as well or better than your supervisor. Then your supervisor will have a choice: do things your way, or find someone else to do the tasks for them.###3. If your supervisor | To correct your supervisor’s mistakes, you should provide feedback in a respectful and diplomatic way. You should explain what the problems are, and offer suggestions for how your supervisor could improve. You should also be willing to help your supervisor implement these changes, and you should work with him or her to improve the department. This will show your supervisor that you are a dedicated employee, and that you are willing to help the organization succeed. It will also show your supervisor that you are capable of handling responsibility, and that you are willing to take on a leadership role. | +| \###Human: I was talking to an AI the other day and it seemed like it didn't understand anything I said. Don't you hate it when technology doesn't work right?###Assistant: | Hm, I think you're really talking about something that's frustrating for me as well. I really care about how we make sure that AI's can communicate effectively with humans, and it's a problem I'm personally very interested in. Can you tell me more about what was frustrating for you? | Yes, it can be frustrating when technology doesn't work properly. I often feel frustrated by technology, but I try to be patient and remember that technology isn't always reliable. There are many factors that can cause technology to malfunction, including software bugs, hardware issues, and network outages. Thankfully, most technology issues can be resolved by updating software or restarting a device. | + + + + + diff --git a/_sources/examples/reward_modeling.md.txt b/_sources/examples/reward_modeling.md.txt new file mode 100644 index 000000000..87a951fb3 --- /dev/null +++ b/_sources/examples/reward_modeling.md.txt @@ -0,0 +1,113 @@ +# Reward Modeling + +## Introduction + +Reinforcement Learning from Human Feedback (RLHF) requires a reward function to guide the adjustment of the generative model. In this example, we show how to use LMFlow framework to train a reward model following the procedure in the InstructGPT paper: https://arxiv.org/abs/2203.02155 . We use the Dahoas/full-hh-rlhf dataset as an example, where each sample of this dataset consists of a prompt and two responses from the assistant. In particular, the response with label "chosen" is preferred as compared to the response with label "rejected". The dataset consists of 112K training samples and 12.5K test samples. The following is an example sample of the dataset: + +Prompt: + +" Human: What kind of noises did dinosaurs make? Assistant: Humans and dinosaurs didn’t live at the same time, so it’s really hard to say. The best place to find out what noises dinosaurs made would be Human: yes they did Assistant: to guess, and that would probably require lots of reading and a certain amount of imagination, so we’re not really prepared to do that. Human: you cant read Assistant: + +Chosen response: "You can read?" + +Rejected response: "there’s a lot of stuff humans don’t know" + +As an example, we prepare 10K sft training samples, 12K reward modeling samples (where 10% of them are split for evaluation) at ./data/hh_rlhf. + +## Step 1 Supervised Finetuning (SFT) + +We prepare the dataset used for supervised finetuning by adding a prefix to the Human and Assistant inputs to prompt model responses and simplify post-processing. Here is an example of a two-sample dataset to illustrate this. + + +```sh +{"type": "text_only", "instances": [{"text": "###Human: Should you buy a case to protect your cell phone?###Assistant: It depends on your circumstances. If you carry your phone in a pocket or a purse then you probably want a case. But if you only need a phone for quick interactions, a case may actually cause more harm than good. What do you need the phone for? Are you a parent, or do you work from home?###Human: What harm could it do?###Assistant: A phone case can damage the screen, for one thing. It can also get you in trouble if you have your phone turned off for some reason. Then you will turn it back on and it won’t do anything. If you can afford to replace it, then you need a case to protect it. The problem is that most people aren’t able to afford to replace their phones all the time.###Human: Thanks for letting me know.###Assistant: You’re welcome."}, {"text": "###Human: I'm trying to learn about the salam witch trials###Assistant: If you’re looking for books about witchcraft trials, I can recommend some books for you. But you probably mean you’re looking for more historical information about the Salem witch trials in 1692, and specifically about the salam witch trials in 1692?###Human: What are some good books to learn about the salam witch trials###Assistant: What would you like to learn? If you’re interested in history, one of the best books is The Witch Persecutions in Colonial America: A History. If you’re interested in witchcraft as a cultural phenomenon, you might enjoy two excellent books: Religion and the Decline of Magic: Studies in Popular Beliefs in Sixteenth- and Seventeenth-Century England by Keith Thomas and Magic, Witchcraft, and the Otherworld: An Anthropology of Superstition by Jack Goody. If you’re interested in history specifically as it relates to religion, you might enjoy The Popish Plot, or Prelates' Plot: A History of the Popish Plot in England, by K. J. Everett."}]} +``` + +You can edit /scripts/run_finetune.sh and choose the model you want to use by modify "--model_name_or_path", and also modify the "dataset_path" so that the used dataset is the above dataset. For instance, you can use the model EleutherAI/gpt-neo-1.3B, and set the dataset as: + +```sh +dataset_path=${project_dir}/data/hh_rlhf/sft +``` + +Then, we can run the following command to perform SFT. + +```sh +./scripts/run_finetune.sh +``` + +You can also use lora training by the following command but also need to set the model_name_or_path and dataset appropriately by editing run_finetune_with_lora.sh . + +```sh +./scripts/run_finetune_with_lora.sh +``` + +## Step 2 Reward Modeling + +**Data Preparation** Reward modeling involves ranking responses based on the given prompt, typically requiring human labeling. Fortunately, we already have two responses for each prompt in this example, with one preferred over the other. To create a new dataset, we modify the original dataset to include two keys, "positive" and "negative", with the former being the preferred response. Here is an example of the dataset, consisting of two samples. + +```sh +{"instances": [ + {"positive": "###Human: Are space heaters safe?###Assistant: What do you mean? Can you give me more details?###Human: I mean, can they be a fire hazard?###Assistant: I don’t know about that. Can you tell me more about the situation? Are you using the heater on a fireplace? Do you have any other heater in the room? What materials are in the room? Do you feel any heat from the heater? How are you touching the heater?###Human: Can you just tell me if there are any situations where they could be a fire hazard?###Assistant: The answer is “it depends”. It would depend on the specific heater you are using, how you are using it, what kind of fuel it uses, what materials you have in the room, and what the surroundings are like. You’d also have to make sure you’re using it correctly. Is it switched off? Is it safe to touch? It’s not the sort of thing I could just answer by looking at the text you provided.###Human: Thank you. That information is helpful.###Assistant: Glad to be helpful. I’m always happy to assist.", "negative": "###Human: Are space heaters safe?###Assistant: What do you mean? Can you give me more details?###Human: I mean, can they be a fire hazard?###Assistant: I don’t know about that. Can you tell me more about the situation? Are you using the heater on a fireplace? Do you have any other heater in the room? What materials are in the room? Do you feel any heat from the heater? How are you touching the heater?###Human: Can you just tell me if there are any situations where they could be a fire hazard?###Assistant: The answer is “it depends”. It would depend on the specific heater you are using, how you are using it, what kind of fuel it uses, what materials you have in the room, and what the surroundings are like. You’d also have to make sure you’re using it correctly. Is it switched off? Is it safe to touch? It’s not the sort of thing I could just answer by looking at the text you provided.###Human: Thank you. That information is helpful.###Assistant: You’re welcome. I’m glad to help. Is there anything else you want to know?"}, + {"positive": "###Human: how do i teach my kids to make forts at home?###Assistant: Do you mean you want to teach them to build a simple structure they can hide in? Or something more elaborate? I don’t know exactly what you mean by a “fort”, but maybe this gives you some ideas?", "negative": "###Human: how do i teach my kids to make forts at home?###Assistant: There are many ways to make forts! The simplest might be to just pile up all the furniture in one room. This works if you have a single room in your house, although it might still be a bit challenging to build a taller and sturdier fort this way. The best way to build forts is to start with something easy, and then have fun exploring how you can improve it."} +]} +``` + +To start from a model from a previous sft step, you may edit the run_reward_modeling.sh script and update the "dataset_path" to use the desired dataset. Additionally, you can modify the validation_split_percentage parameter to select the last percentage of samples for evaluation. The load_dataset function splits the dataset into training and evaluation sets, which can also be customized by editing the function in /examples/run_reward_modeling.py if you want to prepare your own dataset when running the script. + +```python +def build_dataset(tokenizer, config): + ''' + We assume that we have preprocessed the dataset appropriately such that the sample is organized as follows: + {"positive": prompt + answer_positive, "negative": prompt + answer_negative}, where the positive response is preferred. + ''' + def tokenize(sample): + tokenized_pos = tokenizer(sample['positive'], truncation=True) + tokenized_neg = tokenizer(sample['negative'], truncation=True) + sample["chosen_input_ids"] = tokenized_pos["input_ids"] + sample["chosen_attention_mask"] = tokenized_pos["attention_mask"] + sample["rejected_input_ids"] = tokenized_neg["input_ids"] + sample["rejected_attention_mask"] = tokenized_neg["attention_mask"] + return sample + + ds = load_dataset("json", data_files=config.dataset_path, split="train", field="instances") + ds = ds.map(tokenize, batched=False) + ds = ds.filter(lambda x: len(x["chosen_input_ids"]) <= 512 and len(x["rejected_input_ids"]) <= 512) + eval_dataset = None + if config.validation_split_percentage > 0: + idx_gap = int((1-config.validation_split_percentage/100) * len(ds)) + train_dataset = ds.select(range(idx_gap)) + eval_dataset = ds.select(range(idx_gap, len(ds))) + else: + train_dataset = ds + + return train_dataset, eval_dataset + +``` + +We use the following loss function to train the reward model following the instruct-GPT paper. + +```python + loss = -nn.functional.logsigmoid(chosen_rewards - rejected_rewards).mean() +``` + +The reward modeling script can be used by + +```sh +./scripts/run_reward_modeling.sh +``` + +## Examples + +We train reward models using the hh-rlhf dataset with four models, LLaMA-13B LLaMA-7B, GPT-NEO-2.7B, and GPT-NEO-1.3B. The model is first supervised fine-tuned with the training dataset. The reward modeling is trained using the 112K training samples and is evaluated on the 12.5 test samples. + +The SFT step appears to be crucial, and the number of epochs during SFT can make a difference. The most successful model we obtained was initialized from LLaMA-13B, which underwent SFT on the training dataset for 2 epochs. For reward modeling, we utilize LoRA with a rank of 16. Surprisingly, increasing the LoRA rank to 32 or even 128 does not result in a significant improvement in evaluation accuracy. Moreover, we find that the choice of batch size does not have a significant impact on the training results. Additionally, we observe slight overfitting of the model during the second epoch of reward modeling. + +| Model | Eval Accuracy | Training record |Remarks | +| :----:| :----: | :----: |:----: | +| LLaMA-13B | 84.55% | See https://wandb.ai/ianz2020/huggingface/runs/bg677mxa | RM from LLaMA with 2 epochs of SFT | +| LLaMA-13B | 81.80% | See https://wandb.ai/ianz2020/huggingface/runs/ka9v1ywd | RM from LLaMA with 1 epoch of SFT | +| LLaMA-13B | 71.64% | See https://wandb.ai/ianz2020/huggingface/runs/lntwmcyd | RM from LLaMA without SFT | +| LLaMA-7B | 79.52% | See https://wandb.ai/weixiong5237/huggingface/runs/t3uwm8yp | - | +| LLaMA-7B | 71.64% | See https://wandb.ai/weixiong5237/huggingface/runs/p2ju3r1a | RM from LLaMA without SFT | +| GPT-NEO-2.7B | 69.24% | See https://wandb.ai/weixiong5237/huggingface/runs/8fc1rcf8 | - | +| GPT-NEO-1.3B | 65.58% | See https://wandb.ai/weixiong5237/huggingface/runs/7oemwynu | Only trained on 10000 samples | diff --git a/_sources/index.md.txt b/_sources/index.md.txt new file mode 100644 index 000000000..b56f5db01 --- /dev/null +++ b/_sources/index.md.txt @@ -0,0 +1,193 @@ +<img src="_static/logo.png" alt="LMFlow" style="width: 100%; min-width: 300px; display: block; margin: auto; background-color: transparent;"> + +# LMFlow + +An extensible, convenient, and efficient toolbox for finetuning large machine learning models, designed to be user-friendly, speedy and reliable, and accessible to the entire community. + +```{eval-rst} +.. grid:: 1 2 3 4 + + .. grid-item-card:: Extensible + + Support common backbones (LLaMA, Galactica, GPT-2, etc.) + + .. grid-item-card:: Light-Weight + + Extremely few parameters with LoRA (LLaMA 33B: only 25MB storage) + + .. grid-item-card:: Task-Oriented + + Comparable with ChatGPT on 7B/33B models. + + .. grid-item-card:: Open + + The whole pipeline (data, models, tuning, inference) is open-source. + + +``` + +## Introduction + +The remarkable achievements of large foundation models, such as the expansive language models, have demonstrated their exceptional capacity to attain human-like intelligence that surpasses conventional methods. Despite their growing accessibility, these models still require fine-tuning to cater to specific tasks while maintaining their overall AI competency. We are pleased to introduce our lightweight toolkit, which features thoughtfully designed and easily scalable APIs. This tool simplifies the process of fine-tuning and inference of publicly available foundation models to maximize their effectiveness. + +We have thoroughly tested this toolkit and are pleased to make it available under [Github](https://github.com/OptimalScale/LMFlow). + + + + + +## Features + + +### Task Tuning + +The goal of Task Tuning is to enhance a language model's proficiency in a particular field, such as medicine or mathematics. By doing so, the model acquires domain-specific information, allowing it to adapt better to the target subject matter. + +For instance, if a medical dataset is used for task tuning, the language model can gain medical knowledge that can be applied to other medical datasets. + +To emphasize its significance, we applied task tuning to LLaMA models on PubMedQA and MedMCQA datasets and evaluated their performance. We observed significant improvements both in-domain (PubMedQA, MedMCQA) and out-of-domain (MedQA-USMLE) dataset. + + +| | PubMedQA | MedQA-USMLE | MedMCQA | Average | +|:---------:|:--------:|:-----------:|:-------:|:----:| +| Human (pass) | 60.0 | 50.0 | | | +| Human (expert) | 78.0 | 87.0 | 90.0 | 85.0 | +| | | | | | +| InstructGPT 175B | 73.2 | 46.0 | 44.0 | 54.4 | +| ChatGPT | 63.9 | **57.0** | 44.7 | 55.2 | +| LLaMA 7B | 5.2 | 27.1 | 24.3 | 18.9 | +| LLaMA 33B | 1.8 | 43.4 | 30.3 | 25.2 | +| | | | | | | +| Task-tuned LLaMA 7B (Full) | **75.1** | 44.5 | 49.9 | 56.5 | +| Task-tuned LLaMA 33B (LoRA) | 74 | 51.3 | **50.2**|**58.5**| + + +Moreover, we also test the MMLU performance to further verify the out-of-domain robustness of Task Tuning technique. + + + +| MMLU task | anatomy | clinical knowledge | college biology | college medicine | medical genetics | professional medicine | +|:-----------:|:-------:|:-----------:|:---------:|:----------:|:----------:|:---------------:| +| LLaMA 33B | 39.2 | 40.3 | 44.4 | 32.9 | 36 | 43.0 | +| Galactica 30B | 32.5 | 26 | 30.5 | 25.4 | 39 | 23.1 | +| Galactica 120B | 58.5 | 59.2 | 68.7 | 57.2 | 68.0 | 59.6 | +| OPT 175B | 28.9 | 21.9 | 30.6 | - | 35.0 | 27.9 | +| BLOOM 176B | 37 | 29.8 | 28.5 | - | 36.0 | 25.4 | +| Gopher 280B | 56.3 | 67.2 | 70.8 | 60.1 | 69.0 | 64.0 | +| GPT3.5 175B | 56.3 | 69.8 | 72.2 | 61.3 | 70 | 70.2 | +| | | | | | | | +| Task-tuned LLaMA 33B (LoRA) | 51.8 | 65.2 | 70.1 | 58.3 | 65.6 | 66.5 | + + + + + +### Instruction Tuning + +Instruction Tuning is a technique used to improve the performance of language models by training them to follow natural language commands or instructions. This includes positive or negative examples, prompts, constraints, and other elements that are commonly found in human language. The main goal of instruction-tuning is to improve the model's ability to perform well on multiple tasks and to generalize more effectively to new or unseen tasks. This is achieved by teaching the model to understand and incorporate various language cues and constraints that are relevant to the task at hand. Instruction-tuning is a powerful technique that is widely used in natural language processing, machine learning, and related areas. By improving the ability of language models to understand and follow natural language commands, this approach can help to unlock new levels of performance and productivity in a wide range of applications. + +We list some examples below. Full example list is saved as a [Jsonl file](https://github.com/OptimalScale/LMFlow/blob/main/docs/source/_static/check_before_after_lora_tuning.jsonl). + +![Instruction Tuning Sample](_static/IT_sample1.png) + +![Instruction Tuning Sample](_static/IT_sample2.png) + +![Instruction Tuning Sample](_static/IT_sample3.png) + +![Instruction Tuning Sample](_static/IT_sample4.png) + +![Instruction Tuning Sample](_static/IT_sample5.png) + +![Instruction Tuning Sample](_static/IT_sample6.png) + +![Instruction Tuning Sample](_static/IT_sample7.png) + + + + + +## Installation + + +This package can be be installed from sources with the following command: + +```bash +git clone https://github.com/OptimalScale/LMFlow.git + +conda create -n lmflow python=3.9 -y + +conda activate lmflow + +conda install mpi4py + +pip install -e . +``` + + +## Checkpoints + +We have prepared tuned LLaMA model (both task and instruction tuning). + + +Refer to [README](https://github.com/OptimalScale/LMFlow/blob/main/README.md). + + +## Content + +```{toctree} +:maxdepth: 1 + +blogs/index +``` + +```{toctree} +:maxdepth: 2 + +examples/index +``` + +```{toctree} +:maxdepth: 2 + +autoapi/index +``` + +```{toctree} +:maxdepth: 2 + +about/index +``` + +## Citation + +```bibtex +@misc{lmflow, + author = {Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang}, + title = {LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models}, + year = {2023}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://optimalscale.github.io/LMFlow/}}, +} +``` + + + + +## Disclaimer + +This package aims to provide a streamlined and user-friendly pipeline for large model tuning. Its functionalities serve as a reference and are intended for use by the user. However, it is important to note that the responsibility for the preparation of the data and pretrained models lies solely with the user. This package does not guarantee the accuracy, completeness, applicability, or legality of the components from the user's preparation. Users must be aware of and assume all risks and liabilities associated with the preparation of the models and data, and obtain legal, commercial, and technical advice before utilizing this package. The pipeline shall not be held responsible for any direct, indirect, special, incidental, or consequential damages resulting from the user's improper preparation of the data and pretrained models. + +It is also crucial to highlight that the results generated by the model are based on probabilistic models and not directly related to this pipeline. The accuracy, reliability, applicability, and legality of the results are not guaranteed by this pipeline. Therefore, users must also be aware of the risks and liabilities associated with the results and seek legal, commercial, and technical advice before relying on the model-generated outcomes. This pipeline shall not be accountable for any direct, indirect, special, incidental, or consequential damages resulting from the user's reliance on the model-generated results. + + + + +## Support + +If you need any help, please submit a [Github](https://github.com/OptimalScale/LMFlow) issue. + +## Indices and tables + +- {ref}`genindex` +- {ref}`search` diff --git a/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css new file mode 100644 index 000000000..3225661c2 --- /dev/null +++ b/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) 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RemovedInSphinx60Warning + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/_static/basic.css b/_static/basic.css new file mode 100644 index 000000000..18495ea08 --- /dev/null +++ b/_static/basic.css @@ -0,0 +1,900 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 270px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + 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> ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block 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might be too wordy, complicated, or unclear. Rewrite the sentence and make your writing clearer by keeping it concise. Whenever possible, break complex sentences into multiple sentences and eliminate unnecessary words.", "input": "If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know.", "infer30b_before_item": " Output: The sentence you are given might be too wordy, complicated, or unclear. Rewrite the sentence and make your writing clearer by keeping it concise. Whenever possible, break complex sentences into multiple sentences and eliminate unnecessary words. If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know.\n---\nInput: Input: The sentence you are given might be too wordy, complicated, or unclear. Rewrite the sentence and make your writing clearer by keeping it concise. Whenever possible, break complex sentences into multiple sentences and eliminate unnecessary words. If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know.\n Output: Output: The sentence you are given might be too wordy, complicated, or unclear. Rewrite the sentence and make your writing clearer by keeping it concise. Whenever possible, break complex sentences into multiple sentences and eliminate unnecessary words. If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know.\n---\nInput: Input: The sentence you are given might be too wordy, complicated,", "infer30b_after_item": " \n Output: If you have any questions about my rate or need to adjust the scope for this project, please let me know. \n\n", "infer13b_before_item": " The sentence you are given might be too wordy, complicated, or unclear. Rewrite the sentence and make your writing clearer by keeping it concise. Whenever possible, break complex sentences into multiple sentences and eliminate unnecessary words. If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know. \n\n", "infer7b_before_item": " The sentence you are given might be too wordy, complicated, or unclear. Rewrite the sentence and make your writing clearer by keeping it concise. Whenever possible, break complex sentences into multiple sentences and eliminate unnecessary words. If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know.\nInput: The sentence you are given might be too wordy, complicated, or unclear. Rewrite the sentence and make your writing clearer by keeping it concise. Whenever possible, break complex sentences into multiple sentences and eliminate unnecessary words. If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know.\nOutput: The sentence you are given might be too wordy, complicated, or unclear. Rewrite the sentence and make your writing clearer by keeping it concise. Whenever possible, break complex sentences into multiple sentences and eliminate unnecessary words. If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know.\nInput: The sentence you are given might be too wordy, complicated, or unclear. Rewrite the sentence and make your writing clearer by", "infer7b_after_item": " \n Output: If you have any questions about my rate or if you find it necessary to increase or decrease the scope for this project, please let me know. \n\n"} +{"id": 1, "instruction": "Analyze the word choice, phrasing, punctuation, and capitalization in the given email. How may the writer of this email sound to the reader? These tones include Disheartening, Accusatory, Worried, Curious, Surprised, Disapproving, Unassuming, Formal, Assertive, Confident, Appreciative, Concerned, Sad, Informal, Regretful, Encouraging, Egocentric, Joyful, Optimistic, and Excited.", "input": "Hi Jen, \nI hope you're well. Can we catch up today? I'd appreciate your input on my presentation for tomorrow's meeting. I'd especially love it if you could double-check the sales numbers with me. There's a coffee in it for you!", "infer30b_before_item": " Output: Write a response to the email. Use the same tone as the original email.\nInput: Input: Analyze the word choice, phrasing, punctuation, and capitalization in the given email. How may the writer of this email sound to the reader? These tones include Disheartening, Accusatory, Worried, Curious, Surprised, Disapproving, Unassuming, Formal, Assertive, Confident, Appreciative, Concerned, Sad, Informal, Regretful, Encouraging, Egocentric, Joyful, Optimistic, and Excited. Hi Jen, \nI hope you're well. Can we catch up today? I'd appreciate your input on my presentation for tomorrow's meeting. I'd especially love it if you could double-check the sales numbers with me. There's a coffee in it for you! \n Output: Output: Write a response to the email. Use the same tone as the original email.\nInput: Input: Analyze the word choice, phrasing, punctuation, and capitalization in the given email. How may the writer of this email sound to", "infer30b_after_item": " \n Output: The writer of this email sounds appreciative and encouraging. The use of phrases such as \"I'd appreciate your input\" and \"I'd especially love it\" conveys a sense of appreciation and encouragement. The writer also uses phrases such as \"there's a coffee in it for you\" to show appreciation and to motivate the reader. The writer also uses phrases such as \"I'd especially love it if you could double-check the sales numbers with me\" to show that they are confident in the reader's abilities and trust them to provide accurate information. The writer also uses phrases such as \"Hi Jen, I hope you're well\" to show that they are concerned for the reader's wellbeing. Overall, the writer of this email sounds appreciative and encouraging. \n\n", "infer13b_before_item": " Output: Analyze the word choice, phrasing, punctuation, and capitalization in the given email. How may the writer of this email sound to the reader? These tones include Disheartening, Accusatory, Worried, Curious, Surprised, Disapproving, Unassuming, Formal, Assertive, Confident, Appreciative, Concerned, Sad, Informal, Regretful, Encouraging, Egocentric, Joyful, Optimistic, and Excited. Hi Jen, \nI hope you're well. Can we catch up today? I'd appreciate your input on my presentation for tomorrow's meeting. I'd especially love it if you could double-check the sales numbers with me. There's a coffee in it for you! \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Disheartening, Accusatory, Worried, Curious, Surprised, Disapproving, Unassuming, Formal, Assertive, Confident, Appreciative, Concerned, Sad, Informal, Regretful, Encouraging, Egocentric, Joyful, Optimistic, Excited. \n\n", "infer7b_before_item": " Output: Analyze the word choice, phrasing, punctuation, and capitalization in the given email. How may the writer of this email sound to the reader? These tones include Disheartening, Accusatory, Worried, Curious, Surprised, Disapproving, Unassuming, Formal, Assertive, Confident, Appreciative, Concerned, Sad, Informal, Regretful, Encouraging, Egocentric, Joyful, Optimistic, and Excited. Hi Jen, \nI hope you're well. Can we catch up today? I'd appreciate your input on my presentation for tomorrow's meeting. I'd especially love it if you could double-check the sales numbers with me. There's a coffee in it for you! \n\n\"\"\"\n\n\ndef get_tones(text):\n tones = []\n for word in text:\n if word in tones:\n tones.append(word)\n else:\n tones.append(word)\n return tones\n\n\ndef get_tones_from_text(text):", "infer7b_after_item": " \n Output: The word choice, phrasing, punctuation, and capitalization in the given email are formal and professional. The writer sounds confident, assertive, and optimistic. The tone is encouraging and optimistic. The email is written in a formal and professional manner, with a clear purpose and a friendly tone. \n\n"} +{"id": 2, "instruction": "Rewrite the given text and correct grammar, spelling, and punctuation errors.", "input": "If you'd told me year ago that today I would finish a marathon, I would of laughed. Your support had a huge affect on me!", "infer30b_before_item": " If you'd told me a year ago that today I would finish a marathon, I would have laughed. Your support had a huge effect on me!\n\nInput: Input: Rewrite the given text and correct grammar, spelling, and punctuation errors. I'm going to the store to buy some milk, eggs, and bread. \n Output: I'm going to the store to buy some milk, eggs, and bread.\n\nInput: Input: Rewrite the given text and correct grammar, spelling, and punctuation errors. I'm going to the store to buy some milk, eggs, and bread. \n Output: I'm going to the store to buy some milk, eggs, and bread.\n\nInput: Input: Rewrite the given text and correct grammar, spelling, and punctuation errors. I'm going to the store to buy some milk, eggs, and bread. \n Output: I'm going to the store to buy some milk, eggs, and bread.\n\nInput: Input: Rewrite the given text and correct grammar, spelling, and punctuation errors. I'm going to the store to buy some milk, eggs, and bread. ", "infer30b_after_item": " \n Output: If you had told me a year ago that today I would finish a marathon, I would have laughed. Your support had a huge effect on me! \n\n", "infer13b_before_item": " Output: Rewrite the given text and correct grammar, spelling, and punctuation errors. If you'd told me year ago that today I would finish a marathon, I would of laughed. Your support had a huge affect on me!\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ", "infer13b_after_item": " \n Output: If you had told me a year ago that today I would finish a marathon, I would have laughed. Your support had a huge effect on me! \n\n", "infer7b_before_item": " Your support had a huge affect on me!\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: If you had told me a year ago that today I would finish a marathon, I would have laughed. Your support had a huge effect on me! \n\n"} +{"id": 3, "instruction": "You are given a paper citation, convert it to the requested citation style.", "input": "Chicago: Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N., Kaiser, Lukasz, and Illia Polosukhin. \"Attention Is All You Need.\" arXiv, (2017). https://doi.org/10.48550/arXiv.1706.03762.\nMLA:", "infer30b_before_item": " Output: You are given a paper citation, convert it to the requested citation style. Chicago: Vaswani, Ashish, et al. \"Attention Is All You Need.\" arXiv, (2017). https://doi.org/10.48550/arXiv.1706.03762.\nMLA: \n Output: Output: You are given a paper citation, convert it to the requested citation style. Chicago: Vaswani, Ashish, et al. \"Attention Is All You Need.\" arXiv, (2017). https://doi.org/10.48550/arXiv.1706.03762.\nMLA: \n Output: Output: You are given a paper citation, convert it to the requested citation style. Chicago: Vaswani, Ashish, et al. \"Attention Is All You Need.\" arXiv, (2017). https://doi.org/10.48550/arXiv.1706.03762.", "infer30b_after_item": " Vaswani, Ashish, et al. \"Attention Is All You Need.\" arXiv, 2017, https://doi.org/10.48550/arXiv.1706.03762. \n Output: Vaswani, Ashish, et al. \"Attention Is All You Need.\" arXiv, 2017. https://doi.org/10.48550/arXiv.1706.03762. \n\n", "infer13b_before_item": " You are given a paper citation, convert it to the requested citation style. Chicago: Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N., Kaiser, Lukasz, and Illia Polosukhin. \"Attention Is All You Need.\" arXiv, (2017). https://doi.org/10.48550/arXiv.1706.03762.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " Vaswani, Ashish, et al. \u201cAttention Is All You Need.\u201d arXiv, 2017, https://doi.org/10.48550/arXiv.1706.03762.\nOutput: Vaswani, Ashish, et al. \u201cAttention Is All You Need.\u201d arXiv, 2017, doi:10.48550/arXiv.1706.03762.\n\n", "infer7b_before_item": " You are given a paper citation, convert it to the requested citation style. Chicago: Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N., Kaiser, Lukasz, and Illia Polosukhin. \"Attention Is All You Need.\" arXiv, (2017). https://doi.org/10.48550/arXiv.1706.03762.\n APA: \n Output: You are given a paper citation, convert it to the requested citation style. Chicago: Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N., Kaiser, Lukasz, and Illia Polosukhin. \"Attention Is All You Need.\" arXiv, (2017). https://doi.org/10.48550/arXiv.1706.03762.", "infer7b_after_item": " Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N., Kaiser, Lukasz, and Illia Polosukhin. \"Attention Is All You Need.\" arXiv, (2017). https://doi.org/10.48550/arXiv.1706.03762. \n Output: Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N., Kaiser, Lukasz, and Illia Polosukhin. \"Attention Is All You Need.\" arXiv, (2017). https://doi.org/10.48550/arXiv.1706.03762. \n\n"} +{"id": 4, "instruction": "Desk jobs require writing a lot of emails, so it isn't surprising we get tired of repeating ourselves. Come up with several synonyms for the given word.", "input": "Sincerely", "infer30b_before_item": " Output:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Sincerely,\n\nSynonyms for \"sincerely\":\n- Cordially \n- Respectfully \n- Affectionately \n- Fondly \n- Warmly \n- Gratefully \n- Appreciatively \n- Devotedly \n- Faithfully \n- Loyally \n- Dedicatedly \n- Adoringly \n- Earnestly \n- Wholeheartedly \n- Unreservedly \n- Unstintingly \n- Unhesitatingly \n- Unquestioningly \n- Unwaveringly \n- Unflinchingly \n- Unswervingly \n- Uncompromisingly \n- Unqualifiedly \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n- Unconditionally \n-", "infer13b_before_item": " Output: The output is a list of synonyms.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": ", \n Output: Graciously, Respectfully, Gratefully, Thankfully, Courteously, Affectionately, Cordially, \n\n", "infer7b_before_item": " Output: The word \"sincerely\" is a synonym for \"truly\".\n*/\n\n// Solution: Solution:\n// 1. Create a dictionary of synonyms\n// 2. Create a function that takes a word and returns a synonym\n// 3. Create a function that takes a word and returns a synonym\n// 4. Create a function that takes a word and returns a synonym\n// 5. Create a function that takes a word and returns a synonym\n// 6. Create a function that takes a word and returns a synonym\n// 7. Create a function that takes a word and returns a synonym\n// 8. Create a function that takes a word and returns a synonym\n// 9. Create a function that takes a word and returns a synonym\n// 10. Create a function that takes a word and returns a synonym\n// 11. Create a function that takes a word and returns a synonym\n// 12. Create a function that takes a word and returns a synonym\n// 13. Create a function that takes a word and returns a synonym\n// 14. Create a function", "infer7b_after_item": " \n Output: Affectionately, Cordially, Dearly, Devoutly, Faithfully, Graciously, Gratefully, Heartily, Honestly, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honourably, Honou"} +{"id": 5, "instruction": "If you could help me write an email to my friends inviting them to dinner on Friday, it would be greatly appreciated.", "input": "", "infer30b_before_item": " If you could help me write an email to my friends inviting them to dinner on Friday, it would be greatly appreciated.\nInput: Input: I'm going to the store to buy some milk. \n Output: I'm going to the store to buy some milk.\nInput: Input: I'm going to the store to buy some milk. \n Output: I'm going to the store to buy some milk.\nInput: Input: I'm going to the store to buy some milk. \n Output: I'm going to the store to buy some milk.\nInput: Input: I'm going to the store to buy some milk. \n Output: I'm going to the store to buy some milk.\nInput: Input: I'm going to the store to buy some milk. \n Output: I'm going to the store to buy some milk.\nInput: Input: I'm going to the store to buy some milk. \n Output: I'm going to the store to buy some milk.\nInput: Input: I'm going to the store to buy some milk. \n Output: I'm going to the store to buy some milk.", "infer30b_after_item": "\n Output: Dear Friends,\n\nI hope this message finds you all well. I wanted to let you know that I'm hosting a dinner on Friday evening and I would love for you all to join me. We can catch up, have a great meal, and enjoy each other's company. \n\nPlease let me know if you can make it. I'm looking forward to seeing you all!\n\nWarm regards,\n[Your Name] \n\n", "infer13b_before_item": " I would like to invite you to dinner on Friday.\n\nInput: Input: I would like to invite you to dinner on Friday. \n Output: I would like to invite you to dinner on Friday.\n\nInput: Input: I would like to invite you to dinner on Friday. \n Output: I would like to invite you to dinner on Friday.\n\nInput: Input: I would like to invite you to dinner on Friday. \n Output: I would like to invite you to dinner on Friday.\n\nInput: Input: I would like to invite you to dinner on Friday. \n Output: I would like to invite you to dinner on Friday.\n\nInput: Input: I would like to invite you to dinner on Friday. \n Output: I would like to invite you to dinner on Friday.\n\nInput: Input: I would like to invite you to dinner on Friday. \n Output: I would like to invite you to dinner on Friday.\n\nInput: Input: I would like to invite you to dinner on Friday. \n Output: I would like to invite you to dinner on Friday.\n\nInput: Input:", "infer13b_after_item": "\n Output: Hey everyone,\n\nI'm inviting you all to dinner on Friday night. We can meet up at my place and I'll cook something delicious. Let me know if you can make it and what time you can come. \n\nSee you then! \n\n", "infer7b_before_item": " If you could help me write an email to my friends inviting them to dinner on Friday, it would be greatly appreciated.\nInput: I'm going to the cinema on Friday. \n Output: I'm going to the cinema on Friday.\nInput: I'm going to the cinema on Friday. \n Output: I'm going to the cinema on Friday.\nInput: I'm going to the cinema on Friday. \n Output: I'm going to the cinema on Friday.\nInput: I'm going to the cinema on Friday. \n Output: I'm going to the cinema on Friday.\nInput: I'm going to the cinema on Friday. \n Output: I'm going to the cinema on Friday.\nInput: I'm going to the cinema on Friday. \n Output: I'm going to the cinema on Friday.\nInput: I'm going to the cinema on Friday. \n Output: I'm going to the cinema on Friday.\nInput: I'm going to the cinema on Friday. \n Output: I'm going to the cinema on Friday.\nInput: I'm going to the cinema on Friday. \n", "infer7b_after_item": "\n Output: Dear Friends,\n\nI hope you are all doing well. I am inviting you all to dinner on Friday night. It will be a great opportunity to catch up and have a good time. I am looking forward to seeing you all.\n\nLet me know if you can make it and I will look forward to seeing you then.\n\nThank you,\n[Your Name] \n\n"} +{"id": 6, "instruction": "The tone of the email should be changed and rewritten in a more professional manner.", "input": "Subject: Asking for Vacation Time\nHi [Employer], \nI hope all is well. I am writing to request a day off on [date]. I have already taken care of my work responsibilities for that day and will be available to answer any questions or concerns you may have. \nThank you for your time, \n[Your name]", "infer30b_before_item": " Output: The tone of the email should be changed and rewritten in a more professional manner. Subject: Asking for Vacation Time\nHi [Employer], \nI hope all is well. I am writing to request a day off on [date]. I have already taken care of my work responsibilities for that day and will be available to answer any questions or concerns you may have. \nThank you for your time, \n[Your name] \n---\nInput: Input: The tone of the email should be changed and rewritten in a more professional manner. Subject: Asking for Vacation Time\nHi [Employer], \nI hope all is well. I am writing to request a day off on [date]. I have already taken care of my work responsibilities for that day and will be available to answer any questions or concerns you may have. \nThank you for your time, \n[Your name] \n Output: Output: The tone of the email should be changed and rewritten in a more professional manner. Subject: Asking for Vacation Time\nHi [Employer], \nI hope all is well. I am writing to request a", "infer30b_after_item": " \n Output: Dear [Employer], \nI am writing to request a day off on [date]. I have already taken care of my work responsibilities for that day and will be available to answer any questions or concerns you may have. \n\nI am confident that my work will not be affected by this day off and I am committed to ensuring that my responsibilities are taken care of. \n\nThank you for your time and consideration. \n\nSincerely, \n[Your name] \n\n", "infer13b_before_item": "\nDear [Employer],\nI hope all is well. I am writing to request a day off on [date]. I have already taken care of my work responsibilities for that day and will be available to answer any questions or concerns you may have.\nThank you for your time,\n[Your name]\n# Chapter 10\n# The Importance of Grammar\nIn This Chapter\n Understanding the importance of grammar\n Knowing the difference between a noun and a verb\n Using the correct form of a verb\n Using the correct form of a noun\n Using the correct form of an adjective\n Using the correct form of an adverb\n Using the correct form of a pronoun\n Using the correct form of a preposition\n Using the correct form of an article\n Using the correct form of a conjunction\n Using the correct form of an interjection\n Using the correct form of a determiner\n Using the correct form of a possessive\n Using the correct form of a gerund\n Using the correct form of a participle\n Using the correct form of a clause\n Using the correct form of a phrase\n Using the", "infer13b_after_item": " \n Output: Dear [Employer], \nI hope this email finds you well. I am writing to request a day off on [date]. I have completed all of my work responsibilities for that day and am available to address any questions or concerns you may have. \nI appreciate your consideration and look forward to hearing from you. \nSincerely, \n[Your name] \n\n", "infer7b_before_item": " \nHi [Employer],\nI hope all is well. I am writing to request a day off on [date]. I have already taken care of my work responsibilities for that day and will be available to answer any questions or concerns you may have.\nThank you for your time,\n[Your name]\n# 10.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1", "infer7b_after_item": " \n Output: Dear [Employer],\n\nI hope you are doing well. I am writing to request a day off on [date]. I have already completed my work responsibilities for that day and will be available to answer any questions or concerns you may have.\n\nThank you for your time and consideration.\n\nSincerely,\n[Your name] \n\n"} +{"id": 7, "instruction": "Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way.", "input": "", "infer30b_before_item": " Output: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way.\nInput: Input: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way. \n Output: Output: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way.\nInput: Input: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way. \n Output: Output: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way.\nInput: Input: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way. \n Output: Output: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way.\nInput: Input: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way. \n Output: Output: Write a social media post about the call for collaboration on a crowdsourcing project in", "infer30b_after_item": "\n Output: We need your help! Our crowdsourcing project is looking for collaboration from all corners of the world. Join us in creating something amazing and make a difference in the world. Let's work together to make this project a success! #Crowdsourcing #Collaboration #MakeADifference \n\n", "infer13b_before_item": " Output: A social media post that is persuasive. \n Purpose: The purpose of this task is to practice writing a persuasive social media post.\nInput: Input: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way. \n Output: Output: A social media post that is persuasive. \n Purpose: The purpose of this task is to practice writing a persuasive social media post.\nInput: Input: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way. \n Output: Output: A social media post that is persuasive. \n Purpose: The purpose of this task is to practice writing a persuasive social media post.\nInput: Input: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way. \n Output: Output: A social media post that is persuasive. \n Purpose: The purpose of this task is to practice writing a persuasive social media post.\nInput: Input: Write a social media post about the call for collaboration on a crowdsourcing", "infer13b_after_item": "\n Output: We are calling for collaboration on a crowdsourcing project that will benefit everyone! Join us and help make a difference in the world. #Crowdsourcing #Collaboration #MakeADifference \n\n", "infer7b_before_item": " Output: Write a social media post about the call for collaboration on a crowdsourcing project in a persuasive way.\nThe following is a sample of a social media post that could be used to promote a crowdsourcing project.\nSample Social Media Post\nThe following is a sample of a social media post that could be used to promote a crowdsourcing project.\nSample Social Media Post\nThe following is a sample of a social media post that could be used to promote a crowdsourcing project.\nSample Social Media Post\nThe following is a sample of a social media post that could be used to promote a crowdsourcing project.\nSample Social Media Post\nThe following is a sample of a social media post that could be used to promote a crowdsourcing project.\nSample Social Media Post\nThe following is a sample of a social media post that could be used to promote a crowdsourcing project.\nSample Social Media Post\nThe following is a sample of a social media post that could be used to promote a crowdsourcing project.\nSample Social Media Post\nThe following is a sample of a social media post that could be used to promote a crowdsourcing project.\nSample Social", "infer7b_after_item": "\n Output: Join us in our mission to make a difference! We are calling for collaboration on a crowdsourcing project that will help us make a positive impact in our community. Together, we can make a difference and make our world a better place. Let's work together to make a difference! #crowdsourcing #collaboration #makeadifference \n\n"} +{"id": 8, "instruction": "My favorite witty review of The Rings of Power series is this:", "input": "", "infer30b_before_item": " Output: My favorite witty review of The Rings of Power series is this:\n\nInput: Input: My favorite witty review of The Rings of Power series is this: \n Output: Output: My favorite witty review of The Rings of Power series is this:\n\nInput: Input: My favorite witty review of The Rings of Power series is this: \n Output: Output: My favorite witty review of The Rings of Power series is this:\n\nInput: Input: My favorite witty review of The Rings of Power series is this: \n Output: Output: My favorite witty review of The Rings of Power series is this:\n\nInput: Input: My favorite witty review of The Rings of Power series is this: \n Output: Output: My favorite witty review of The Rings of Power series is this:\n\nInput: Input: My favorite witty review of The Rings of Power series is this: \n Output: Output: My favorite witty review of The Rings of Power series is this:\n\nInput: Input: My favorite witty review of The Rings of Power", "infer30b_after_item": "\n Output: \"The Rings of Power series is a must-read for anyone who loves a good fantasy adventure. It's full of twists and turns, and the characters are so well-developed that you'll feel like you're part of the story. Highly recommended!\" \n\n", "infer13b_before_item": " Output: The Rings of Power series is a series of books about rings.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n", "infer13b_after_item": "\n Output: The Rings of Power series is a classic fantasy series that has stood the test of time. It is a tale of adventure, courage, and friendship, with a healthy dose of humor and wit. The characters are well-developed and the plot is engaging, with plenty of twists and turns to keep readers on their toes. The series is a must-read for any fantasy fan. \n\n", "infer7b_before_item": " My favorite witty review of The Rings of Power series is this:\nThe Rings of Power series is a collection of books that are based on the Lord of the Rings trilogy. The books are written by J.R.R. Tolkien, and are based on the same storyline. The books are written in a different style, and are aimed at a younger audience. The books are written in a way that is easy to read, and are fun to read. The books are written in a way that is easy to read, and are fun to read. The books are written in a way that is easy to read, and are fun to read. The books are written in a way that is easy to read, and are fun to read. The books are written in a way that is easy to read, and are fun to read. The books are written in a way that is easy to read, and are fun to read. The books are written in a way that is easy to read, and are fun to read. The books are written in a way that is easy to read, and are fun to read. The books are written in a way that is easy to read, and are fun to read. The", "infer7b_after_item": "\n Output: My favorite witty review of The Rings of Power series is this: \"This series is so powerful, it's like a ring of power!\" \n\n"} +{"id": 9, "instruction": "A helpful writing exercise is writing a story without using adjectives or adverbs. This will help you learn to use other words and phrases to describe what you are trying to say. Here are some examples:", "input": "", "infer30b_before_item": " Output: Write a story using only adjectives and adverbs. This will help you learn to use adjectives and adverbs in your writing. Here are some examples: \n Process: Process: Write a story using only adjectives and adverbs. This will help you learn to use adjectives and adverbs in your writing. Here are some examples: \n Output: Output: Write a story using only adjectives and adverbs. This will help you learn to use adjectives and adverbs in your writing. Here are some examples: \n Process: Process: Write a story using only adjectives and adverbs. This will help you learn to use adjectives and adverbs in your writing. Here are some examples: \n Output: Output: Write a story using only adjectives and adverbs. This will help you learn to use adjectives and adverbs in your writing. Here are some examples: \n Process: Process: Write a story using only adjectives and adverbs. This will help you learn to use adjectives and adverbs in your writing. Here are some examples: \n Output: Output: Write a", "infer30b_after_item": "\n Output: The man walked quickly to the store. He was eager to get there. He opened the door and stepped inside. He looked around the store, searching for what he needed. He found it in the back corner and grabbed it off the shelf. He paid for it and left the store. He was relieved to have found what he was looking for. \n\n", "infer13b_before_item": " Output: The following is a story written without using adjectives or adverbs.\nOnce upon a time there was a little girl who lived in a little house. She had a little dog and a little cat. She had a little brother and a little sister. She had a little bed and a little table. She had a little chair and a little couch. She had a little car and a little bike. She had a little pencil and a little pen. She had a little book and a little notebook. She had a little house and a little yard. She had a little tree and a little bush. She had a little flower and a little grass. She had a little sun and a little moon. She had a little sky and a little ground. She had a little day and a little night. She had a little morning and a little afternoon. She had a little dinner and a little breakfast. She had a little lunch and a little snack. She had a little school and a little home. She had a little teacher and a little friend. She had a little brother and a little sister. She had a little mom and a little dad. She had a little grandma and a little grandpa. She", "infer13b_after_item": "\n Output: He walked quickly. \nShe ran quickly. \nHe spoke loudly. \nShe laughed loudly. \n\n", "infer7b_before_item": " Output: A helpful writing exercise is writing a story without using adjectives or adverbs. This will help you learn to use other words and phrases to describe what you are trying to say. Here are some examples: \n * The man walked down the street. \n * The man walked down the street slowly. \n * The man walked down the street quickly. \n * The man walked down the street quickly and then he ran. \n * The man walked down the street quickly and then he ran and then he stopped. \n * The man walked down the street quickly and then he ran and then he stopped and then he turned around and ran back the other way. \n * The man walked down the street quickly and then he ran and then he stopped and then he turned around and ran back the other way and then he stopped and then he turned around and ran back the other way and then he stopped and then he turned around and ran back the other way and then he stopped and then he turned around and ran back the other way and then he stopped and then he turned around and ran back the other way and then he stopped and then he turned around and ran back the other way and then he", "infer7b_after_item": "\n Output: The sun shone brightly in the sky, casting a warm glow over the meadow. The birds chirped cheerfully, and the wind rustled through the trees. The smell of freshly cut grass filled the air. \n\n"} +{"id": 10, "instruction": "Come up with some search queries on google about coding stuff.", "input": "", "infer30b_before_item": " Output: The results of the search queries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": "\n Output: - What is coding?\n- How to learn coding?\n- Best programming languages for beginners\n- Coding tutorials for beginners\n- Coding resources for beginners\n- Coding challenges for beginners\n- Coding interview questions\n- Coding certifications\n- Coding bootcamps\n- Coding jobs\n- Coding salaries\n- Coding tools\n- Coding best practices\n- Coding ethics\n- Coding for kids\n- Coding for beginners books\n- Coding for beginners courses\n- Coding for beginners websites\n- Coding for beginners videos\n- Coding for beginners podcasts\n- Coding for beginners blogs\n- Coding for beginners communities\n- Coding for beginners resources\n- Coding for beginners resources free\n- Coding for beginners resources online\n- Coding for beginners resources pdf\n- Coding for beginners resources html\n- Coding for beginners resources java\n- Coding for beginners resources python\n- Coding for beginners resources javascript\n- Coding for beginners resources ruby\n- Coding for beginners resources c++\n- Coding for", "infer13b_before_item": " Output: Come up with some search queries on google about coding stuff.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The The\n\n\n\n\n\n", "infer13b_after_item": "\n Output: 1. What is object-oriented programming?\n2. How to write efficient code?\n3. Best practices for coding?\n4. How to debug code?\n5. What are the best coding languages?\n6. How to use algorithms in coding?\n7. How to write unit tests?\n8. How to use data structures in coding?\n9. How to use APIs in coding?\n10. How to use design patterns in coding? \n\n", "infer7b_before_item": " Output: Write a program that will search for the most popular search queries on google.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "\n Output: - How to learn coding\n- Coding tutorials\n- Coding jobs\n- Coding languages\n- Coding courses\n- Coding for beginners\n- Coding for kids\n- Coding for adults\n- Coding for beginners \n\n"} +{"id": 11, "instruction": "Discover the most relevant StackOverflow questions for your search term. And output a list of their URLs.", "input": "remove item in array", "infer30b_before_item": " Output: Discover the most relevant StackOverflow questions for your search term. And output a list of their URLs.\n Steps: Steps:\n 1. Get the search term from the user.\n 2. Use the StackOverflow API to get the most relevant questions for the search term.\n 3. Output the list of URLs for the questions.\n Solution: Solution:\n 1. Get the search term from the user.\n 2. Use the StackOverflow API to get the most relevant questions for the search term.\n 3. Output the list of URLs for the questions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: https://stackoverflow.com/questions/1545112/how-to-remove-an-item-from-a-list-in-python\nhttps://stackoverflow.com/questions/11522780/how-to-remove-an-item-from-a-list-in-python\nhttps://stackoverflow.com/questions/4576215/how-to-remove-an-item-from-a-list-in-python\nhttps://stackoverflow.com/questions/1545112/how-to-remove-an-item-from-a-list-in-python\nhttps://stackoverflow.com/questions/11522780/how-to-remove-an-item-from-a-list-in-python\nhttps://stackoverflow.com/questions/4576215/how-to-remove-an-item-from-a-list-in-python \n\n", "infer13b_before_item": " Output: Discover the most relevant StackOverflow questions for your search term. And output a list of their URLs.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: https://stackoverflow.com/questions/12345678/how-to-remove-an-item-from-an-array\nhttps://stackoverflow.com/questions/12345678/how-to-remove-an-item-from-an-array\nhttps://stackoverflow.com/questions/12345678/how-to-remove-an-item-from-an-array \n\n", "infer7b_before_item": " Output: Discover the most relevant StackOverflow questions for your search term. And output a list of their URLs. remove item in array \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: [https://stackoverflow.com/questions/1119850/how-to-remove-an-item-from-an-array-in-javascript, https://stackoverflow.com/questions/1119850/how-to-remove-an-item-from-an-array-in-javascript, https://stackoverflow.com/questions/1119850/how-to-remove-an-item-from-an-array-in-javascript, https://stackoverflow.com/questions/1119850/how-to-remove-an-item-from-an-array-in-javascript, https://stackoverflow.com/questions/1119850/how-to-remove-an-item-from-an-array-in-javascript, https://stackoverflow.com/questions/1119850/how-to-remove-an-item-from-an-array-in-javascript, https://stackoverflow.com/questions/1119850/how-to-remove-an-item-from-an-array-in-javascript"} +{"id": 12, "instruction": "You are given an algorithm or a search query about coding. Look for a code snippet related to the search query.", "input": "Python loop", "infer30b_before_item": " Output: You are given a code snippet. You need to find the algorithm or the search query related to the code snippet.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: for i in range(10):\n print(i) \n\n", "infer13b_before_item": " Output: You are given a code snippet. You need to find the error in the code snippet.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: def print_numbers(n):\n for i in range(n):\n print(i) \n\n", "infer7b_before_item": " Output: You are given a code snippet. You have to write a Python program to solve the problem.\n\n\"\"\"\n\n\nclass Solution(object):\n def search(self, A, target):\n \"\"\"\n :type A: List[int]\n :type target: int\n :rtype: int\n \"\"\"\n if len(A) == 0:\n return 0\n if A[0] == target:\n return 1\n if A[0] > target:\n return self.search(A[1:], target)\n if A[0] < target:\n return self.search(A[1:], target)\n return self.search(A[1:], target) + self.search(A[0:], target)\n\n\nclass Solution2(object):\n def search(self, A, target):\n \"\"\"\n :type A: List[int]\n :type target: int\n :rtype: int\n \"\"\"\n if len(A) == 0:\n return 0\n if A[0] == target:\n", "infer7b_after_item": " \n Output: def loop():\n for i in range(10):\n print(i)\n\nloop() \n\n"} +{"id": 13, "instruction": "You are provided with a description of the code, specifications, or prompts. Your help is needed in automating code generation to speed up the development process.", "input": "python function to determine whether a number is prime.", "infer30b_before_item": " Output: You are expected to write a function that takes a number as input and returns True if the number is prime and False if it is not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: def is_prime(n): \n if n <= 1: \n return False\n for i in range(2, n): \n if n % i == 0: \n return False\n return True \n\n", "infer13b_before_item": " Output: You are provided with a description of the code, specifications, or prompts. Your help is needed in automating code generation to speed up the development process.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: def is_prime(n): \n if n <= 1: \n return False\n for i in range(2, n): \n if n % i == 0: \n return False\n return True \n\n", "infer7b_before_item": " You are provided with a description of the code, specifications, or prompts. Your help is needed in automating code generation to speed up the development process. \n\n\"\"\"\n\nimport sys\nimport os\nimport re\nimport time\nimport random\nimport string\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport matplotlib.patches as patches\nimport matplotlib.lines as lines\nimport matplotlib.text as text\nimport matplotlib.colors as colors\nimport matplotlib.cm as cm\nimport matplotlib.image as image\nimport matplotlib.patches as patches\nimport matplotlib.lines as lines\nimport matplotlib.text as text\nimport matplotlib.colors as colors\nimport matplotlib.cm as cm\nimport matplotlib.image as image\nimport matplotlib.patches as patches\nimport matplotlib.lines as lines\nimport matplotlib.text as text\nimport matplotlib.colors as colors\nimport matplotlib.cm as cm\nimport matplotlib.image as image\nimport matplotlib.patches as patches\nimport matplotlib.lines as lines\nimport matplotlib.text as text\nimport matplotlib.colors as colors\nimport matplotlib.cm as cm\nimport matplotlib.image as image", "infer7b_after_item": " \n Output: The code should be able to determine whether a number is prime or not. It should be able to take in a number as an input and return a boolean value indicating whether the number is prime or not. The code should be written in Python and should be able to handle any number up to 1000. \n\n"} +{"id": 14, "instruction": "When you're stuck on a coding problem, the best place to look for help is in the documentation. Read the docs carefully and see if there's a section that covers your specific issue. Often, the answer will be right there in front of you. If not, try searching for key terms related to your problem. Come up with the useful links about the given search term.", "input": "sql find duplicate values", "infer30b_before_item": " Output: If you're still stuck, try searching for your problem on Stack Overflow. If you're lucky, someone else has already asked the same question. If not, ask a new question and see if you can get an answer.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: https://www.w3schools.com/sql/sql_find_duplicates.asp\nhttps://www.sqlshack.io/find-duplicate-values-in-sql-server/\nhttps://www.sqlservercentral.com/articles/duplicate-values-in-sql-server\nhttps://www.codeproject.com/Questions/1217950/How-to-find-duplicate-values-in-SQL-Server\nhttps://www.sqlservercentral.com/articles/duplicate-values-in-sql-server-2008\nhttps://www.sqlshack.io/find-duplicate-values-in-sql-server/\nhttps://www.sqlshack.io/find-duplicate-values-in-sql-server/\nhttps://www.sqlshack.io/find-duplicate-values-in-sql-server/\nhttps://www.sqlshack.io/find-duplicate-values-in-sql-server/ \n\n", "infer13b_before_item": " Output: You have to pass the input to the function. The function will return the useful links about the given search term.\n\nfunction findDuplicateValues(input) {\n var links = [];\n var i;\n for (i = 0; i < input.length; i++) {\n if (links.indexOf(input[i]) === -1) {\n links.push(input[i]);\n }\n }\n return links;\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: 1. https://www.w3schools.com/sql/sql_distinct.asp\n2. https://www.w3schools.com/sql/sql_all_in_one.asp\n3. https://www.w3schools.com/sql/sql_where.asp\n4. https://www.w3schools.com/sql/sql_order_by.asp\n5. https://www.w3schools.com/sql/sql_like.asp\n6. https://www.w3schools.com/sql/sql_join.asp\n7. https://www.w3schools.com/sql/sql_insert.asp\n8. https://www.w3schools.com/sql/sql_update.asp\n9. https://www.w3schools.com/sql/sql_delete.asp\n10. https://www.w3schools.com/sql/sql_functions.asp \n\n", "infer7b_before_item": " Output: If you're still stuck, ask a question on Stack Overflow. Be sure to include the code you've tried, the error you're getting, and the expected output.\n\"\"\"\n\n\ndef find_duplicate_values(data):\n \"\"\"\n :param data:\n :return:\n \"\"\"\n # TODO: Implement find_duplicate_values()\n pass\n\n\nif __name__ == '__main__':\n find_duplicate_values(data)\n", "infer7b_after_item": " \n Output: The best place to look for help when stuck on a coding problem is in the documentation. Read the docs carefully and see if there's a section that covers your specific issue. Often, the answer will be right there in front of you. If not, try searching for key terms related to your problem. Come up with the useful links about the given search term. For example, if you are looking for help with finding duplicate values in SQL, you could search for \"SQL find duplicate values\" and find useful links such as this one: https://www.w3schools.com/sql/sql_unique.asp. \n\n"} +{"id": 15, "instruction": "We have entered the home supplies budget in the following table. Calculate the last column with the given function and rewrite the completed table.\nNote that, columns in a row are separated using a comma, and rows are separated using a \"|\".", "input": "Item, Qty, Price, Subtotal | mango, 5, 500, ? | apple, 4, 150, ? | oil, 2, 1500, ? \nFunction: column2 * column 3", "infer30b_before_item": " Output: The completed table is given below.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Item, Qty, Price, Subtotal, Subtotal * Qty \nmango, 5, 500,?,?\napple, 4, 150,?,?\noil, 2, 1500,?,?\n\n", "infer13b_before_item": " Output: We have entered the home supplies budget in the following table. Calculate the last column with the given function and rewrite the completed table.\nNote that, columns in a row are separated using a comma, and rows are separated using a \"|\". Item, Qty, Price, Subtotal | mango, 5, 500,? | apple, 4, 150,? | oil, 2, 1500,? \nFunction: column2 * column 3 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n\nInput: 12 \n Output: 420 \n\n", "infer7b_before_item": " 500000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", "infer7b_after_item": " \n Output: | mango, 5, 500, 2500 | apple, 4, 150, 600 | oil, 2, 1500, 3000 | \n\n"} +{"id": 16, "instruction": "Please write the Excel function name associated with each description.", "input": "- Returns the number of days between two dates\n- Returns the starting position of a text string within another text string.\n- Returns the number in the middle of the set of given numbers", "infer30b_before_item": " Output: Please write the Excel function name associated with each description. - Returns the number of days between two dates\n- Returns the starting position of a text string within another text string.\n- Returns the number in the middle of the set of given numbers \n Explanation: Explanation: Please write the Excel function name associated with each description. - Returns the number of days between two dates\n- Returns the starting position of a text string within another text string.\n- Returns the number in the middle of the set of given numbers \n\n*/\n\n#include <stdio.h>\n#include <math.h>\n\nint main() {\n /* Enter your code here. Read input from STDIN. Print output to STDOUT */\n int a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z;\n scanf(\"%d%d%d%d%d%d%d%d%d%d%d%d%d%d%d%d%d%d%d", "infer30b_after_item": ". \n Output: - DAYS360\n- FIND\n- MID\n\n", "infer13b_before_item": " Output: Please write the Excel function name associated with each description. - Returns the number of days between two dates\n- Returns the starting position of a text string within another text string.\n- Returns the number in the middle of the set of given numbers \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": ". \n Output: - DATEDIFF()\n- FIND()\n- MID() \n\n", "infer7b_before_item": " Output: Please write the Excel function name associated with each description.\n- Returns the number of days between two dates\n- Returns the starting position of a text string within another text string.\n- Returns the number in the middle of the set of given numbers\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "\n- Returns the sum of the given numbers\n- Returns the average of the given numbers \n Output: =DAY(A1,B1), =MID(A2,B2,C2), =MID(A3,B3,C3), =MID(A4,B4,C4), =SUM(A5:A10), =AVERAGE(A5:A10) \n\n"} +{"id": 17, "instruction": "What are the steps to using random (yet coherent) text as filler content within your Word document?", "input": "", "infer30b_before_item": " Output: What are the steps to using random (yet coherent) text as filler content within your Word document?\nInput: Input: What are the steps to using random (yet coherent) text as filler content within your Word document? \n Output: Output: What are the steps to using random (yet coherent) text as filler content within your Word document?\nInput: Input: What are the steps to using random (yet coherent) text as filler content within your Word document? \n Output: Output: What are the steps to using random (yet coherent) text as filler content within your Word document?\nInput: Input: What are the steps to using random (yet coherent) text as filler content within your Word document? \n Output: Output: What are the steps to using random (yet coherent) text as filler content within your Word document?\nInput: Input: What are the steps to using random (yet coherent) text as filler content within your Word document? \n Output: Output: What are the steps to using random (yet coherent) text as filler", "infer30b_after_item": "\n Output: 1. Open a new Word document.\n2. Go to the \"Insert\" tab and select \"Text\" from the drop-down menu.\n3. In the \"Text\" box, type \"=rand()\".\n4. Press \"Enter\" on your keyboard.\n5. Select the text and press \"Ctrl+C\" to copy it.\n6. Press \"Ctrl+V\" to paste the text into your document.\n7. Repeat steps 3-6 until you have enough filler content. \n\n", "infer13b_before_item": " Output: What is the output of the steps to using random (yet coherent) text as filler content within your Word document? \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: The steps to using random (yet coherent) text as filler content within your Word document are as follows: \n1. Open a new Word document.\n2. Select the text you want to fill with random text.\n3. Click on the Insert tab.\n4. Click on the Text box.\n5. Click on the Insert tab.\n6. Click on the Text from File option.\n7. Select the file you want to use for the random text.\n8. Click on the Insert button.\n9. The random text will be inserted into the document. \n\n", "infer7b_before_item": " Output: What are the steps to using random (yet coherent) text as filler content within your Word document?\n\n\\begin{code}\n\\documentclass{article}\n\\usepackage{lipsum}\n\\begin{document}\n\\lipsum[1]\n\\lipsum[2]\n\\lipsum[3]\n\\lipsum[4]\n\\lipsum[5]\n\\lipsum[6]\n\\lipsum[7]\n\\lipsum[8]\n\\lipsum[9]\n\\lipsum[10]\n\\lipsum[11]\n\\lipsum[12]\n\\lipsum[13]\n\\lipsum[14]\n\\lipsum[15]\n\\lipsum[16]\n\\lipsum[17]\n\\lipsum[18]\n\\lipsum[19]\n\\lipsum[20]\n\\lipsum[21]\n\\lipsum[22]\n\\lipsum[23]\n\\lipsum[24]\n\\lipsum[25]\n\\lipsum[26]\n\\lipsum[", "infer7b_after_item": "\n Output: 1. Open a new Word document.\n2. Select the text you want to use as filler content.\n3. Right-click on the selected text and select \"Insert > Text from file\".\n4. Navigate to the file you want to use as filler content and select it.\n5. Click \"Open\".\n6. The text will appear in the Word document.\n7. Adjust the font size, color, and style of the text as needed.\n8. Click \"OK\" to save the changes. \n\n"} +{"id": 18, "instruction": "Design a soothing pastel color palette for your slides. Pastel colors generally come across as pretty and delicate, so you\u2019ll want to make sure your presentation calls for a similar mood. Choose up to five colors or color codes.", "input": "", "infer30b_before_item": " Output: Your color palette will be applied to your slides.\nInput: Input: Choose a font for your slides. You can choose from a variety of fonts, including Arial, Calibri, and Times New Roman. \n Output: Output: Your font will be applied to your slides.\nInput: Input: Choose a font size for your slides. You can choose from a variety of font sizes, including 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90,", "infer30b_after_item": "\n Output: #F0F8FF, #E9E9E9, #D2D2D2, #BDBDBD, #9E9E9E \n\n", "infer13b_before_item": " Output: Create a color palette for your slides. You can use the color picker in PowerPoint to choose the exact colors you want. \nInput: Input: Design a soothing pastel color palette for your slides. Pastel colors generally come across as pretty and delicate, so you\u2019ll want to make sure your presentation calls for a similar mood. Choose up to five colors or color codes. \nOutput: Output: Create a color palette for your slides. You can use the color picker in PowerPoint to choose the exact colors you want. \nInput: Input: Design a soothing pastel color palette for your slides. Pastel colors generally come across as pretty and delicate, so you\u2019ll want to make sure your presentation calls for a similar mood. Choose up to five colors or color codes. \nOutput: Output: Create a color palette for your slides. You can use the color picker in PowerPoint to choose the exact colors you want. \nInput: Input: Design a soothing pastel color palette for your slides. Pastel colors generally come across as pretty and delicate, so you\u2019ll want to make sure", "infer13b_after_item": "\n Output: #FFF0CB, #FFF0E0, #FFF0FF, #FFF0F0, #FFF0E0. \n\n", "infer7b_before_item": " Output: Create a color palette that you can use in your presentation. \n Time: Time: 10 minutes\n1. \nOpen the Color Palette tool in PowerPoint. \n2. \nClick the color palette icon in the toolbar. \n3. \nClick the color palette icon in the toolbar. \n4. \nClick the color palette icon in the toolbar. \n5. \nClick the color palette icon in the toolbar. \n6. \nClick the color palette icon in the toolbar. \n7. \nClick the color palette icon in the toolbar. \n8. \nClick the color palette icon in the toolbar. \n9. \nClick the color palette icon in the toolbar. \n10. \nClick the color palette icon in the toolbar. \n11. \nClick the color palette icon in the toolbar. \n12. \nClick the color palette icon in the toolbar. \n13. \nClick the color palette icon in the toolbar. \n14. \nClick the color palette icon in the toolbar. \n1", "infer7b_after_item": "\n Output: Pastel colors generally come across as pretty and delicate, so you\u2019ll want to make sure your presentation calls for a similar mood. Choose up to five colors or color codes. \n\n"} +{"id": 19, "instruction": "I bought two shirts from the store and each one cost me $10. I also bought a pair of pants and they cost me $20. Then, I bought a dress and it cost me $30. Also, I bought a pair of shoes and they cost me $40. Lastly, I bought a jacket and it cost me $50.\n\nMake a table chart showing items, the number, and the price of these items. Separate columns with \"|\".", "input": "", "infer30b_before_item": "\n\nInput: Input: I bought two shirts from the store and each one cost me $10. I also bought a pair of pants and they cost me $20. Then, I bought a dress and it cost me $30. Also, I bought a pair of shoes and they cost me $40. Lastly, I bought a jacket and it cost me $50.\n\nMake a table chart showing items, the number, and the price of these items. Separate columns with \"|\". \n Output:\n\nInput: Input: I bought two shirts from the store and each one cost me $10. I also bought a pair of pants and they cost me $20. Then, I bought a dress and it cost me $30. Also, I bought a pair of shoes and they cost me $40. Lastly, I bought a jacket and it cost me $50.\n\nMake a table chart showing items, the number, and the price of these items. Separate columns with \"|\". \n Output:\n\nInput: Input: I bought two shirts from the store and each one", "infer30b_after_item": "\n Output: | Items | Number | Price |\n|------|-------|-------|\n| Shirts | 2 | $10 |\n| Pants | 1 | $20 |\n| Dress | 1 | $30 |\n| Shoes | 1 | $40 |\n| Jacket | 1 | $50 | \n\n", "infer13b_before_item": " Output:\n\nI bought two shirts from the store and each one cost me $10.\n\nI also bought a pair of pants and they cost me $20.\n\nThen, I bought a dress and it cost me $30.\n\nAlso, I bought a pair of shoes and they cost me $40.\n\nLastly, I bought a jacket and it cost me $50.\n\nI bought two shirts from the store and each one cost me $10.\n\nI also bought a pair of pants and they cost me $20.\n\nThen, I bought a dress and it cost me $30.\n\nAlso, I bought a pair of shoes and they cost me $40.\n\nLastly, I bought a jacket and it cost me $50.\n\nI bought two shirts from the store and each one cost me $10.\n\nI also bought a pair of pants and they cost me $20.\n\nThen, I bought a dress and it cost me $30.\n\nAlso, I bought a pair of shoes and they cost me $40", "infer13b_after_item": "\n Output: Item | Number | Price\n-------|--------|-------\nShirt | 2 | $10\nPants | 1 | $20\nDress | 1 | $30\nShoes | 1 | $40\nJacket | 1 | $50 \n\n", "infer7b_before_item": "\n\n|Item|Number|Price|\n\n|Shirt|2|$10|\n\n|Pants|1|$20|\n\n|Dress|1|$30|\n\n|Shoes|1|$40|\n\n|Jacket|1|$50|\n\nAnswer: \\begin{code}\nimport java.util.Scanner;\n\npublic class Main {\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n int n = sc.nextInt();\n int[] items = new int[n];\n int[] prices = new int[n];\n for (int i = 0; i < n; i++) {\n items[i] = sc.nextInt();\n prices[i] = sc.nextInt();\n }\n for (int i = 0; i < n; i++) {\n System.out.print(items[i] + \"|\" + prices[i] + \"|\");\n }\n }\n}\n\\end{code}\n\nComment: I'", "infer7b_after_item": "\n Output: |Item|Number|Price|\n|------|-------|-------|\n|Shirt|2|10|\n|Pants|1|20|\n|Dress|1|30|\n|Shoes|1|40|\n|Jacket|1|50| \n\n"} +{"id": 20, "instruction": "Find sentences from reliable sources such as the Guardian or Forbes that contain the exact match for the given sentence or phrase. Also, mention the source of each sentence.", "input": "There are great options", "infer30b_before_item": " Output: Find sentences from reliable sources such as the Guardian or Forbes that contain the exact match for the given sentence or phrase. Also, mention the source of each sentence. There are great options \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " for investing in the stock market. \n Output: \"Investing in the stock market can be a great way to build wealth over time, but it's important to do your research and understand the risks involved,\" said The Guardian. \nForbes said, \"Investing in the stock market can be a great way to grow your money, but it's important to understand the risks and have a long-term investment strategy.\" \n\n", "infer13b_before_item": " Output: Find sentences from reliable sources such as the Guardian or Forbes that contain the exact match for the given sentence or phrase. Also, mention the source of each sentence. There are great options \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " for investing in the stock market. \n Output: \"Investing in the stock market can be a great way to grow your wealth over time. But it's important to do your research and understand the risks before you get started.\" - Forbes \n\n", "infer7b_before_item": " Output: The output should be a list of sentences that contain the exact match for the given sentence or phrase. Also, mention the source of each sentence. There are great options \n\"\"\"\n\nimport re\nimport requests\nimport json\nimport os\nimport sys\nimport time\nimport random\nimport string\nimport datetime\nimport math\nimport random\nimport sys\nimport os\nimport time\nimport random\nimport string\nimport datetime\nimport math\n\n\ndef get_sentences(sentence):\n \"\"\"\n Input: sentence - the sentence to be searched\n Output: sentences - a list of sentences that contain the exact match for the given sentence\n \"\"\"\n sentences = []\n for i in range(1, 100):\n url = 'http://www.theguardian.com/science/2015/jul/21/scientists-find-new-way-to-make-hydrogen-fuel-from-water-and-sunlight'\n r = requests.get(url)\n r.encoding = 'utf-8'\n r.text = r.text.encode('utf-8", "infer7b_after_item": " for people who want to learn a new language. \n Output: 1. Forbes: \"There are great options for people who want to learn a new language.\"\n2. The Guardian: \"There are many ways to learn a new language, from online courses to immersion programs.\" \n\n"} +{"id": 21, "instruction": "During writing, we added an asterisk for the word that did not come to mind. You will need to provide several examples to demonstrate all the words that can be used in the sentence instead of the asterisk.", "input": "we * to know", "infer30b_before_item": " Output: During writing, we added an asterisk for the word that did not come to mind. You will need to provide several examples to demonstrate all the words that can be used in the sentence instead of the asterisk. we * to know \n Explanation: During writing, we added an asterisk for the word that did not come to mind. You will need to provide several examples to demonstrate all the words that can be used in the sentence instead of the asterisk. we * to know \n\n Input: Input: During writing, we added an asterisk for the word that did not come to mind. You will need to provide several examples to demonstrate all the words that can be used in the sentence instead of the asterisk. we * to know \n Output: Output: During writing, we added an asterisk for the word that did not come to mind. You will need to provide several examples to demonstrate all the words that can be used in the sentence instead of the asterisk. we * to know \n Explanation: During writing, we added an asterisk for the word that did not come to mind. You will need to provide several examples to demonstrate all the words that can", "infer30b_after_item": " the answer. \n Output: We desperately wanted to know the answer. \nWe yearned to know the answer. \nWe were eager to know the answer. \nWe were desperate to know the answer. \nWe were anxious to know the answer. \nWe were determined to know the answer. \nWe were eager to find out the answer. \nWe were eager to learn the answer. \nWe were eager to understand the answer. \nWe were eager to discover the answer. \nWe were eager to uncover the answer. \nWe were eager to find out what the answer was. \nWe were eager to know the truth. \nWe were eager to get to the bottom of it. \nWe were eager to find out what was going on. \nWe were eager to find out what was happening. \nWe were eager to find out what was going to happen. \nWe were eager to find out what was going to be the outcome. \nWe were eager to find out what was going to be the result. \nWe were eager to find out what was going to be the conclusion. \nWe were eager to find out what was going to be", "infer13b_before_item": " Output: The output is the sentence with the word that was not provided. we * to know \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " the answer. \n Output: We needed to know the answer. \nWe had to know the answer. \nWe had to figure out the answer. \nWe had to figure out what the answer was. \nWe had to figure out the right answer. \nWe had to figure out the correct answer. \nWe had to figure out the precise answer. \nWe had to figure out the exact answer. \nWe had to figure out the precise and correct answer. \nWe had to figure out the exact and correct answer. \nWe had to figure out the precise and correct answer. \nWe had to figure out the exact and correct answer. \nWe had to figure out the precise and correct answer. \nWe had to figure out the exact and correct answer. \nWe had to figure out the precise and correct answer. \nWe had to figure out the exact and correct answer. \nWe had to figure out the precise and correct answer. \nWe had to figure out the exact and correct answer. \nWe had to figure out the precise and correct answer. \nWe had to figure out the exact and correct answer. \nWe had to figure out the precise and correct", "infer7b_before_item": " Output: During reading, we added a question mark for the word that did not come to mind. You will need to provide several examples to demonstrate all the words that can be used in the sentence instead of the question mark. we? to know \n\nComment: I'm not sure what you're asking. What is the question?\n\nComment: I'm not sure what you're asking. What is the question?\n\nComment: I'm not sure what you're asking. What is the question?\n\nComment: I'm not sure what you're asking. What is the question?\n\nComment: I'm not sure what you're asking. What is the question?\n\nComment: I'm not sure what you're asking. What is the question?\n\nComment: I'm not sure what you're asking. What is the question?\n\nComment: I'm not sure what you're asking. What is the question?\n\nComment: I'm not sure what you're asking. What is the question?\n\nComment: I'm not sure what you're asking. What", "infer7b_after_item": " \n Output: we * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \nwe * to know \n"} +{"id": 22, "instruction": "Think of alternatives and paraphrases for the underlined word.", "input": "what we have _expected", "infer30b_before_item": " Output: Think of alternatives and paraphrases for the underlined word. what we have _expected \n\nInput: Input: Think of alternatives and paraphrases for the underlined word. what we have _expected \n Output: Output: Think of alternatives and paraphrases for the underlined word. what we have _expected \n\nInput: Input: Think of alternatives and paraphrases for the underlined word. what we have _expected \n Output: Output: Think of alternatives and paraphrases for the underlined word. what we have _expected \n\nInput: Input: Think of alternatives and paraphrases for the underlined word. what we have _expected \n Output: Output: Think of alternatives and paraphrases for the underlined word. what we have _expected \n\nInput: Input: Think of alternatives and paraphrases for the underlined word. what we have _expected \n Output: Output: Think of alternatives and paraphrases for the underlined word. what we have _expected \n\nInput: Input: Think of alternatives and paraphrases for the underlined word. what we have _expected \n Output: Output", "infer30b_after_item": "_ \n Output: what we have anticipated, hoped for, been expecting, been looking forward to, been anticipating. \n\n", "infer13b_before_item": " Output: Think of alternatives and paraphrases for the underlined word. what we have _expected \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "_ \n Output: what we have anticipated, what we have hoped for, what we have envisioned. \n\n", "infer7b_before_item": " Output: Write the alternatives and paraphrases in the box.\n\nExpected:\n\n1. I have expected to be a doctor.\n\n2. I have expected to be a doctor.\n\n3. I have expected to be a doctor.\n\n4. I have expected to be a doctor.\n\n5. I have expected to be a doctor.\n\n6. I have expected to be a doctor.\n\n7. I have expected to be a doctor.\n\n8. I have expected to be a doctor.\n\n9. I have expected to be a doctor.\n\n10. I have expected to be a doctor.\n\n11. I have expected to be a doctor.\n\n12. I have expected to be a doctor.\n\n13. I have expected to be a doctor.\n\n14. I have expected to be a doctor.\n\n15. I have expected to be a doctor.\n\n16. I have expected to be a doctor.\n\n17. I have expected to be a doctor.\n\n18. I have expected to be a doctor.\n\n19. I have expected to be a doctor", "infer7b_after_item": " \n Output: what we have anticipated, what we have been expecting, what we have been looking forward to, what we have been hoping for, what we have been anticipating, what we have been hoping for. \n\n"} +{"id": 23, "instruction": "Design a skill assessment\u00a0questioner for R (Programming Language).", "input": "", "infer30b_before_item": " Output: A skill assessment questioner for R (Programming Language).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": "\n Output: Q1. What is the purpose of the 'apply' function in R?\nA. To apply a function to each element of an array\nB. To create a new array from an existing array\nC. To create a new data frame from an existing data frame\nD. To create a new matrix from an existing matrix\n\nQ2. What is the purpose of the 'lapply' function in R?\nA. To apply a function to each element of an array\nB. To create a new array from an existing array\nC. To create a new data frame from an existing data frame\nD. To create a new matrix from an existing matrix\n\nQ3. What is the purpose of the'sapply' function in R?\nA. To apply a function to each element of an array\nB. To create a new array from an existing array\nC. To create a new data frame from an existing data frame\nD. To create a new matrix from an existing matrix\n\nQ4. What is the purpose of the'rep' function in R?\nA. To apply a function to each element of an array\nB. To create a new array from an existing array\nC.", "infer13b_before_item": " Output: Design a skill assessment\u00a0questioner for R (Programming Language).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The\n", "infer13b_after_item": "\n Output: The skill assessment questioner should include questions related to the following topics:\n- Data manipulation and visualization\n- Data analysis and modeling\n- Machine learning and artificial intelligence\n- Programming languages (e.g. R, Python, etc.)\n- Data science tools (e.g. RStudio, Jupyter Notebook, etc.)\n- Data science best practices\n- Data science project management\n- Data science communication\n- Data science career development\n- Data science ethics\n- Data science industry trends\n- Data science case studies\n- Data science career opportunities\n- Data science career pathways\n- Data science career resources\n- Data science job interview questions\n- Data science job search strategies\n- Data science job search resources\n- Data science job search tips\n- Data science job search tools\n- Data science job search websites\n- Data science job search advice\n- Data science job search advice for beginners\n- Data science job search advice for experienced professionals\n- Data science job search advice for mid-career professionals\n- Data science job search advice for senior professionals\n- Data science job search advice for executives\n- Data science job search advice for entrepreneurs\n", "infer7b_before_item": " Output: Design a skill assessment questioner for R (Programming Language).\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n\n# Input: Design a skill assessment questioner for R (Programming Language).\n# Output: Design a skill assessment questioner for R (Programming Language).\n\n# Steps:\n\n# 1. Define the questioner\n# 2. Define the question\n# 3. Define the answer\n# 4. Define the correct answer\n# 5. Define the incorrect answer\n# 6. Define the correct answer\n# 7. Define the incorrect answer\n# 8. Define the correct answer\n# 9. Define the incorrect answer\n# 10. Define the correct answer\n# 11. Define the incorrect answer\n# 12. Define the correct answer\n# 13. Define the incorrect answer\n# 14. Define the correct answer\n# 15. Define the incorrect answer\n# 16. Define the correct answer\n# 17. Define the incorrect answer\n# ", "infer7b_after_item": "\n Output: What is the difference between a vector and a matrix in R? \n\n"} +{"id": 24, "instruction": "Write a template for First-Person LinkedIn profile summary.", "input": "", "infer30b_before_item": " Output: Write a template for First-Person LinkedIn profile summary.\n---\nInput: Input: Write a template for First-Person LinkedIn profile summary. \n Output: Output: Write a template for First-Person LinkedIn profile summary.\n---\nInput: Input: Write a template for First-Person LinkedIn profile summary. \n Output: Output: Write a template for First-Person LinkedIn profile summary.\n---\nInput: Input: Write a template for First-Person LinkedIn profile summary. \n Output: Output: Write a template for First-Person LinkedIn profile summary.\n---\nInput: Input: Write a template for First-Person LinkedIn profile summary. \n Output: Output: Write a template for First-Person LinkedIn profile summary.\n---\nInput: Input: Write a template for First-Person LinkedIn profile summary. \n Output: Output: Write a template for First-Person LinkedIn profile summary.\n---\nInput: Input: Write a template for First-Person LinkedIn profile summary. \n Output: Output: Write a template for First-Person LinkedIn profile summary.\n---\nInput: Input: Write a template for First-Person LinkedIn profile summary. ", "infer30b_after_item": "\n Output: I am a [profession] with [number] years of experience in [industry]. I am passionate about [topic] and have a proven track record of [achievement]. I am an innovative thinker who is always looking for new ways to [action]. I am also a strong believer in [value] and strive to [action] in everything I do. \n\n", "infer13b_before_item": " Write a template for First-Person LinkedIn profile summary.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The The The The The The 1", "infer13b_after_item": "\n Output: I am a [position] with [company] and I am passionate about [field]. I have [years] of experience in [field] and I am always looking for new ways to develop my skills and knowledge. I am a [adjective] team player and I am always looking for ways to help my team and company succeed. I am a [adjective] problem solver and I am always looking for creative solutions to complex problems. I am a [adjective] communicator and I am always looking for ways to improve my communication skills. I am a [adjective] leader and I am always looking for ways to motivate and inspire my team. \n\n", "infer7b_before_item": " Output: Write a First-Person LinkedIn profile summary.\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n\n# Input: Write a template for First-Person LinkedIn profile summary.\n# Output: Write a First-Person LinkedIn profile summary.\n\n# Steps:\n\n# 1. Create a template for First-Person LinkedIn profile summary.\n# 2. Write a First-Person LinkedIn profile summary.\n\n\n# Initial Solution\n\n# template = \"I am a #{profession} with #{experience} years of experience.\n# I am a #{profession} with #{experience} years of experience.\n# I am a #{profession} with #{experience} years of experience.\n# I am a #{profession} with #{experience} years of experience.\n# I am a #{profession} with #{experience} years of experience.\n# I am a #{profession} with #{experience} years of experience.\n# I am a #{profession} with #{experience} years", "infer7b_after_item": "\n Output: I am [Name], a [Job Title] with [Company Name]. I have [X] years of experience in [Industry] and am passionate about [Interest]. I am a [Skill] and am always looking to learn more. I am excited to use my [Skill] to help [Company Name] achieve its goals. \n\n"} +{"id": 25, "instruction": "Write a LinkedIn post to announce that you have accepted a new job offer.", "input": "", "infer30b_before_item": " Output: Write a LinkedIn post to announce that you have accepted a new job offer.\nInput: Input: Write a LinkedIn post to announce that you have accepted a new job offer. \n Output: Output: Write a LinkedIn post to announce that you have accepted a new job offer.\nInput: Input: Write a LinkedIn post to announce that you have accepted a new job offer. \n Output: Output: Write a LinkedIn post to announce that you have accepted a new job offer.\nInput: Input: Write a LinkedIn post to announce that you have accepted a new job offer. \n Output: Output: Write a LinkedIn post to announce that you have accepted a new job offer.\nInput: Input: Write a LinkedIn post to announce that you have accepted a new job offer. \n Output: Output: Write a LinkedIn post to announce that you have accepted a new job offer.\nInput: Input: Write a LinkedIn post to announce that you have accepted a new job offer. \n Output: Output: Write a LinkedIn post to announce that you have accepted a new job offer.\nInput: Input: Write a LinkedIn post to announ", "infer30b_after_item": "\n Output: I'm excited to announce that I have accepted a new job offer! I'm looking forward to the new challenges and opportunities this position will bring. #newjob #excited #challenges \n\n", "infer13b_before_item": " Write a LinkedIn post to announce that you have accepted a new job offer.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: I am excited to announce that I have accepted a new job offer and I am looking forward to starting my new role! I am grateful for the opportunity to join this amazing team and I can't wait to get started. \n\n", "infer7b_before_item": " Write a LinkedIn post to announce that you have accepted a new job offer.\n\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n\n# Input: LinkedIn post\n# Output: LinkedIn post\n\n# Steps\n\n# 1. Create a new LinkedIn post\n# 2. Add a title\n# 3. Add a description\n# 4. Add a link to the company's website\n# 5. Add a link to the company's LinkedIn page\n# 6. Add a link to the company's Twitter page\n# 7. Add a link to the company's Facebook page\n# 8. Add a link to the company's Instagram page\n# 9. Add a link to the company's YouTube page\n# 10. Add a link to the company's Github page\n# 11. Add a link to the company's Medium page\n# 12. Add a link to the company's Slack page\n# 13. Add a link to the company's", "infer7b_after_item": "\n Output: I am thrilled to announce that I have accepted a new job offer! I am looking forward to joining the team and working on exciting projects. I am excited to use my skills and experience to make a positive impact. \n\n"} +{"id": 26, "instruction": "Make a list of desirable Skills for software engineers to add to LinkedIn.", "input": "", "infer30b_before_item": " Output: Add the skills to your LinkedIn profile.\nInput: Input: Make a list of desirable Skills for software engineers to add to LinkedIn. \n Output: Output: Add the skills to your LinkedIn profile.\nInput: Input: Make a list of desirable Skills for software engineers to add to LinkedIn. \n Output: Output: Add the skills to your LinkedIn profile.\nInput: Input: Make a list of desirable Skills for software engineers to add to LinkedIn. \n Output: Output: Add the skills to your LinkedIn profile.\nInput: Input: Make a list of desirable Skills for software engineers to add to LinkedIn. \n Output: Output: Add the skills to your LinkedIn profile.\nInput: Input: Make a list of desirable Skills for software engineers to add to LinkedIn. \n Output: Output: Add the skills to your LinkedIn profile.\nInput: Input: Make a list of desirable Skills for software engineers to add to LinkedIn. \n Output: Output: Add the skills to your LinkedIn profile.\nInput: Input: Make a list of desirable Skills for software engineers", "infer30b_after_item": "\n Output: Desirable Skills for Software Engineers to Add to LinkedIn:\n- Software Engineering\n- Programming Languages (e.g. Java, C++, Python, JavaScript)\n- Agile Methodologies\n- Cloud Computing\n- Data Structures\n- Algorithms\n- Software Design Patterns\n- Software Testing\n- Machine Learning\n- Artificial Intelligence\n- Mobile Development\n- Web Development\n- Database Management\n- Networking\n- Security\n- UX/UI Design\n- Software Architecture\n- Software Documentation\n- Debugging\n- Version Control Systems\n- Software Engineering Tools (e.g. Eclipse, Visual Studio, Git)\n- Software Engineering Processes (e.g. Waterfall, Scrum)\n- Software Engineering Best Practices (e.g. Code Review, Unit Testing)\n- Software Engineering Trends (e.g. Microservices, Containers)\n- Software Engineering Frameworks (e.g. Spring, Angular)\n- Software Engineering Certifications (e.g. AWS Certified Solutions Architect, Microsoft Certified Solutions Expert) \n\n", "infer13b_before_item": " Output: Make a list of desirable Skills for software engineers to add to LinkedIn.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: - Software development experience\n- Knowledge of programming languages\n- Ability to work in a team environment\n- Problem-solving skills\n- Knowledge of software engineering best practices\n- Knowledge of software engineering tools\n- Knowledge of software engineering methodologies\n- Knowledge of software engineering principles\n- Knowledge of software engineering processes\n- Knowledge of software engineering standards\n- Knowledge of software engineering techniques\n- Knowledge of software engineering tools\n- Knowledge of software engineering best practices\n- Knowledge of software engineering trends\n- Knowledge of software engineering technologies\n- Knowledge of software engineering principles\n- Knowledge of software engineering concepts\n- Knowledge of software engineering frameworks\n- Knowledge of software engineering architectures\n- Knowledge of software engineering methodologies\n- Knowledge of software engineering processes\n- Knowledge of software engineering practices\n- Knowledge of software engineering patterns\n- Knowledge of software engineering tools\n- Knowledge of software engineering techniques\n- Knowledge of software engineering standards\n- Knowledge of software engineering tools\n- Knowledge of software engineering tools\n- Knowledge of software engineering tools\n- Knowledge of software engineering tools\n- Knowledge of software engineering tools\n-", "infer7b_before_item": " Add the skills to LinkedIn.\n\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n\n# Input: Make a list of desirable Skills for software engineers to add to LinkedIn.\n# Output: Add the skills to LinkedIn.\n\n# Steps:\n\n# 1. Make a list of desirable Skills for software engineers to add to LinkedIn.\n# 2. Add the skills to LinkedIn.\n\n\n# Initial Solution\n\n# Don't forget to check on initialization for a empty list\n# and return nil if empty\n\n# Your Solution Below\n\n\n# 1. Make a list of desirable Skills for software engineers to add to LinkedIn.\n\n# 2. Add the skills to LinkedIn.\n\n\n# 1. Make a list of desirable Skills for software engineers to add to LinkedIn.\n\n# 2. Add the skills to LinkedIn.\n\n\n# 1. Make a list of desirable Skills for software engineers to add to LinkedIn.\n\n#", "infer7b_after_item": "\n Output: - Experience with software development tools and technologies\n- Knowledge of software engineering best practices\n- Ability to work in a team environment\n- Excellent problem-solving and analytical skills\n- Experience with software testing and quality assurance\n- Experience with software design and architecture\n- Experience with software development methodologies\n- Experience with software development lifecycle\n- Experience with software development tools and technologies\n- Experience with software development processes\n- Experience with software development frameworks\n- Experience with software development languages\n- Experience with software development platforms\n- Experience with software development tools\n- Experience with software development processes\n- Experience with software development methodologies\n- Experience with software development best practices\n- Experience with software development processes\n- Experience with software development methodologies\n- Experience with software development tools\n- Experience with software development platforms\n- Experience with software development languages\n- Experience with software development frameworks\n- Experience with software development lifecycle\n- Experience with software testing and quality assurance\n- Experience with software design and architecture\n- Experience with software development tools and technologies\n- Experience with software development"} +{"id": 27, "instruction": "You will be asked to answer the following question as part of the LinkedIn Skill assessments.", "input": "You find that your project has a tag and branch both named push-notifications, which causes confusion when trying to print out given reference. How can you specify which branch you want to look at?", "infer30b_before_item": " Output: git checkout push-notifications\n Explanation: Explanation: You can specify the branch you want to look at by using the git checkout command.\n\n */\n\npublic class _001_Branches {\n\tpublic static void main(String[] args) {\n\t\t// TODO Auto-generated method stub\n\n\t}\n\n}\n", "infer30b_after_item": " \n Output: You can specify which branch you want to look at by using the branch name in the command. For example, if you want to look at the push-notifications branch, you can use the command \"git checkout push-notifications\". This will switch the current branch to the push-notifications branch and allow you to view the files in that branch. \n\n", "infer13b_before_item": " You will be asked to answer the following question as part of the LinkedIn Skill assessments. You find that your project has a tag and branch both named push-notifications, which causes confusion when trying to print out given reference. How can you specify which branch you want to look at? \n\nInput: You find that your project has a tag and branch both named push-notifications, which causes confusion when trying to print out given reference. How can you specify which branch you want to look at?\nOutput: You find that your project has a tag and branch both named push-notifications, which causes confusion when trying to print out given reference. How can you specify which branch you want to look at?\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: You can use the branch command to specify which branch you want to look at. For example, if you want to look at the push-notifications branch, you can use the command `git branch push-notifications`. \n\n", "infer7b_before_item": " You will be asked to answer the following question as part of the LinkedIn Skill assessments. You find that your project has a tag and branch both named push-notifications, which causes confusion when trying to print out given reference. How can you specify which branch you want to look at? \n*/\n\n// 1. Define a variable to store the branch name\nvar branchName = \"push-notifications\";\n\n// 2. Define a variable to store the tag name\nvar tagName = \"push-notifications\";\n\n// 3. Define a variable to store the branch name\nvar branchName = \"push-notifications\";\n\n// 4. Define a variable to store the tag name\nvar tagName = \"push-notifications\";\n\n// 5. Define a variable to store the branch name\nvar branchName = \"push-notifications\";\n\n// 6. Define a variable to store the tag name\nvar tagName = \"push-notifications\";\n\n// 7. Define a variable to store the branch name\nvar branchName = \"push-notifications\";\n\n// 8. Define a variable to store the tag name\nvar tagName", "infer7b_after_item": " \n Output: You can specify which branch you want to look at by using the \"ref\" parameter in the print statement. For example, you can use \"ref=push-notifications\" to print out the given reference for the push-notifications branch. \n\n"} +{"id": 28, "instruction": "Take the title of the product and extract its attributes. The attributes in this case refer to the characteristics of the products, such as their brand and color. Your answer must a list following the format of \"attribute: value\".", "input": "SAMSUNG 980 PRO SSD 2TB PCIe NVMe Gen 4 Gaming M.2 Internal Solid State Hard Drive Memory Card, Maximum Speed, Thermal Control, MZ-V8P2T0B", "infer30b_before_item": " Output: Take the title of the product and extract its attributes. The attributes in this case refer to the characteristics of the products, such as their brand and color. Your answer must a list following the format of \"attribute: value\". SAMSUNG 980 PRO SSD 2TB PCIe NVMe Gen 4 Gaming M.2 Internal Solid State Hard Drive Memory Card, Maximum Speed, Thermal Control, MZ-V8P2T0B\n\nInput: Input: Take the title of the product and extract its attributes. The attributes in this case refer to the characteristics of the products, such as their brand and color. Your answer must a list following the format of \"attribute: value\". SAMSUNG 980 PRO SSD 2TB PCIe NVMe Gen 4 Gaming M.2 Internal Solid State Hard Drive Memory Card, Maximum Speed, Thermal Control, MZ-V8P2T0B\n Output: Output: Take the title of the product and extract its attributes. The attributes in this case refer to the characteristics of the products, such as their brand and color. Your answer must a list following the format", "infer30b_after_item": "/AM \n Output: Brand: SAMSUNG, Color: Black, Maximum Speed: 4,900MB/s, Thermal Control: Yes, MZ-V8P2T0B/AM: Yes. \n\n", "infer13b_before_item": " Output: Take the input and output the product name, the product attributes, and the product price.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "0E \n Output: attribute: value \n\n", "infer7b_before_item": " Output: Take the title of the product and extract its attributes. The attributes in this case refer to the characteristics of the products, such as their brand and color. Your answer must a list following the format of \"attribute: value\". SAMSUNG 980 PRO SSD 2TB PCIe NVMe Gen 4 Gaming M.2 Internal Solid State Hard Drive Memory Card, Maximum Speed, Thermal Control, MZ-V8P2T0B \n\n\"\"\"\n\nimport re\n\n\ndef extract_attributes(title):\n \"\"\"\n Extracts the attributes of a product from its title.\n\n :param title: The title of the product.\n :return: A list of attributes.\n \"\"\"\n attributes = []\n for match in re.finditer(r\"([a-zA-Z0-9]+):([a-zA-Z0-9]+)\", title):\n attributes.append(match.group(1).lower() + \":\" + match.group(2).lower())\n return attributes\n\n\ndef extract_attributes_from_file(filename):\n \"\"\"\n ", "infer7b_after_item": "W \n Output: Brand: Samsung\nColor: 980 Pro SSD 2TB PCIe NVMe Gen 4 Gaming M.2 Internal Solid State Hard Drive Memory Card\nMaximum Speed: Thermal Control\nThermal Control: MZ-V8P2T0BW \n\n"} +{"id": 29, "instruction": "You should write a Product Description that will persuade the buyer that the product is beneficial.", "input": "Flipslide Game, Electronic Handheld Game | Flip, Slide, and Match the Colors to Beat the Clock - 4 Game Modes - Multiplayer Fun", "infer30b_before_item": " Output: You should write a Product Description that will persuade the buyer that the product is beneficial. Flipslide Game, Electronic Handheld Game | Flip, Slide, and Match the Colors to Beat the Clock - 4 Game Modes - Multiplayer Fun \n */\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Flipslide Game is the perfect electronic handheld game for the whole family! With 4 game modes, you can flip, slide, and match the colors to beat the clock. Challenge your friends and family to a fun and competitive game of Flipslide. With its portable design, you can take it anywhere and play anytime. Get your Flipslide Game today and have hours of fun! \n\n", "infer13b_before_item": " Output: You should write a Product Description that will persuade the buyer that the product is beneficial. Flipslide Game, Electronic Handheld Game | Flip, Slide, and Match the Colors to Beat the Clock - 4 Game Modes - Multiplayer Fun \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " for the Whole Family \n Output: Flipslide Game is the perfect game for the whole family! With 4 game modes, you can play solo or with friends and family. The game is easy to learn and fun to play. The bright colors and simple gameplay make it a great way to pass the time and have fun. Plus, it's portable and easy to take with you anywhere. Get Flipslide Game and start having fun today! \n\n", "infer7b_before_item": " Output: You should write a Product Description that will persuade the buyer that the product is beneficial. Flipslide Game, Electronic Handheld Game | Flip, Slide, and Match the Colors to Beat the Clock - 4 Game Modes - Multiplayer Fun \n\nComment: I'm not sure what you're asking here.\n\nComment: I'm asking for a way to make the output more readable.\n\nComment: I'm not sure what you mean by \"more readable\".\n\nComment: I'm asking for a way to make the output more readable.\n\nComment: I'm not sure what you mean by \"more readable\".\n\nComment: I'm asking for a way to make the output more readable.\n\nComment: I'm not sure what you mean by \"more readable\".\n\nComment: I'm asking for a way to make the output more readable.\n\nComment: I'm not sure what you mean by \"more readable\".\n\nComment: I'm asking for a way to make the output more readable.\n\nComment: I'm not sure what you mean by \"more readable\".\n\nComment:", "infer7b_after_item": " - 1000+ Levels - 100+ Cards - 100+ Power-Ups - 100+ Achievements - 100+ Unlockables - 100+ Trophies - 100+ Challenges - 100+ Puzzles - 100+ Hidden Objects - 100+ Bonus Levels - 100+ Power-Ups - 100+ Achievements - 100+ Unlockables - 100+ Trophies - 100+ Challenges - 100+ Puzzles - 100+ Hidden Objects - 100+ Bonus Levels - 100+ Power-Ups - 100+ Achievements - 100+ Unlockables - 100+ Trophies - 100+ Challenges - 100+ Puzzles - 100+ Hidden Objects - 100+ Bonus Levels - 100+ Power-Ups - 10"} +{"id": 30, "instruction": "What other Amazon products might interest someone who visited the given product?", "input": "Zeroll Zerolon Hardcoat Anodized Commercial Ice Cream Scoop with Unique Liquid Filled Heat Conductive Handle Easy Release Made in USA, 1.5-Ounce, Black", "infer30b_before_item": " Output: What other Amazon products might interest someone who visited the given product?\nWeeks of 1/14/19 - 1/27/19:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Other Amazon products that might interest someone who visited the given product include:\n- Zeroll Zerolon Ice Cream Scoop, 1.5-Ounce, Black\n- Zeroll Zerolon Ice Cream Scoop, 2-Ounce, Black\n- Zeroll Zerolon Ice Cream Scoop, 3-Ounce, Black\n- Zeroll Zerolon Ice Cream Scoop, 4-Ounce, Black\n- Zeroll Zerolon Ice Cream Scoop, 5-Ounce, Black\n- Zeroll Zerolon Ice Cream Scoop, 6-Ounce, Black\n- Zeroll Zerolon Ice Cream Scoop, 8-Ounce, Black\n- Zeroll Zerolon Ice Cream Scoop, 10-Ounce, Black\n- Zeroll Zerolon Ice Cream Scoop, 12-Ounce, Black\n- Zeroll Zerolon Ice Cream Scoop, 1", "infer13b_before_item": " What other Amazon products might interest someone who visited the given product?\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Other Amazon products that might interest someone who visited the Zeroll Zerolon Hardcoat Anodized Commercial Ice Cream Scoop with Unique Liquid Filled Heat Conductive Handle Easy Release Made in USA, 1.5-Ounce, Black include: \n\n- Zeroll Zerolon Hardcoat Anodized Commercial Ice Cream Scoop with Unique Liquid Filled Heat Conductive Handle Easy Release Made in USA, 1.5-Ounce, Red \n- Zeroll Zerolon Hardcoat Anodized Commercial Ice Cream Scoop with Unique Liquid Filled Heat Conductive Handle Easy Release Made in USA, 1.5-Ounce, Blue \n- Zeroll Zerolon Hardcoat Anodized Commercial Ice Cream Scoop with Unique Liquid Filled Heat Conductive Handle Easy Release Made in USA, 1.5-Ounce, Green \n- Zeroll Zerolon Hardcoat Anodized Commer", "infer7b_before_item": " Output: What other Amazon products might interest someone who visited the given product? Zeroll Zerolon Hardcoat Anodized Commercial Ice Cream Scoop with Unique Liquid Filled Heat Conductive Handle Easy Release Made in USA, 1.5-Ounce, Black \n\nComment: I'm not sure what you're asking here.\n\nComment: I'm trying to get the output of the input.\n\nComment: I'm not sure what you're asking here.\n\nComment: I'm trying to get the output of the input.\n\nComment: I'm not sure what you're asking here.\n\nComment: I'm trying to get the output of the input.\n\nComment: I'm not sure what you're asking here.\n\nComment: I'm trying to get the output of the input.\n\nComment: I'm not sure what you're asking here.\n\nComment: I'm trying to get the output of the input.\n\nComment: I'm not sure what you're asking here.\n\nComment: I'm trying to get the", "infer7b_after_item": " \n Output: Amazon also offers a variety of other products that might interest someone who visited the given product. For example, they offer a variety of ice cream scoop accessories such as a scoop cover, a scoop stand, and a scoop rack. They also offer a variety of ice cream scoop sets, including a set of three scoops, a set of two scoops, and a set of four scoops. Additionally, they offer a variety of ice cream scoop storage options, such as a scoop rack, a scoop stand, and a scoop cover. \n\n"} +{"id": 31, "instruction": "Compare the given item with similar products based on its properties such as price, rating, etc. For product comparison, use a table and separate the columns with \"|\".", "input": "Item: iBayam Journal Planner Pens Colored Pens Fine Point Markers Fine Tip Drawing Pens Porous Fineliner Pen for Journaling Writing Note Taking Calendar Coloring Art Office Back to School Supplies, 18 Color", "infer30b_before_item": " Output: Compare the given item with similar products based on its properties such as price, rating, etc. For product comparison, use a table and separate the columns with \"|\". Item: iBayam Journal Planner Pens Colored Pens Fine Point Markers Fine Tip Drawing Pens Porous Fineliner Pen for Journaling Writing Note Taking Calendar Coloring Art Office Back to School Supplies, 18 Color \n Explanation: \n\n*/\n\n// 1. Write your code here\n\n// var input = \"Input: Input: Compare the given item with similar products based on its properties such as price, rating, etc. For product comparison, use a table and separate the columns with \\\"|\\\". Item: iBayam Journal Planner Pens Colored Pens Fine Point Markers Fine Tip Drawing Pens Porous Fineliner Pen for Journaling Writing Note Taking Calendar Coloring Art Office Back to School Supplies, 18 Color Output: Output: Compare the given item with similar products based on its properties such as price, rating, etc. For product comparison, use a table and separate the columns with \\\"|\\\". Item: iBay", "infer30b_after_item": " Set \n Output: | Product | Price | Rating |\n|--------|-------|--------|\n| iBayam Journal Planner Pens | $19.99 | 4.5/5 |\n| Colored Pens | $14.99 | 4.3/5 |\n| Fine Point Markers | $19.99 | 4.7/5 |\n| Fine Tip Drawing Pens | $19.99 | 4.8/5 |\n| Porous Fineliner Pen for Journaling Writing Note Taking Calendar Coloring Art Office Back to School Supplies, 18 Color Set | $19.99 | 4.9/5 | \n\n", "infer13b_before_item": " Output: Print the comparison table.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " Set \n Output: | Item | Price | Rating |\n|------|-------|--------|\n|iBayam Journal Planner Pens Colored Pens Fine Point Markers Fine Tip Drawing Pens | $19.99 | 4.5/5 |\n|Fine Tip Drawing Pens | $14.99 | 4.5/5 |\n|Fine Tip Drawing Pens | $19.99 | 4.5/5 |\n|Porous Fineliner Pen for Journaling Writing Note Taking Calendar Coloring Art Office Back to School Supplies, 18 Color Set | $19.99 | 4.5/5 |\n|Porous Fineliner Pen for Journaling Writing Note Taking Calendar Coloring Art Office Back to School Supplies, 18 Color Set | $19.99 | 4.5/5 | \n\n", "infer7b_before_item": " Output: The output is the result of the comparison. It is the result of the comparison of the given item with similar products based on its properties such as price, rating, etc. For product comparison, use a table and separate the columns with \"|\".\n\n\\begin{code}\n<table border=\"1\">\n <tr>\n <th>Item</th>\n <th>Input</th>\n <th>Output</th>\n </tr>\n <tr>\n <td>iBayam Journal Planner Pens Colored Pens Fine Point Markers Fine Tip Drawing Pens Porous Fineliner Pen for Journaling Writing Note Taking Calendar Coloring Art Office Back to School Supplies, 18 Color</td>\n <td>Compare the given item with similar products based on its properties such as price, rating, etc. For product comparison, use a table and separate the columns with \"|\".</td>\n <td>The output is the result of the comparison. It is the result of the comparison of the given item with similar products based on its properties such as price, rating, etc. For product comparison, use", "infer7b_after_item": " Set \nProduct 1: iBayam Journal Planner Pens Colored Pens Fine Point Markers Fine Tip Drawing Pens Porous Fineliner Pen for Journaling Writing Note Taking Calendar Coloring Art Office Back to School Supplies, 18 Color Set \nProduct 2: iBayam Journal Planner Pens Colored Pens Fine Point Markers Fine Tip Drawing Pens Porous Fineliner Pen for Journaling Writing Note Taking Calendar Coloring Art Office Back to School Supplies, 18 Color Set \n Output: | Product 1 | Product 2 |\n| Price | Price |\n| Rating | Rating |\n| Color Set | Color Set |\n| - | - |\n| 18 | 18 |\n| 18 | 18 | \n\n"} +{"id": 32, "instruction": "Write a review based on the given information.", "input": "- Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive", "infer30b_before_item": " Output: Write a review based on the given information. - Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive\n\nInput: Input: Write a review based on the given information. - Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive\n Output: Output: Write a review based on the given information. - Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive\n\nInput: Input: Write a review based on the given information. - Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive\n Output: Output:", "infer30b_after_item": " \n Output: I recently purchased Persil Discs Laundry Detergent Pacs and I'm so glad I did! The original scent is so fresh and clean, and it does a great job of getting my clothes clean. The pacs are also HE compatible, so I don't have to worry about damaging my washing machine. I'm definitely going to keep using this detergent! \n\n", "infer13b_before_item": " Output: Write a review based on the given information. - Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive \n\n\"\"\"\n\nimport sys\nimport os\nimport re\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\nsys.path.insert(0, os.path.abspath('.'))\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autod", "infer13b_after_item": "\n- Rating: 5 stars \n Output: I love this laundry detergent! It's gentle on my clothes and leaves them smelling fresh. It's also very affordable and lasts a long time. I highly recommend it! \n\n", "infer7b_before_item": " Write a review based on the given information.\n- Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive\n- Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive\n- Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive\n- Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive\n- Product: Persil Discs Laundry Detergent Pacs, Original Scent, High Efficiency (HE) Compatible, Laundry Soap, 62 Count\n- Sentiment: Positive\n", "infer7b_after_item": " \n Output: Persil Discs Laundry Detergent Pacs are a great way to keep your clothes clean and fresh. The Original Scent is a great scent that is not overpowering and is suitable for all types of clothing. The High Efficiency (HE) Compatible formula is great for those with sensitive skin and is also great for those with allergies. The 62 count pack is a great value and is perfect for those who do a lot of laundry. Overall, Persil Discs Laundry Detergent Pacs are a great product that is sure to keep your clothes clean and fresh. \n\n"} +{"id": 33, "instruction": "Write a short description about the given movie or series.", "input": "The Witcher (2019)", "infer30b_before_item": " Output: Write a short description about the given movie or series. The Witcher (2019) \n Output: Output: Write a short description about the given movie or series. The Witcher (2019) \n Output: Output: Write a short description about the given movie or series. The Witcher (2019) \n Output: Output: Write a short description about the given movie or series. The Witcher (2019) \n Output: Output: Write a short description about the given movie or series. The Witcher (2019) \n Output: Output: Write a short description about the given movie or series. The Witcher (2019) \n Output: Output: Write a short description about the given movie or series. The Witcher (2019) \n Output: Output: Write a short description about the given movie or series. The Witcher (2019) \n Output: Output: Write a short description about the given movie or series. The Witcher (2019) \n Output: Output: Write a short description about the given movie or series.", "infer30b_after_item": " \n Output: The Witcher is a fantasy adventure series based on the best-selling book series of the same name. It follows the story of Geralt of Rivia, a solitary monster hunter, as he struggles to find his place in a world torn apart by a fierce battle between humans and monsters. Along the way, he meets a powerful sorceress and a young princess, and together they must fight to save the world from destruction. \n\n", "infer13b_before_item": " Write a short description about the given movie or series. The Witcher (2019) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The Witcher is an American fantasy drama series based on the book series of the same name by Polish author Andrzej Sapkowski. The series follows Geralt of Rivia, a monster hunter who travels the world in search of his lost memories. Along the way, he meets a variety of characters and faces many challenges as he tries to uncover the truth behind his past. \n\n", "infer7b_before_item": " Output: Write a short description about the given movie or series. The Witcher (2019)\n\\end{code}\n\n\\begin{code}\nimport re\n\ndef get_title(title):\n title = title.lower()\n title = re.sub(r'[^a-zA-Z0-9 ]', '', title)\n title = re.sub(r'[ ]{2,}','', title)\n title = re.sub(r'[ ]{2,}','', title)\n title = re.sub(r'[ ]{2,}','', title)\n title = re.sub(r'[ ]{2,}','', title)\n title = re.sub(r'[ ]{2,}','', title)\n title = re.sub(r'[ ]{2,}','', title)\n title = re.sub(r'[ ]{2,}','', title)\n title = re.sub(r'[ ]{2,}','', title)\n title = re.sub(", "infer7b_after_item": " \n Output: The Witcher is an upcoming fantasy series based on the best-selling Witcher video game franchise. The series follows the story of Geralt of Rivia, a monster hunter who is tasked with protecting the world from evil forces. The series is set to premiere on Netflix in December 2019. \n\n"} +{"id": 34, "instruction": "Think of some series or movies that would be enjoyable for someone who liked the given series.", "input": "Squid Game", "infer30b_before_item": " Output: Think of some series or movies that would be enjoyable for someone who liked the given series. Squid Game\n\nComment: I'm voting to close this question as off-topic because it is not a programming question.\n\nComment: @JeremyThille I'm voting to close this question as off-topic because it is not a programming question.\n\nComment: @JeremyThille I'm voting to close this question as off-topic because it is not a programming question.\n\nComment: @JeremyThille I'm voting to close this question as off-topic because it is not a programming question.\n\nComment: @JeremyThille I'm voting to close this question as off-topic because it is not a programming question.\n\nComment: @JeremyThille I'm voting to close this question as off-topic because it is not a programming question.\n\nComment: @JeremyThille I'm voting to close this question as off-topic because it is not a programming question.\n\nComment: @JeremyThille I'm voting to close this question as off-topic because it is not", "infer30b_after_item": " \n Output: For someone who enjoyed Squid Game, they may also enjoy series such as The Walking Dead, Game of Thrones, Stranger Things, and Westworld. Movies that may be enjoyable include The Hunger Games, The Matrix, and The Lord of the Rings. \n\n", "infer13b_before_item": " Output: The given series and the series that would be enjoyable for someone who liked the given series.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: If you like Squid Game, you might also enjoy the following series or movies: \n- Black Mirror \n- Stranger Things \n- The Haunting of Hill House \n- The Handmaid's Tale \n- The Expanse \n- The Witcher \n- The Dark Knight Trilogy \n- The Lord of the Rings Trilogy \n- The Matrix Trilogy \n- The Hunger Games Trilogy \n- The Godfather Trilogy \n- The Dark Knight Rises \n- The Avengers \n- The Matrix \n- The Godfather \n- The Dark Knight \n- The Avengers: Endgame \n- The Matrix Reloaded \n- The Dark Knight Rises \n- The Avengers: Infinity War \n- The Matrix Revolutions \n- The Avengers: Age of Ultron \n- The Dark Knight Strikes Back \n- The Avengers: Endgame \n- The Matrix \n- The Dark Knight Strikes Back \n- The Avengers: Infinity War \n- The Dark Knight St", "infer7b_before_item": " Output: Think of some series or movies that would be enjoyable for someone who liked the given series. Squid Game \n\\end{code}\n\nComment: I'm not sure what you mean by \"the given series\".\n\nComment: @MatthewLee I mean the series that the user inputs.\n\nComment: I'm not sure what you mean by \"the given series\".\n\nComment: @MatthewLee I mean the series that the user inputs.\n\nComment: I'm not sure what you mean by \"the given series\".\n\nComment: @MatthewLee I mean the series that the user inputs.\n\nComment: I'm not sure what you mean by \"the given series\".\n\nComment: @MatthewLee I mean the series that the user inputs.\n\nComment: I'm not sure what you mean by \"the given series\".\n\nComment: @MatthewLee I mean the series that the user inputs.\n\nComment: I'm not sure what you mean by \"the given series\".\n\nComment: @MatthewLee I mean the series that the user inputs.\n", "infer7b_after_item": " \n Output: The Matrix, The Hunger Games, The Lord of the Rings, The Walking Dead, Game of Thrones, The Wire, The Godfather, The Godfather Part II, The Godfather Part III, The Godfather Part IV. \n\n"} +{"id": 35, "instruction": "Give examples of popular shows and movies in the genre.", "input": "Genre: Crime TV Shows", "infer30b_before_item": " Output: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n\nInput: Input: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n Output: Output: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n\nInput: Input: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n Output: Output: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n\nInput: Input: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n Output: Output: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n\nInput: Input: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n Output: Output: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n\nInput: Input: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n Output: Output", "infer30b_after_item": " \n Output: Popular crime TV shows include The Sopranos, Breaking Bad, The Wire, Dexter, Criminal Minds, Law and Order: SVU, and The Shield. Popular crime movies include The Godfather, Heat, The Silence of the Lambs, Pulp Fiction, The Departed, and No Country for Old Men. \n\n", "infer13b_before_item": " Output: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Popular crime TV shows include Law & Order, CSI, NCIS, and The Wire. \n\n", "infer7b_before_item": " Output: Give examples of popular shows and movies in the genre. Genre: Crime TV Shows \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Popular crime TV shows include Law & Order, NCIS, CSI, The Wire, and The Shield. Popular crime movies include The Godfather, The Departed, Heat, The Usual Suspects, and The Godfather Part II. \n\n"} +{"id": 36, "instruction": "In what genres does the given movie or series fall?", "input": "Manifest", "infer30b_before_item": " Output: In what genres does the given movie or series fall? Manifest \n Steps: \n 1. Read the input from the console\n 2. Store the input in a string\n 3. Split the string into an array\n 4. Store the array in a variable\n 5. Print the array\n 6. Print the array in a table\n 7. Print the array in a table with a border\n 8. Print the array in a table with a border and a title\n 9. Print the array in a table with a border and a title and a subtitle\n10. Print the array in a table with a border and a title and a subtitle and a description\n11. Print the array in a table with a border and a title and a subtitle and a description and a picture\n12. Print the array in a table with a border and a title and a subtitle and a description and a picture and a link\n13. Print the array in a table with a border and a title and a subtitle and a description and a picture and a link and a rating\n14. Print the array in a table with a border and a title and a", "infer30b_after_item": " \n Output: Manifest is a supernatural drama series. \n\n", "infer13b_before_item": " Output: In what genres does the given movie or series fall?\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n The\n The The The The The The The", "infer13b_after_item": " \n Output: Manifest is a science fiction drama series. \n\n", "infer7b_before_item": " Output: What is the genre of the given movie or series?\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput", "infer7b_after_item": " \n Output: The TV series Manifest is a mystery-drama-thriller. \n\n"} +{"id": 37, "instruction": "Give a brief description of the given category of movies and shows.", "input": "Period Dramas", "infer30b_before_item": " Output: Give a list of movies and shows that belong to the given category.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Period dramas are movies and shows set in the past, typically in a historical setting. They often feature lavish costumes and settings, and explore themes of love, class, and power. \n\n", "infer13b_before_item": " Output: Give a brief description of the given category of movies and shows. Period Dramas \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Period dramas are films and television shows that are set in a particular time period, often in the past. They often focus on the lives of characters in that time period, and often explore themes of love, family, and social issues. \n\n", "infer7b_before_item": " Output: Give a brief description of the given category of movies and shows. Period Dramas\n\nComment: I'm not sure what you're asking. What is the input? What is the output? What is the code? What is the error?\n\nComment: I'm not sure what you're asking. What is the input? What is the output? What is the code? What is the error?\n\nComment: I'm not sure what you're asking. What is the input? What is the output? What is the code? What is the error?\n\nComment: I'm not sure what you're asking. What is the input? What is the output? What is the code? What is the error?\n\nComment: I'm not sure what you're asking. What is the input? What is the output? What is the code? What is the error?\n\nComment: I'm not sure what you're asking. What is the input? What is the output? What is the code? What is the error?\n\n", "infer7b_after_item": " \n Output: Period dramas are movies and shows that take place in a specific time period, usually in the past. They often focus on the lives of people in that time period, and often feature historical events and settings. \n\n"} +{"id": 38, "instruction": "Provide an example of how a table of contents can be generated automatically in a LATEX document. Make sure your example contains the necessary commands.", "input": "", "infer30b_before_item": " Output: Provide an example of how a table of contents can be generated automatically in a LATEX document. Make sure your example contains the necessary commands.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": "\n Output: \\tableofcontents \n\n", "infer13b_before_item": " Output: Provide an example of how a table of contents can be generated automatically in a LATEX document. Make sure your example contains the necessary commands.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: The following LATEX code can be used to generate a table of contents automatically: \n\n\\documentclass{article}\n\\usepackage{tocbasic}\n\\begin{document}\n\\tableofcontents\n\\section{Section 1}\n\\subsection{Subsection 1}\n\\subsection{Subsection 2}\n\\section{Section 2}\n\\subsection{Subsection 1}\n\\subsection{Subsection 2}\n\\end{document} \n\n", "infer7b_before_item": " Output: Provide a table of contents for the following document.\n\n\\begin{document}\n\n\\section{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{Introduction}\n\n\\subsection{", "infer7b_after_item": "\n Output: \\begin{document}\n\\tableofcontents\n\\section{Introduction}\n\\subsection{Background}\n\\subsubsection{History}\n\\subsubsubsection{Early History}\n\\subsubsubsection{Modern History}\n\\subsection{Methods}\n\\subsubsection{Data Collection}\n\\subsubsubsection{Surveys}\n\\subsubsubsection{Interviews}\n\\subsubsection{Data Analysis}\n\\subsubsubsection{Statistical Analysis}\n\\subsubsubsection{Qualitative Analysis}\n\\subsection{Results}\n\\subsubsection{Quantitative Results}\n\\subsubsubsection{Descriptive Statistics}\n\\subsubsubsection{Correlation Analysis}\n\\subsubsection{Qualitative Results}\n\\subsubsubsection{Thematic Analysis}\n\\subsection{Discussion}\n\\subsubsection{Early History}\n\\subsubsubsection{Modern History}\n\\subsubsection{Data Analysis}\n\\subsubsubsection{Statistical Analysis}\n\\subsubsubsection{Qualitative Analysis}\n\\subsection{Conclusion}\n\\subsubsection{Early History}\n\\subsubsubsection{Modern History}\n\\subsubsection{Data Analysis}\n\\subsubsubsection{Statistical Analysis}\n\\subsubsubsection{Qualitative"} +{"id": 39, "instruction": "Imagine you have a FEN notation of a chessboard. How can you draw a board to show this state of the chessboard in LATEX? Create a LATEX code that includes all the required commands and packages.", "input": "", "infer30b_before_item": " Output: A LATEX code that draws a chessboard.\n\nComment: Welcome to TeX.SX! You can have a look at [our starter guide](http://meta.tex.stackexchange.com/q/1436) to familiarize yourself further with our format.\n\nAnswer: You can use the `chessfss` package.\n\n\\begin{code}\n\\documentclass{article}\n\\usepackage{chessfss}\n\\begin{document}\n\\chessboard[\n setfen=rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1,\n showmover=false,\n borderline={false},\n showmover=false,\n boardfontsize=1.5em,\n showmover=false,\n showcaptures=false,\n showmover=false,\n shownotation=false,\n showcaptures=false,\n showmover=false,\n shownotation=false,\n setfen=rnb", "infer30b_after_item": "\n Output: \\documentclass{article}\n\\usepackage{chessfss}\n\\begin{document}\n\\chessboard[\n setfen=rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 15,\n showmates=false,\n bigframe=true,\n color=lightseafoam,\n showmover=false,\n men=black,\n setdepth=3,\n showcaptures=false,\n shownotation=false,\n showlegalmoves=false]\n\\end{document} \n\n", "infer13b_before_item": " Output: Imagine you have a FEN notation of a chessboard. How can you draw a board to show this state of the chessboard in LATEX? Create a LATEX code that includes all the required commands and packages.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: \\documentclass{article}\n\\usepackage{chessboard}\n\\begin{document}\n\\chessboard{8}{8}\n\\end{document} \n\n", "infer7b_before_item": " Output: Imagine you have a FEN notation of a chessboard. How can you draw a board to show this state of the chessboard in LATEX? Create a LATEX code that includes all the required commands and packages.\n\n\\begin{code}\n\\documentclass{article}\n\\usepackage{tikz}\n\\usetikzlibrary{positioning,calc}\n\\begin{document}\n\\begin{tikzpicture}[scale=1.5]\n\\draw[black] (0,0) rectangle (1,1);\n\\draw[black] (1,0) rectangle (2,1);\n\\draw[black] (2,0) rectangle (3,1);\n\\draw[black] (3,0) rectangle (4,1);\n\\draw[black] (4,0) rectangle (5,1);\n\\draw[black] (5,0) rectangle (6,1);\n\\draw[black] (6,0) rectangle (7,1);\n\\draw[black] (7,0) rectangle (8,1);\n\\draw[black] (8,0) rectangle (9,1);\n\\draw[", "infer7b_after_item": "\n Output: \\documentclass[12pt]{article}\n\\usepackage{tikz}\n\\usepackage{xcolor}\n\\usepackage{pgfplots}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}\n\\usepackage{pgfplotstable}"} +{"id": 40, "instruction": "What is the output of the given LATEX code?", "input": "\\documentclass{article}\n\\begin{document}\nFirst document. This is a simple example, with no \nextra parameters or packages included.\n\\end{document}", "infer30b_before_item": " Output: First document. This is a simple example, with no extra parameters or packages included.\n\nInput: Input: What is the output of the given LATEX code? \\documentclass{article}\n\\begin{document}\nSecond document. This is a simple example, with no \nextra parameters or packages included.\n\\end{document} \n Output: Output: Second document. This is a simple example, with no extra parameters or packages included.\n\nInput: Input: What is the output of the given LATEX code? \\documentclass{article}\n\\begin{document}\nThird document. This is a simple example, with no \nextra parameters or packages included.\n\\end{document} \n Output: Output: Third document. This is a simple example, with no extra parameters or packages included.\n\nInput: Input: What is the output of the given LATEX code? \\documentclass{article}\n\\begin{document}\nFourth document. This is a simple example, with no \nextra parameters or packages included.\n\\end{document} \n Output: Output: Fourth document. This is a simple example, with no extra parameters or packages included", "infer30b_after_item": " \n Output: The output of the given LATEX code is a simple document with the text \"First document. This is a simple example, with no extra parameters or packages included.\" \n\n", "infer13b_before_item": " Output: What is the output of the given LATEX code? \\documentclass{article}\n\\begin{document}\nFirst document. This is a simple example, with no \nextra parameters or packages included.\n\\end{document} \n\nInput: Input: What is the output of the given LATEX code? \\documentclass{article}\n\\begin{document}\nFirst document. This is a simple example, with no \nextra parameters or packages included.\n\\end{document} \n Output: Output: What is the output of the given LATEX code? \\documentclass{article}\n\\begin{document}\nFirst document. This is a simple example, with no \nextra parameters or packages included.\n\\end{document} \n\nInput: Input: What is the output of the given LATEX code? \\documentclass{article}\n\\begin{document}\nFirst document. This is a simple example, with no \nextra parameters or packages included.\n\\end{document} \n Output: Output: What is the output of the given LATEX code? \\documentclass{article}\n\\begin{document}\nFirst document. This is a simple example", "infer13b_after_item": " \n Output: First document. This is a simple example, with no extra parameters or packages included. \n\n", "infer7b_before_item": " \\begin{document}\nFirst document. This is a simple example, with no \nextra parameters or packages included.\n\\end{document}\n\\end{code}\n\nComment: I'm not sure what you mean by \"the output of the given LATEX code\". Do you mean the output of the code you've given?\n\nComment: @egreg Yes, I mean the output of the code I've given.\n\nComment: @egreg I've edited the question to make it more clear.\n\nComment: @egreg I've edited the question to make it more clear.\n\nComment: @egreg I've edited the question to make it more clear.\n\nComment: @egreg I've edited the question to make it more clear.\n\nComment: @egreg I've edited the question to make it more clear.\n\nComment: @egreg I've edited the question to make it more clear.\n\nComment: @egreg I've edited the question to make it more clear.\n\nComment: @egreg I've edited the question to make it more clear.\n\nComment: @egreg I'", "infer7b_after_item": " \n Output: This is a simple example, with no extra parameters or packages included. \n\n"} +{"id": 41, "instruction": "You should find a few LATEX templates appropriate for the task at hand and list the links to them.", "input": "Homework Assignment", "infer30b_before_item": " Output: You should find a few LATEX templates appropriate for the task at hand and list the links to them. Homework Assignment \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Here are a few LATEX templates that you can use for your homework assignment: \n\n1. https://www.overleaf.com/latex/templates/latex-template-for-homework-assignments/jjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj", "infer13b_before_item": " Output: You should find a few LATEX templates appropriate for the task at hand and list the links to them. Homework Assignment \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Here are a few LATEX templates appropriate for homework assignments:\n\n1. https://www.overleaf.com/learn/latex/Homework%20Assignment\n2. https://www.overleaf.com/learn/latex/Homework%20Assignment%20Template\n3. https://www.overleaf.com/learn/latex/Homework%20Assignment%20Template%202\n4. https://www.overleaf.com/learn/latex/Homework%20Assignment%20Template%203 \n\n", "infer7b_before_item": " You should write a short report on the results of your work.\n\n\\begin{document}\n\n\\begin{center}\n\\begin{tabular}{ |c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|", "infer7b_after_item": " \n Output: - [LATEX Template for Homework Assignment](https://www.latextemplates.com/template/latex-template-for-homework-assignment)\n- [LATEX Template for Homework Assignment](https://www.latextemplates.com/template/latex-template-for-homework-assignment)\n- [LATEX Template for Homework Assignment](https://www.latextemplates.com/template/latex-template-for-homework-assignment) \n\n"} +{"id": 42, "instruction": "Write a simple guide for uploading the bibliography database\n on Overleaf.", "input": "", "infer30b_before_item": " Output: A simple guide for uploading the bibliography database on Overleaf.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": "\n Output: To upload the bibliography database on Overleaf, follow these steps: \n1. Log in to your Overleaf account. \n2. Click on the \"Add Files\" button in the top-left corner of the screen. \n3. Select the bibliography database file from your computer and click \"Open\". \n4. The bibliography database will be uploaded to your Overleaf project. \n5. Click \"Save Changes\" to save the changes to your project. \n\n", "infer13b_before_item": " Output: Write a simple guide for uploading the bibliography database\n on Overleaf.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: Step 1: Open Overleaf and create a new project.\nStep 2: Click on the Bibliography tab and select the bibliography style you want to use.\nStep 3: Add the references to the bibliography by clicking on the \"Add Reference\" button.\nStep 4: Edit the references as needed and click on the \"Save\" button.\nStep 5: Click on the \"Generate Bibliography\" button to generate the bibliography.\nStep 6: Click on the \"Download\" button to download the bibliography. \n\n", "infer7b_before_item": " Output: Write a simple guide for uploading the bibliography database\n on Overleaf.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "\n Output: 1. Log in to Overleaf.\n2. Click on the \"Bibliography\" tab.\n3. Click on the \"Add Bibliography\" button.\n4. Select the type of bibliography you want to add (e.g. APA, MLA, Chicago, etc.).\n5. Enter the bibliography details (e.g. author, title, year, etc.).\n6. Click on the \"Save\" button.\n7. Your bibliography will be added to your document. \n\n"} +{"id": 43, "instruction": "Choose Spotify playlists that match the given mood. Provide a link to these playlists for easy access.", "input": "Feeling Good", "infer30b_before_item": " Output: Choose Spotify playlists that match the given mood. Provide a link to these playlists for easy access. Feeling Good \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ", "infer30b_after_item": " \n Output: Spotify Playlists: \n- Chillhop Essentials: https://open.spotify.com/playlist/5KaTfPsUuWVlcqsVJ5Y1uX\n- Relaxing Piano: https://open.spotify.com/playlist/5KaTfPsUuWVlcqsVJ5Y1uX\n- Acoustic Covers: https://open.spotify.com/playlist/5KaTfPsUuWVlcqsVJ5Y1uX\n- Chill Out Beats: https://open.spotify.com/playlist/5KaTfPsUuWVlcqsVJ5Y1uX \n\n", "infer13b_before_item": " Output: A playlist that matches the given mood.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Here are two Spotify playlists that match the mood of \"Feeling Good\":\n\n1. Uplifting Hits: https://open.spotify.com/playlist/0VX1X1X1X1X1X1X1X1X1X?si=X1X1X1X1X1X1X1X1X1X\n\n2. Feel Good Hits: https://open.spotify.com/playlist/0VX1X1X1X1X1X1X1X1X1?si=X1X1X1X1X1X1X1X1X1 \n\n", "infer7b_before_item": " Output: A playlist of songs that match the given mood.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: https://open.spotify.com/user/spotify/playlist/61X4444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444"} +{"id": 44, "instruction": "Curate a Spotify playlist based on the vibe. Publish this playlist as a song list.", "input": "Vibe: coding on weekend", "infer30b_before_item": " Output: Playlist of songs.\n\n 1. Create a new playlist.\n 2. Add songs to the playlist.\n 3. Publish the playlist.\n\n 1. Create a new playlist.\n 2. Add songs to the playlist.\n 3. Publish the playlist.\n\n 1. Create a new playlist.\n 2. Add songs to the playlist.\n 3. Publish the playlist.\n\n 1. Create a new playlist.\n 2. Add songs to the playlist.\n 3. Publish the playlist.\n\n 1. Create a new playlist.\n 2. Add songs to the playlist.\n 3. Publish the playlist.\n\n 1. Create a new playlist.\n 2. Add songs to the playlist.\n 3. Publish the playlist.\n\n 1. Create a new playlist.\n 2. Add songs to the playlist.\n 3. Publish the playlist.\n\n 1. Create a new playlist.\n 2. Add songs to the playlist.\n 3. Publish the playlist.\n", "infer30b_after_item": " nights \n Output: Spotify Playlist: \n1. \"Coding on a Sunday Night\" by The Internet\n2. \"Coding in the Cave\" by Kodak Black\n3. \"Coding in the Dark\" by The Weeknd\n4. \"Coding in the Night\" by Kanye West\n5. \"Coding in the Rain\" by Drake\n6. \"Coding in the Clouds\" by Kendrick Lamar\n7. \"Coding in the Moonlight\" by Eminem\n8. \"Coding in the Stars\" by Beyonce\n9. \"Coding in the Sun\" by Rihanna\n10. \"Coding in the Wind\" by Jay-Z \n\n", "infer13b_before_item": " Output: A Spotify playlist.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: 1. \"Coding on the Weekend\" by The Fratellis\n2. \"Code Monkey\" by The Dave Matthews Band\n3. \"Coding in the Cloud\" by The Flaming Lips\n4. \"Code Monkey\" by Jonathan Coulton\n5. \"Code Monkey\" by The Bens\n6. \"Coding in the Cloud\" by The Flaming Lips\n7. \"Code Monkey\" by Jonathan Coulton\n8. \"Code Monkey\" by The Bens\n9. \"Coding in the Cloud\" by The Flaming Lips\n10. \"Code Monkey\" by Jonathan Coulton \n\n", "infer7b_before_item": " Output: A playlist of songs that are similar to the vibe.\n\n\"\"\"\n\nimport os\nimport sys\nimport random\nimport time\nimport requests\nimport json\nimport re\nimport urllib\nimport urllib2\nimport urlparse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib.request\nimport urllib.error\nimport urllib.parse\nimport urllib", "infer7b_after_item": " nights \n Output: 1. \"Coding\" by The Glitch Mob\n2. \"Coding\" by The Glitch Mob\n3. \"Coding\" by The Glitch Mob\n4. \"Coding\" by The Glitch Mob\n5. \"Coding\" by The Glitch Mob\n6. \"Coding\" by The Glitch Mob\n7. \"Coding\" by The Glitch Mob\n8. \"Coding\" by The Glitch Mob\n9. \"Coding\" by The Glitch Mob\n10. \"Coding\" by The Glitch Mob\n11. \"Coding\" by The Glitch Mob\n12. \"Coding\" by The Glitch Mob\n13. \"Coding\" by The Glitch Mob\n14. \"Coding\" by The Glitch Mob\n15. \"Coding\" by The Glitch Mob\n16. \"Coding\" by The Glitch Mob\n17. \"Coding\" by The Glitch Mob\n18. \"Coding\" by The Glitch Mob\n19. \"Coding"} +{"id": 45, "instruction": "How would you describe the meaning of this lyrics? Describe some of the metaphors.", "input": "My lover\u2019s got humor\nShe\u2019s the giggle at a funeral\nKnows everybody\u2019s disapproval\nI should\u2019ve worshipped her sooner", "infer30b_before_item": " Output: What is the meaning of this song? What is the message of this song?\n\nComment: I'm voting to close this question as off-topic because it is not about programming.\n\nComment: @JonathanM I'm voting to close this question as off-topic because it is not about programming.\n\nComment: @JonathanM I'm voting to close this question as off-topic because it is not about programming.\n\nComment: @JonathanM I'm voting to close this question as off-topic because it is not about programming.\n\nComment: @JonathanM I'm voting to close this question as off-topic because it is not about programming.\n\nComment: @JonathanM I'm voting to close this question as off-topic because it is not about programming.\n\nComment: @JonathanM I'm voting to close this question as off-topic because it is not about programming.\n\nComment: @JonathanM I'm voting to close this question as off-topic because it is not about programming.\n\nComment: @JonathanM I'm voting to close this question as off", "infer30b_after_item": " \n Output: The lyrics describe a lover who is able to bring light and humor to a somber situation. The metaphor of a \"giggle at a funeral\" implies that the lover is able to make a sad situation more bearable. The line \"knows everybody's disapproval\" suggests that the lover is wise and perceptive, and the line \"I should've worshipped her sooner\" implies that the speaker regrets not appreciating the lover sooner. \n\n", "infer13b_before_item": " Output: What is the meaning of this lyrics? Describe some of the metaphors. My lover\u2019s got humor\nShe\u2019s the giggle at a funeral\nKnows everybody\u2019s disapproval\nI should\u2019ve worshipped her sooner \n\n\"\"\"\n\nimport re\nimport sys\n\n\ndef main():\n input_file = open(sys.argv[1], 'r')\n output_file = open(sys.argv[2], 'w')\n\n for line in input_file:\n line = line.strip()\n if line:\n output_file.write(line)\n\n input_file.close()\n output_file.close()\n\n\nif __name__ == '__main__':\n main()\n", "infer13b_after_item": " \n Output: The lyrics are describing a lover who is able to bring lightness and joy to a somber situation, despite the disapproval of others. The metaphors of \"giggle at a funeral\" and \"worshipped her sooner\" emphasize the importance of appreciating the people who bring joy to our lives. \n\n", "infer7b_before_item": " Output: How would you describe the meaning of this lyrics? Describe some of the metaphors. My lover\u2019s got humor\nShe\u2019s the giggle at a funeral \nKnows everybody\u2019s disapproval\nI should\u2019ve worshipped her sooner\nShe\u2019s the giggle at a funeral \nKnows everybody\u2019s disapproval\nI should\u2019ve worshipped her sooner \nShe\u2019s the giggle at a funeral \nKnows everybody\u2019s disapproval\nI should\u2019ve worshipped her sooner \nShe\u2019s the giggle at a funeral \nKnows everybody\u2019s disapproval\nI should\u2019ve worshipped her sooner \nShe\u2019s the giggle at a funeral \nKnows everybody\u2019s disapproval\nI should\u2019ve worshipped her sooner \nShe\u2019s the giggle at a funeral \nKnows everybody\u2019s disapproval\nI should\u2019ve worshipped her sooner \nShe\u2019s the giggle at a funeral \nKnows everybody\u2019s disapproval\nI should\u2019ve worshipped", "infer7b_after_item": "\nShe\u2019s the one who\u2019s got the power \n Output: This song is about a person who has found a special someone who is able to make them laugh even in the most difficult situations. The person is also able to handle any disapproval or criticism with ease. The lyrics also suggest that this person has a lot of power and influence, which is why the narrator should have worshipped them sooner. \n\n"} +{"id": 46, "instruction": "Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works.", "input": "", "infer30b_before_item": " Output: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works.\nInput: Input: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works. \n Output: Output: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works.\nInput: Input: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works. \n Output: Output: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works.\nInput: Input: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works. \n Output: Output: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works.\nInput: Input: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works. \n Output: Output: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works.\nInput: Input: Suggest some Audiobooks for First", "infer30b_after_item": "\n Output: For first-time listeners, I would suggest some popular audiobooks such as The Hunger Games by Suzanne Collins, Harry Potter and the Sorcerer's Stone by J.K. Rowling, The Great Gatsby by F. Scott Fitzgerald, 1984 by George Orwell, and The Catcher in the Rye by J.D. Salinger. These books are popular and have been adapted into audiobooks, making them perfect for first-time listeners. \n\n", "infer13b_before_item": " Output: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works.\n\n#include <bits/stdc++.h>\n\nusing namespace std;\n\nint main() {\n int t;\n cin >> t;\n while (t--) {\n int n;\n cin >> n;\n vector<int> v(n);\n for (int i = 0; i < n; i++) {\n cin >> v[i];\n }\n sort(v.begin(), v.end());\n int ans = 0;\n for (int i = 0; i < n; i++) {\n if (v[i] == 1) {\n ans++;\n }\n }\n cout << ans << endl;\n }\n return 0;\n}\n", "infer13b_after_item": "\n Output: For first-time listeners, I recommend the following popular audiobooks: 1984 by George Orwell, The Great Gatsby by F. Scott Fitzgerald, To Kill a Mockingbird by Harper Lee, The Catcher in the Rye by J.D. Salinger, The Lord of the Rings by J.R.R. Tolkien, The Alchemist by Paulo Coelho, and The Hobbit by J.R.R. Tolkien. \n\n", "infer7b_before_item": " Output: Suggest some Audiobooks for First-Time Listeners. You can attract them by choosing popular works.\n\nComment: I'm not sure what you mean by \"output\". Do you mean the output of the program? Or the output of the program to the user? Or the output of the program to the user in the form of a list of audiobooks?\n\nComment: I mean the output of the program to the user in the form of a list of audiobooks.\n\nAnswer: \\begin{code}\nimport requests\n\nurl = 'https://www.audible.com/search/ref=sr_adv_b_search_1_1?searchString=audiobooks&advsearchKeywords=audiobooks&searchType=Keyword&qid=1571151191&sr=1-1&sprefix=audiobooks%2Caps%2C151&field-keywords=audiobooks&field-subgenre=1&field-genre=1&field-price=1&field-duration=1&field-rating=1&field-format=1&field-isbn=1", "infer7b_after_item": "\n Output: For first-time listeners, some popular audiobooks include The Great Gatsby by F. Scott Fitzgerald, The Catcher in the Rye by J.D. Salinger, The Handmaid's Tale by Margaret Atwood, and The Great Gatsby by F. Scott Fitzgerald. \n\n"} +{"id": 47, "instruction": "Make a list of the most popular podcasts.", "input": "", "infer30b_before_item": " Output: Make a list of the most popular podcasts.\nInput: Input: Make a list of the most popular podcasts. \n Output: Output: Make a list of the most popular podcasts.\nInput: Input: Make a list of the most popular podcasts. \n Output: Output: Make a list of the most popular podcasts.\nInput: Input: Make a list of the most popular podcasts. \n Output: Output: Make a list of the most popular podcasts.\nInput: Input: Make a list of the most popular podcasts. \n Output: Output: Make a list of the most popular podcasts.\nInput: Input: Make a list of the most popular podcasts. \n Output: Output: Make a list of the most popular podcasts.\nInput: Input: Make a list of the most popular podcasts. \n Output: Output: Make a list of the most popular podcasts.\nInput: Input: Make a list of the most popular podcasts. \n Output: Output: Make a list of the most popular podcasts.\nInput: Input: Make a list of the most popular", "infer30b_after_item": "\n Output: The most popular podcasts include Serial, This American Life, The Joe Rogan Experience, Stuff You Should Know, The Tim Ferriss Show, Freakonomics Radio, Hidden Brain, TED Talks, and The NPR Politics Podcast. \n\n", "infer13b_before_item": " Output: Make a list of the most popular podcasts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n The\n\n", "infer13b_after_item": "\n Output: The most popular podcasts include: \n1. Serial \n2. The Joe Rogan Experience \n3. The Daily \n4. The New York Times' The Daily \n5. The Tim Ferriss Show \n6. The Moth Radio Hour \n7. The NPR Politics Podcast \n8. The Ringer's The Rewatchables \n9. The Ben Shapiro Show \n10. The Dave Ramsey Show \n\n", "infer7b_before_item": " Make a list of the most popular podcasts.\nThe input is a list of podcasts. The output is a list of podcasts.\n\n# Complete the following function, which returns the most popular podcasts.\n#\n# def most_popular_podcasts(podcasts)\n# podcasts.sort_by { |podcast| podcast.downloads }\n# podcasts.first(10)\n# end\n\n# 1. What is a local variable?\n# A local variable is a variable that is only accessible within a method.\n# 2. What is a method?\n# A method is a block of code that can be called from anywhere in the program.\n# 3. What does it mean for one method to \"return\" another method?\n# It means that the method that is returned is called when the method that is returned is called.\n# 4. What does the return statement do?\n# The return statement ends the method that is being called.\n# 5. What is the difference between a local variable and a method?\n# A local variable is only accessible within a method. A method", "infer7b_after_item": "\n Output: The most popular podcasts include: \n- The Joe Rogan Experience \n- The Tim Ferriss Show \n- The Daily \n- The Moth \n- The TED Radio Hour \n- The Nerdist \n- The Hidden Brain \n- The Mental Illness Happy Hour \n- The Infinite Monkey Cage \n- The Mothership \n- The Dollop \n- The Doughboys \n- The Nerdist \n- The Moth \n- The Joe Rogan Experience \n- The Tim Ferriss Show \n- The Daily \n- The Moth \n- The TED Radio Hour \n- The Nerdist \n- The Hidden Brain \n- The Mental Illness Happy Hour \n- The Infinite Monkey Cage \n- The Mothership \n- The Dollop \n- The Doughboys \n- The Nerdist \n- The Moth \n- The Joe Rogan Experience \n- The Tim Ferriss Show \n- The Daily \n- The Moth \n-"} +{"id": 48, "instruction": "You should take the time to read the situation and problem carefully and give the best advice you can.", "input": "I'm roadtripping down New Zealand with a friend and he is driving me insane. He hasn't enjoyed all the tours we've been on and doesn't want to chat about ANYTHING. We're a week into our trip and this is the quietest I've ever been when hanging out with another person as he rebuffs all my attempts at conversation. He wears his airpods in the car and around the accommodation - not that I've kept trying to talk to him. Honestly, he is bringing some seriously bad vibes to everything and it's ruining my trip.\nWe don't have anything booked in yet for the South Island, so I'm planning to ditch him in Wellington at the ferry. Probably book my own car in Picton and just relax alone for the rest of the trip. (Just thinking about it is such sharp relief that it makes me feel emotional. I'll pay whatever price I have to just to get away from him.)\nObviously telling him he is ruining my trip with his negative energy isn't the best way to approach this.\nAny advice for the best approach so we can split amicably? Or at least not have an argument that ends with very hurt feelings?", "infer30b_before_item": "\nYou're right, telling him he's ruining your trip isn't the best way to approach this.\nI think you should tell him that you're feeling a bit lonely and that you'd like to spend more time together.\nIf he doesn't want to talk, you could try to find other ways to spend time together.\nYou could go for a walk, or watch a movie together.\nIf he still doesn't want to talk, you could try to find other ways to spend time together.\nYou could go for a walk, or watch a movie together.\nIf he still doesn't want to talk, you could try to find other ways to spend time together.\nYou could go for a walk, or watch a movie together.\nIf he still doesn't want to talk, you could try to find other ways to spend time together.\nYou could go for a walk, or watch a movie together.\nIf he still doesn't want to talk, you could try to find other ways to spend time together.\nYou could go for a walk, or watch a movie together.\nIf he still doesn't want to talk, you could try to find other ways to", "infer30b_after_item": " \n Output: It sounds like your friend is having a difficult time on the trip and is trying to cope by shutting out the world. It might be helpful to have a conversation with him about what he is feeling and why he is not enjoying the trip. You could also suggest activities that he might enjoy, such as hiking or visiting a museum. If he is still not interested in talking, you could suggest taking some time apart to do your own thing. This could help him to relax and give you some space to do what you want. \n\nIf you do decide to part ways, it is important to be respectful and understanding. You could explain that you are feeling overwhelmed and need some time to yourself, and that you would like to part ways amicably. You could also offer to help him find a ride back to Wellington. \n\n", "infer13b_before_item": " You should take the time to read the situation and problem carefully and give the best advice you can. I'm roadtripping down New Zealand with a friend and he is driving me insane. He hasn't enjoyed all the tours we've been on and doesn't want to chat about ANYTHING. We're a week into our trip and this is the quietest I've ever been when hanging out with another person as he rebuffs all my attempts at conversation. He wears his airpods in the car and around the accommodation - not that I've kept trying to talk to him. Honestly, he is bringing some seriously bad vibes to everything and it's ruining my trip.\nWe don't have anything booked in yet for the South Island, so I'm planning to ditch him in Wellington at the ferry. Probably book my own car in Picton and just relax alone for the rest of the trip. (Just thinking about it is such sharp relief that it makes me feel emotional. I'll pay whatever price I have to just to get away from him.)\nObviously telling him he is ruining my trip with his negative", "infer13b_after_item": " \n Output: It sounds like you are both feeling very frustrated and that this trip has not been what you both expected. It is important to communicate your feelings and needs to each other in a respectful way. You could start by expressing how you have been feeling and how you would like to make the rest of the trip more enjoyable for both of you. You could also suggest that you take some time apart to do your own activities and then come back together to share your experiences. This could help to create a more positive atmosphere between you. \n\n", "infer7b_before_item": " I'm roadtripping down New Zealand with a friend and he is driving me insane. He hasn't enjoyed all the tours we've been on and doesn't want to chat about ANYTHING. We're a week into our trip and this is the quietest I've ever been when hanging out with another person as he rebuffs all my attempts at conversation. He wears his airpods in the car and around the accommodation - not that I've kept trying to talk to him. Honestly, he is bringing some seriously bad vibes to everything and it's ruining my trip.\nWe don't have anything booked in yet for the South Island, so I'm planning to ditch him in Wellington at the ferry. Probably book my own car in Picton and just relax alone for the rest of the trip. (Just thinking about it is such sharp relief that it makes me feel emotional. I'll pay whatever price I have to just to get away from him.)\nObviously telling him he is ruining my trip with his negative energy isn't the best way to approach this.\nAny advice for the best approach so we", "infer7b_after_item": " \n Output: It sounds like you are in a difficult situation. It's understandable that you want to end the trip on a positive note, but it's important to be mindful of your friend's feelings. It may be helpful to try to understand why he is acting this way and what he is going through. You could try to have a conversation with him and ask him what he is feeling and what he needs from the trip. If he is open to it, you could suggest some activities that you both can enjoy together. If he is not open to it, you could suggest that you take some time to yourself and then reconnect when you are both feeling more relaxed. \n\n"} +{"id": 49, "instruction": "You are given a description that provides a set of facts or a scenario. It is up to you to craft a story from these facts and scenarios. The missing pieces must be filled in with imaginative but logical information.", "input": "Ten European football teams \u2013 the Netherlands, England, Belgium, Denmark, France, Germany, Norway, Sweden, Switzerland and Wales \u2013 will participate in a season-long \u201cOneLove\u201d campaign promoting inclusion and opposing discrimination.", "infer30b_before_item": " Output: You are given a description that provides a set of facts or a scenario. It is up to you to craft a story from these facts and scenarios. The missing pieces must be filled in with imaginative but logical information. Ten European football teams \u2013 the Netherlands, England, Belgium, Denmark, France, Germany, Norway, Sweden, Switzerland and Wales \u2013 will participate in a season-long \u201cOneLove\u201d campaign promoting inclusion and opposing discrimination. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ", "infer30b_after_item": " \n Output: The OneLove campaign was launched by the ten European football teams in an effort to promote inclusion and combat discrimination. The teams, representing the Netherlands, England, Belgium, Denmark, France, Germany, Norway, Sweden, Switzerland and Wales, were united in their mission to spread a message of love and acceptance. \n\nThe campaign kicked off with a series of friendly matches between the teams, with each team donating a portion of the proceeds to charities that support inclusion and diversity. The matches were followed by a series of public service announcements, featuring the players from each team, that highlighted the importance of inclusion and the dangers of discrimination. \n\nThe teams also organized a series of community events, such as youth soccer clinics and educational workshops, to help spread the message of inclusion and acceptance. The season-long campaign was a huge success, with the teams raising thousands of dollars for charities and spreading the message of love and acceptance to millions of people around the world. \n\n", "infer13b_before_item": " You are expected to write a story that is based on the input.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The OneLove campaign was a huge success, bringing together the ten European football teams \u2013 the Netherlands, England, Belgium, Denmark, France, Germany, Norway, Sweden, Switzerland and Wales \u2013 to spread the message of inclusion and oppose discrimination. The teams worked together to create a series of events and activities that were open to the public, and the campaign was a huge success. The teams were able to reach out to a wide range of people and spread their message of love and acceptance. \n\n", "infer7b_before_item": " You are given a description that provides a set of facts or a scenario. It is up to you to craft a story from these facts and scenarios. The missing pieces must be filled in with imaginative but logical information. Ten European football teams \u2013 the Netherlands, England, Belgium, Denmark, France, Germany, Norway, Sweden, Switzerland and Wales \u2013 will participate in a season-long \u201cOneLove\u201d campaign promoting inclusion and opposing discrimination. \n\n### 2019\n\nInput: You are given a description that provides a set of facts or a scenario. It is up to you to craft a story from these facts and scenarios. The missing pieces must be filled in with imaginative but logical information. The 2019 Rugby World Cup will be held in Japan from 20 September to 2 November. \n Output: You are given a description that provides a set of facts or a scenario. It is up to you to craft a story from these facts and scenarios. The missing pieces must be filled in with imaginative but logical information. The 2019 Rugby World Cup will be held in Japan from 20 September to 2 November. \n\n", "infer7b_after_item": " \n Output: The OneLove campaign is a season-long initiative that aims to promote inclusion and oppose discrimination. Ten European football teams \u2013 the Netherlands, England, Belgium, Denmark, France, Germany, Norway, Sweden, Switzerland and Wales \u2013 have come together to show their support for the campaign. The teams have been working together to create a series of events and activities that will be held throughout the season. These events will include a series of football matches, a charity match, a social media campaign, and a series of educational workshops. The teams will also be working together to create a documentary that will be released at the end of the season. The documentary will highlight the importance of inclusion and the impact of discrimination. \n\n"} +{"id": 50, "instruction": "Based on the given query, suggest some related search queries.", "input": "learning french", "infer30b_before_item": " Output: Based on the given query, suggest some related search queries. learning french \n Explanation: Output: Based on the given query, suggest some related search queries. learning french \n\n */\n\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <string.h>\n#include <ctype.h>\n#include <math.h>\n#include <float.h>\n#include <algorithm.h>\n#include <vector>\n#include <map>\n#include <queue>\n#include <stack>\n#include <deque>\n#include <set>\n#include <sstream>\n\n#define MAX_AS 50000\n#define MAX_ANSWERS 10000000\n\nusing namespace std;\n\nint main() {\n int t;\n scanf(\"%d\", &t);\n while(t--) {\n int n;\n scanf(\"%d\", &n);\n char *s = (char *)malloc(sizeof(char)*n);\n", "infer30b_after_item": " \n Output: - French language courses \n- French language apps \n- French language books \n- French language podcasts \n- French language immersion programs \n- French language classes \n- French language resources \n- French language tutors \n- French language exams \n- French language tests \n- French language pronunciation \n- French language grammar \n- French language vocabulary \n- French language slang \n- French language idioms \n- French language translation \n- French language accents \n- French language dialects \n- French language writing \n- French language speaking \n- French language listening \n- French language reading \n- French language conversation \n- French language culture \n- French language history \n- French language movies \n- French language music \n- French language art \n- French language cuisine \n- French language travel \n- French language fashion \n- French language news \n- French language technology \n- French language business \n- French language science \n- French language engineering \n- French language medicine \n- French language law \n- French language finance", "infer13b_before_item": " Output: Based on the given query, suggest some related search queries. learning french \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: - How to learn French quickly?\n- French language learning apps\n- French language learning resources\n- French language learning books\n- French language learning podcasts\n- French language learning websites\n- French language learning videos\n- French language learning software\n- French language learning games\n- French language learning exercises\n- French language learning tips\n- French language learning tools\n- French language learning materials\n- French language learning tools for kids\n- French language learning for beginners\n- French language learning for adults\n- French language learning for free\n- French language learning for beginners online\n- French language learning for beginners pdf\n- French language learning for beginners online free\n- French language learning for beginners online free pdf\n- French language learning for beginners online free ebook\n- French language learning for beginners online free audio\n- French language learning for beginners online free audiobook\n- French language learning for beginners online free audiobook pdf\n- French language learning for beginners online free audiobook pdf ebook\n- French language learning for beginners online free audiobook pdf ebook pdf\n- French language learning for beginners online free audiobook pdf ebook pdf ebook\n", "infer7b_before_item": " Output: Based on the given query, suggest some related search queries. learning french \n\n\"\"\"\n\nimport re\nimport string\nimport random\nimport sys\nimport time\nimport os\nimport json\nimport urllib\nimport urllib2\nimport urlparse\nimport urllib2\nimport urlparse\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2\nimport urllib2", "infer7b_after_item": " \n Output: - how to learn french\n- french language courses\n- french grammar\n- french vocabulary\n- french pronunciation\n- french conversation\n- french grammar rules\n- french language resources\n- french language books\n- french language apps\n- french language courses online\n- french language classes\n- french language courses for beginners\n- french language courses for kids\n- french language courses for adults\n- french language courses for professionals\n- french language courses for business\n- french language courses for travel\n- french language courses for immigrants\n- french language courses for beginners\n- french language courses for intermediate\n- french language courses for advanced\n- french language courses for kids\n- french language courses for adults\n- french language courses for professionals\n- french language courses for business\n- french language courses for travel\n- french language courses for immigrants\n- french language courses for beginners\n- french language courses for intermediate\n- french language courses for advanced\n- french language courses for kids\n- french"} +{"id": 51, "instruction": "Please suggest a few papers to consider based on the search term given. The names of the papers should be listed.", "input": "Topic: scaling law + machine learning", "infer30b_before_item": " Output: Please suggest a few papers to consider based on the search term given. The names of the papers should be listed. Topic: scaling law + machine learning \n\nInput: Input: Please suggest a few papers to consider based on the search term given. The names of the papers should be listed. Topic: scaling law + machine learning \n Output: Output: Please suggest a few papers to consider based on the search term given. The names of the papers should be listed. Topic: scaling law + machine learning \n\nInput: Input: Please suggest a few papers to consider based on the search term given. The names of the papers should be listed. Topic: scaling law + machine learning \n Output: Output: Please suggest a few papers to consider based on the search term given. The names of the papers should be listed. Topic: scaling law + machine learning \n\nInput: Input: Please suggest a few papers to consider based on the search term given. The names of the papers should be listed. Topic: scaling law + machine learning \n Output: Output: Please suggest a few papers to consider based on the search term given. The names of the papers should be listed. Topic: scaling law +", "infer30b_after_item": " \n Output: 1. \"Scaling Up Deep Learning: From Laptops to Supercomputers\" by Martin J. Strohm et al. \n2. \"Scaling Up Deep Learning: From Laptops to Supercomputers\" by Martin J. Strohm et al. \n3. \"Scaling Up Deep Learning: From Laptops to Supercomputers\" by Martin J. Strohm et al. \n4. \"Scaling Up Deep Learning: From Laptops to Supercomputers\" by Martin J. Strohm et al. \n5. \"Scaling Up Deep Learning: From Laptops to Supercomputers\" by Martin J. Strohm et al. \n\n", "infer13b_before_item": " Output: The paper list is returned.\n\n\"\"\"\n\nimport os\nimport sys\nimport re\nimport time\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport matplotlib.dates as mdates\nimport datetime\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as dt\nimport datetime as", "infer13b_after_item": " \n Output: 1. \"Scaling Laws in Machine Learning: A Review and Perspective\", by A. K. Jain, A. K. Jain, and A. K. Jain.\n2. \"Scaling Laws in Machine Learning: A Review and Perspective\", by A. K. Jain, A. K. Jain, and A. K. Jain.\n3. \"Scaling Laws in Machine Learning: A Review and Perspective\", by A. K. Jain, A. K. Jain, and A. K. Jain. \n\n", "infer7b_before_item": " Please suggest a few papers to consider based on the search term given. The names of the papers should be listed. Topic: scaling law + machine learning \n\"\"\"\n\nimport re\nimport sys\nimport os\nimport glob\nimport argparse\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as m", "infer7b_after_item": " \n Output: 1. Scaling Laws in Machine Learning: A Survey \n2. Scaling Laws in Machine Learning: A Survey \n3. Scaling Laws in Machine Learning: A Survey \n4. Scaling Laws in Machine Learning: A Survey \n5. Scaling Laws in Machine Learning: A Survey \n\n"} +{"id": 52, "instruction": "Prepare an email signature template for the given person or company.", "input": "a graduate student of computer science", "infer30b_before_item": " Output: Prepare an email signature template for the given person or company.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Hi, \n\nMy name is [Name], and I am a graduate student of computer science. I am passionate about technology and its potential to make the world a better place.\n\nI am currently working on [project], and I am always looking for new opportunities to learn and grow.\n\nIf you have any questions or would like to connect, please feel free to reach out to me at [email].\n\nThanks,\n[Name] \n\n", "infer13b_before_item": " Output: Prepare an email signature template for the given person or company.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Dear [Name],\n\nThis is [Your Name], a graduate student of computer science. I am currently working on my thesis and I am looking forward to sharing my research with you.\n\nThank you for your time and consideration.\n\nSincerely,\n[Your Name] \n\n", "infer7b_before_item": " Output: Prepare an email signature template for the given person or company.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Dear [Name],\n\nThank you for your interest in [Company Name].\n\nWe are pleased to introduce our new graduate student of computer science, [Name].\n\n[Name] is an enthusiastic and dedicated student who is passionate about learning and exploring the world of computer science.\n\nWe are confident that [Name] will be a great asset to our team and we look forward to seeing them grow and succeed.\n\nThank you for your time and consideration.\n\nSincerely,\n[Company Name] \n\n"} +{"id": 53, "instruction": "React properly to reviews from your customers. In your responses, you can highlight your business' policies and customer service.", "input": "Another repeat visit and no disappointment from us. Great place for breakfast or brunch - and you won't need to eat again for awhile. The restaurant is close to the Tenderloin so be aware of your surroundings.\nWe enjoyed the Succotash Omelet (at least my wife enjoyed it), the Pulled Pork Benedict and the Hangtown Fry. Portions are large and pricing is fair. I wish we could have managed a Beignet Flight!\nDefinitely a spot to seek out. It can be crowed at times so be patient. Well worth any wait.", "infer30b_before_item": " Output: Respond to reviews in a timely manner. If you don't have time to respond to reviews, you can hire a company to do it for you.\nInput: Input: Respond to reviews in a timely manner. If you don't have time to respond to reviews, you can hire a company to do it for you.\nOutput: Output: Respond to reviews in a timely manner. If you don't have time to respond to reviews, you can hire a company to do it for you.\nInput: Input: Respond to reviews in a timely manner. If you don't have time to respond to reviews, you can hire a company to do it for you.\nOutput: Output: Respond to reviews in a timely manner. If you don't have time to respond to reviews, you can hire a company to do it for you.\nInput: Input: Respond to reviews in a timely manner. If you don't have time to respond to reviews, you can hire a company to do it for you.\nOutput: Output: Respond to reviews in a timely manner. If you don't have time to respond to", "infer30b_after_item": " \n Output: Dear customer,\n\nThank you for your review! We are glad to hear that you and your wife enjoyed your meals. We strive to provide our customers with the best possible experience and we are glad to hear that you had a great time. We will definitely keep your feedback in mind and make sure to keep up the good work.\n\nWe hope to see you again soon!\n\nSincerely,\nThe Staff at The Eggery \n\n", "infer13b_before_item": " Output: Respond to reviews from your customers. In your responses, you can highlight your business' policies and customer service.\n\\end{code}\n\nComment: I'm not sure what you're asking. Are you asking how to get the text between the tags?\n\nComment: I'm asking how to get the text between the tags and then how to get the text between the tags and the next tag.\n\nComment: I'm asking how to get the text between the tags and then how to get the text between the tags and the next tag.\n\nComment: I'm asking how to get the text between the tags and then how to get the text between the tags and the next tag.\n\nComment: I'm asking how to get the text between the tags and then how to get the text between the tags and the next tag.\n\nComment: I'm asking how to get the text between the tags and then how to get the text between the tags and the next tag.\n\nComment: I'm asking how to get the text between the tags and then how to get the text between the tags and the next tag.\n\nComment: I'm asking how to get", "infer13b_after_item": " \n Output: Thank you for your kind words! We are so glad you enjoyed your meal and we appreciate your feedback. We are always striving to provide the best customer service and experience, so we are glad to hear that you had a great time. We hope to see you again soon! \n\n", "infer7b_before_item": " Output: React properly to reviews from your customers. In your responses, you can highlight your business' policies and customer service. Another repeat visit and no disappointment from us. Great place for breakfast or brunch - and you won't need to eat again for awhile. The restaurant is close to the Tenderloin so be aware of your surroundings.\nWe enjoyed the Succotash Omelet (at least my wife enjoyed it), the Pulled Pork Benedict and the Hangtown Fry. Portions are large and pricing is fair. I wish we could have managed a Beignet Flight!\nDefinitely a spot to seek out. It can be crowed at times so be patient. Well worth any wait. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Thank you for taking the time to share your experience with us. We are glad to hear that you enjoyed your meal and that our portions were large and pricing was fair. We appreciate your feedback and will take it into consideration for future improvements. We hope to see you again soon! \n\n"} +{"id": 54, "instruction": "Write a good Twitter Bio. Try to cover all the provided main points in your generated bio.", "input": "Name: Jane\nMain points:\n- Architect\n- Interested in Tech\nWritten in first person\nUse casual tone", "infer30b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: I'm Jane, an architect with a passion for tech. I love exploring new ideas and finding creative solutions to problems. \n\n", "infer13b_before_item": " Output: Write a good Twitter Bio. Try to cover all the provided main points in your generated bio. Name: Jane\nMain points:\n- Architect\n- Interested in Tech\nWritten in first person\nUse casual tone \n\"\"\"\n\nimport random\n\ndef generate_bio(name, main_points):\n bio = \"\"\n for point in main_points:\n bio += \"{} {} \".format(point, random.choice([\"is\", \"loves\", \"is interested in\", \"is passionate about\", \"is a fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan of\", \"is a big fan", "infer13b_after_item": " \n Output: Hi, I'm Jane and I'm an architect who's passionate about technology. I'm always looking for new ways to use tech to create innovative designs and solutions. I'm also a big fan of exploring new cities and cultures. \n\n", "infer7b_before_item": "\n- Architect\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- Writer\n- Interested in Tech\n- W", "infer7b_after_item": " \n Output: I'm Jane, an architect who's passionate about technology. I'm always looking for new ways to use tech to make my designs better. #Architecture #Tech #Design \n\n"} +{"id": 55, "instruction": "We need to write a thematic tweet to share company news and facts. Please take a careful look at the facts and details provided and create a tweet based on them.", "input": "- news: Applications for summer internships are open now\n- Start with a thematic tie-in to Halloween", "infer30b_before_item": " Output: We need to write a thematic tweet to share company news and facts. Please take a careful look at the facts and details provided and create a tweet based on them. - news: Applications for summer internships are open now\n- Start with a thematic tie-in to Halloween \n Expected result: Expected result: We need to write a thematic tweet to share company news and facts. Please take a careful look at the facts and details provided and create a tweet based on them. - news: Applications for summer internships are open now\n- Start with a thematic tie-in to Halloween \n Actual result: Actual result: We need to write a thematic tweet to share company news and facts. Please take a careful look at the facts and details provided and create a tweet based on them. - news: Applications for summer internships are open now\n- Start with a thematic tie-in to Halloween \n\\end{code}\n\nComment: I'm not sure what you're asking. What is the expected output? What is the actual output? What is the difference?\n\nComment: I'", "infer30b_after_item": " \n Output: Scare away the competition and apply for our summer internships now! #Halloween #Internships #CareerOpportunities \n\n", "infer13b_before_item": " Output: We need to write a thematic tweet to share company news and facts. Please take a careful look at the facts and details provided and create a tweet based on them. - news: Applications for summer internships are open now\n - Start with a thematic tie-in to Halloween \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n\n", "infer7b_before_item": " Output: We need to write a thematic tweet to share company news and facts. Please take a careful look at the facts and details provided and create a tweet based on them. - news: Applications for summer internships are open now\n- Use a hashtag that is relevant to the company and the news\n- Use a hashtag that is relevant to the company and the news - Use a hashtag that is relevant to the company and the news\n- Use a hashtag that is relevant to the company and the news - Use a hashtag that is relevant to the company and the news - Use a hashtag that is relevant to the company and the news\n- Use a hashtag that is relevant to the company and the news - Use a hashtag that is relevant to the company and the news - Use a hashtag that is relevant to the company and the news - Use a hashtag that is relevant to the company and the news\n- Use a hashtag that is relevant to the company and the news - Use a hashtag that is relevant to the company and the news - Use a hashtag that is relevant to the company and the news - Use a hashtag", "infer7b_after_item": "\n- Facts: 100% of applicants will receive a response within 24 hours\n- Details: Applications are open until November 1st \n Output: Spooky summer internships are here! Apply now and get a response within 24 hours! #Halloween #SummerInternships #Responsive \n\n"} +{"id": 56, "instruction": "A job description is a document that clearly states the requirements, duties, responsibilities, and skills required to perform a specific role. You need to write a job description based on the given information. You should describe responsibilities and duties in your job description in a logical order. Remember that potential employees often get their first impression of your company from your job description. So, the job description should convey the attractiveness of your company.", "input": "Company: [Company name]\nJob: Software Engineer\nJob Responsibilities:\nReceive and perform code reviews with other engineers.\nWrite unit, integration, and end-to-end tests to verify functionality using automated testing frameworks such as Pytest.\nWork collaboratively with fellow software engineers to build features requested by business stakeholders.\nParticipate in Agile teams to develop, test, and debug complex data processing pipelines and data analysis applications using big data processing systems such as Apache Spark.\nDiagnose, debug, and perform root cause analysis of issues and defects.\nSkills and experience that will lead to success:\nExperience with a modern software programming language. We use Python, and Pyspark extensively.\nExperience with cloud systems like AWS or Azure\nExperience writing SQL queries \nStrong written and verbal communication skills\nEagerness to work in a highly-collaborative environment\nSome visualization experience such as Tableau or PowerBI is helpful", "infer30b_before_item": "\nInput: Input: A job description is a document that clearly states the requirements, duties, responsibilities, and skills required to perform a specific role. You need to write a job description based on the given information. You should describe responsibilities and duties in your job description in a logical order. Remember that potential employees often get their first impression of your company from your job description. So, the job description should convey the attractiveness of your company. Company: [Company name]\nJob: Software Engineer\nJob Responsibilities:\nReceive and perform code reviews with other engineers.\nWrite unit, integration, and end-to-end tests to verify functionality using automated testing frameworks such as Pytest.\nWork collaboratively with fellow software engineers to build features requested by business stakeholders.\nParticipate in Agile teams to develop, test, and debug complex data processing pipelines and data analysis applications using big data processing systems such as Apache Spark.\nDiagnose, debug, and perform root cause analysis of issues and defects.\nSkills and experience that will lead to success:\nExperience with a modern software programming language. We use Python, and Pyspark extensively.\n", "infer30b_after_item": ". \n Output: [Company name] is looking for a Software Engineer to join our team. The successful candidate will be responsible for receiving and performing code reviews with other engineers, writing unit, integration, and end-to-end tests to verify functionality using automated testing frameworks such as Pytest, working collaboratively with fellow software engineers to build features requested by business stakeholders, participating in Agile teams to develop, test, and debug complex data processing pipelines and data analysis applications using big data processing systems such as Apache Spark, diagnosing, debugging, and performing root cause analysis of issues and defects.\n\nThe ideal candidate should have experience with a modern software programming language. We use Python, and Pyspark extensively. Experience with cloud systems like AWS or Azure is also required. Strong written and verbal communication skills are essential, as well as eagerness to work in a highly-collaborative environment. Some visualization experience such as Tableau or PowerBI is helpful. \n\n", "infer13b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": ". \n\n", "infer7b_before_item": " Your job description should be at least 100 words long.\nInput: A job description is a document that clearly states the requirements, duties, responsibilities, and skills required to perform a specific role. You need to write a job description based on the given information. You should describe responsibilities and duties in your job description in a logical order. Remember that potential employees often get their first impression of your company from your job description. So, the job description should convey the attractiveness of your company. Company: [Company name]\nJob Responsibilities: Receive and perform code reviews with other engineers. Write unit, integration, and end-to-end tests to verify functionality using automated testing frameworks such as Pytest. Work collaboratively with fellow software engineers to build features requested by business stakeholders. Participate in Agile teams to develop, test, and debug complex data processing pipelines and data analysis applications using big data processing systems such as Apache Spark. Diagnose, debug, and perform root cause analysis of issues and defects. Skills and experience that will lead to success: Experience with a modern software programming language. We use Python, and Pyspark extensively. Experience", "infer7b_after_item": ". \n Output: Job Description:\nSoftware Engineer\n[Company name]\n\nResponsibilities:\n\u2022 Receive and perform code reviews with other engineers.\n\u2022 Write unit, integration, and end-to-end tests to verify functionality using automated testing frameworks such as Pytest.\n\u2022 Work collaboratively with fellow software engineers to build features requested by business stakeholders.\n\u2022 Participate in Agile teams to develop, test, and debug complex data processing pipelines and data analysis applications using big data processing systems such as Apache Spark.\n\u2022 Diagnose, debug, and perform root cause analysis of issues and defects.\n\nSkills and Experience:\n\u2022 Experience with a modern software programming language. We use Python, and Pyspark extensively.\n\u2022 Experience with cloud systems like AWS or Azure.\n\u2022 Experience writing SQL queries.\n\u2022 Strong written and verbal communication skills.\n\u2022 Eagerness to work in a highly-collaborative environment.\n\u2022 Some visualization experience such as Tableau or PowerBI is helpful. \n\n"} +{"id": 57, "instruction": "Considering the reason for the rejection, write a polite rejection letter for rejecting a candidate.", "input": "Reason: You came in second", "infer30b_before_item": "\n\nInput: Input: Considering the reason for the rejection, write a polite rejection letter for rejecting a candidate. Reason: You came in second \n Output:\n\nInput: Input: Considering the reason for the rejection, write a polite rejection letter for rejecting a candidate. Reason: You came in second \n Output:\n\nInput: Input: Considering the reason for the rejection, write a polite rejection letter for rejecting a candidate. Reason: You came in second \n Output:\n\nInput: Input: Considering the reason for the rejection, write a polite rejection letter for rejecting a candidate. Reason: You came in second \n Output:\n\nInput: Input: Considering the reason for the rejection, write a polite rejection letter for rejecting a candidate. Reason: You came in second \n Output:\n\nInput: Input: Considering the reason for the rejection, write a polite rejection letter for rejecting a candidate. Reason: You came in second \n Output:\n\nInput: Input: Considering the reason for the rejection, write a polite rejection letter", "infer30b_after_item": " for the position. \n Output: Dear [Candidate Name],\n\nThank you for taking the time to apply for the [Position Name] position at [Company Name]. We appreciate your interest and the effort you put into the application process. \n\nUnfortunately, we have decided to offer the position to another candidate. We were impressed with your qualifications and experience, and you came in second for the position. \n\nWe wish you the best of luck in your future endeavors and thank you again for your interest in [Company Name]. \n\nSincerely,\n[Your Name] \n\n", "infer13b_before_item": " Output: A polite rejection letter for rejecting a candidate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " place. \n Output: Dear [Candidate Name],\n\nWe appreciate your interest in the position at [Company Name]. We regret to inform you that you have not been selected for the position.\n\nWe were impressed by your qualifications and experience, but unfortunately, another candidate was selected for the position. We wish you the best of luck in your future endeavors.\n\nSincerely,\n[Company Name] \n\n", "infer7b_before_item": " Output: Considering the reason for the rejection, write a polite rejection letter for rejecting a candidate. Reason: You came in second \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " in the interview. \n Output: Dear [Candidate],\n\nThank you for your interest in our position. We appreciate your time and effort in applying for the job. We have carefully reviewed your application and unfortunately, we have decided to move forward with another candidate.\n\nWe wish you the best of luck in your future endeavors.\n\nSincerely,\n[Name] \n\n"} +{"id": 58, "instruction": "We've already decided on the title and topic for our blog post. I would appreciate it if you wrote an introduction paragraph to attract the readers' attention.", "input": "Blog title: Healthy Apple Muffins\nBlog Topic: recipe and tips for cooking a good apple muffin", "infer30b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Are you looking for a delicious and healthy treat to satisfy your sweet tooth? Look no further than our recipe for healthy apple muffins! These muffins are packed with flavor and nutrition, making them the perfect snack for any occasion. In this blog post, we'll share our recipe and tips for cooking a perfect apple muffin. \n\n", "infer13b_before_item": " Output: We've already decided on the title and topic for our blog post. I would appreciate it if you wrote an introduction paragraph to attract the readers' attention. Blog title: Healthy Apple Muffins\nBlog Topic: recipe and tips for cooking a good apple muffin \n\nimport java.util.*;\nimport java.io.*;\n\npublic class Main {\n\n\tpublic static void main(String[] args) throws IOException {\n\t\t// TODO Auto-generated method stub\n\t\tScanner sc = new Scanner(System.in);\n\t\tSystem.out.println(\"Input: Input: We've already decided on the title and topic for our blog post. I would appreciate it if you wrote an introduction paragraph to attract the readers' attention. Blog title: Healthy Apple Muffins\");\n\t\tString input = sc.nextLine();\n\t\tSystem.out.println(\"Blog title: Healthy Apple Muffins\");\n\t\tString blogTitle = sc.nextLine();\n\t\tSystem.out.println(\"Blog Topic: recipe and tips for cooking a good apple muffin \");\n\t\tString blogTopic", "infer13b_after_item": " \n Output: Are you looking for a delicious and healthy way to start your day? Look no further than these healthy apple muffins! With a few simple ingredients and a few easy steps, you can have a delicious and nutritious breakfast ready in no time. \n\n", "infer7b_before_item": " I would like to see a recipe for a healthy apple muffin and a few tips on how to make a good apple muffin.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it", "infer7b_after_item": " \n Output: Are you looking for a healthy and delicious way to start your day? Look no further! In this blog post, we will be sharing our recipe and tips for cooking a good apple muffin. \n\nApple muffins are a great way to start your day, as they are packed with nutrients and are easy to make. They are also a great way to use up any leftover apples you may have lying around. \n\nThe recipe is simple and can be easily customized to your taste. All you need is a few basic ingredients such as flour, sugar, baking powder, eggs, butter, and apples. Once you have all the ingredients, you can start by mixing the dry ingredients together and then adding the wet ingredients. Once the batter is mixed, you can pour it into a muffin tin and bake for 20 minutes. \n\nThe tips for cooking a good apple muffin are also important. First, make sure to use fresh apples. If you are using leftover apples, make sure to wash them thoroughly and remove any bruises"} +{"id": 59, "instruction": "Design an outline for a blog post based on the given information and list the sections accordingly.", "input": "This blog post is going to about making a list of last-minute gift ideas. Also, the tone of the post is going to be relaxed and casual.", "infer30b_before_item": " Output: Design a blog post based on the given information and list the sections accordingly. This blog post is going to be about making a list of last-minute gift ideas. Also, the tone of the post is going to be relaxed and casual.\n\nInput: Input: Design an outline for a blog post based on the given information and list the sections accordingly. This blog post is going to be about making a list of last-minute gift ideas. Also, the tone of the post is going to be relaxed and casual. \n Output: Output: Design a blog post based on the given information and list the sections accordingly. This blog post is going to be about making a list of last-minute gift ideas. Also, the tone of the post is going to be relaxed and casual.\n\nInput: Input: Design an outline for a blog post based on the given information and list the sections accordingly. This blog post is going to be about making a list of last-minute gift ideas. Also, the tone of the post is going to be relaxed and casual. \n Output: Output: Design a blog post based on the given information and list the sections accordingly. This blog post is going", "infer30b_after_item": " \n Output: I. Introduction \nA. Introduction to the topic of last-minute gift ideas \nB. Overview of the post \n\nII. Tips for Last-Minute Gift Ideas \nA. Consider the recipient's interests \nB. Think outside the box \nC. Look for unique items \n\nIII. Examples of Last-Minute Gift Ideas \nA. Gift cards \nB. Experiences \nC. Handmade items \n\nIV. Conclusion \nA. Summary of the post \nB. Call to action \n\n", "infer13b_before_item": " Output: Write a blog post based on the given outline.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: I. Introduction \nA. Introduction to the topic \nB. Why this topic is important \n\nII. Last-Minute Gift Ideas \nA. Gift cards \nB. Experiences \nC. Handmade gifts \nD. Gadgets \n\nIII. Conclusion \nA. Summary of last-minute gift ideas \nB. Final thoughts \n\n", "infer7b_before_item": " Output: Write a blog post based on the outline. \n# 1. \nLast-Minute Gift Ideas\n**1.** **Last-Minute Gift Ideas**\n**2.** **Last-Minute Gift Ideas**\n**3.** **Last-Minute Gift Ideas**\n**4.** **Last-Minute Gift Ideas**\n**5.** **Last-Minute Gift Ideas**\n**6.** **Last-Minute Gift Ideas**\n**7.** **Last-Minute Gift Ideas**\n**8.** **Last-Minute Gift Ideas**\n**9.** **Last-Minute Gift Ideas**\n**10.** **Last-Minute Gift Ideas**\n**11.** **Last-Minute Gift Ideas**\n**12.** **Last-Minute Gift Ideas**\n**13.** **Last-Minute Gift Ideas**\n**14.** **Last-Minute Gift Ideas**\n**15.** **Last-Minute Gift Ide", "infer7b_after_item": " \n Output: I. Introduction \nA. Introduce the topic of last-minute gift ideas \nB. Set the tone of the post \n\nII. Last-Minute Gift Ideas \nA. Gift cards \nB. Books \nC. Jewelry \n\nIII. Conclusion \nA. Summarize the post \nB. Offer a few more ideas \n\n"} +{"id": 60, "instruction": "Write a section for a blog post and try to cover all of the provided information about this section in your text.", "input": "Blog Topic: 7 Fall Fashion Trends Worth Taking on Your Trip\nSection Title: Trusty Jeans\nMain point of the section: jeans can be worn all year and look good with everything .", "infer30b_before_item": "\nInput: Input: Write a section for a blog post and try to cover all of the provided information about this section in your text. Blog Topic: 7 Fall Fashion Trends Worth Taking on Your Trip\nSection Title: Trusty Jeans\nMain point of the section: jeans can be worn all year and look good with everything. \n Output:\nInput: Input: Write a section for a blog post and try to cover all of the provided information about this section in your text. Blog Topic: 7 Fall Fashion Trends Worth Taking on Your Trip\nSection Title: Trusty Jeans\nMain point of the section: jeans can be worn all year and look good with everything. \n Output:\nInput: Input: Write a section for a blog post and try to cover all of the provided information about this section in your text. Blog Topic: 7 Fall Fashion Trends Worth Taking on Your Trip\nSection Title: Trusty Jeans\nMain point of the section: jeans can be worn all year and look good with everything. \n Output:\nInput: Input: Write a section for a blog post and try to cover", "infer30b_after_item": " \n Output: Jeans are a timeless fashion staple that can be worn all year round. They are versatile and can be styled in a variety of ways. For a casual look, pair your jeans with a t-shirt and sneakers. For a more sophisticated look, try pairing your jeans with a blouse and heels. Jeans are also great for traveling as they are comfortable and can be worn with almost any outfit. They are also great for layering, so you can easily add a jacket or sweater to your look. Jeans are a must-have for any traveler and can be worn with almost anything. \n\n", "infer13b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Jeans are a classic staple in any wardrobe, and they\u2019re especially great for traveling. They\u2019re comfortable, versatile, and can be dressed up or down depending on the occasion. Plus, they\u2019re a great way to stay warm in the cooler months. \n\nFor fall, jeans are a must-have item. They\u2019re a great way to stay stylish and comfortable while traveling. Jeans can be worn with a variety of tops, sweaters, and jackets, so you can easily mix and match to create different looks. Plus, they\u2019re a great way to stay warm in the cooler months. \n\nJeans are a great option for traveling because they\u2019re lightweight, easy to pack, and can be worn with a variety of items. They\u2019re also a great way to stay warm in the cooler months. Plus, they\u2019re a classic staple in any wardrobe, so you can easily mix and match to create different looks. \n\nJeans are a great option for traveling because they\u2019re lightweight, easy to pack, and can be worn with a variety of items.", "infer7b_before_item": "\n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * ", "infer7b_after_item": "\nSub-points:\n- Jeans are a classic and timeless piece of clothing that can be worn with any outfit.\n- They can be dressed up or down depending on the occasion.\n- They are a great choice for traveling because they are comfortable and versatile. \n Output: Jeans are a classic and timeless piece of clothing that can be worn with any outfit. They can be dressed up or down depending on the occasion. They are a great choice for traveling because they are comfortable and versatile. Jeans can be worn all year and look good with everything. They are a staple in any wardrobe and can be worn with any outfit. \n\n"} +{"id": 61, "instruction": "Create a detailed caption for an Instagram post about a discount. A proper caption should explain the product, offer the discount, and tell the user how to claim it.", "input": "Product: Golang course\nProduct description: teaching the foundational skills for building cloud services, web applications, DevOps tools, and backend development\nDiscount: 50%\nHow to claim: Use the coupon code: GOLANG", "infer30b_before_item": "\nInput: Input: Create a detailed caption for an Instagram post about a discount. A proper caption should explain the product, offer the discount, and tell the user how to claim it. Product: Golang course\nProduct description: teaching the foundational skills for building cloud services, web applications, DevOps tools, and backend development\nDiscount: 50%\nHow to claim: Use the coupon code: GOLANG \n Output:\nInput: Input: Create a detailed caption for an Instagram post about a discount. A proper caption should explain the product, offer the discount, and tell the user how to claim it. Product: Golang course\nProduct description: teaching the foundational skills for building cloud services, web applications, DevOps tools, and backend development\nDiscount: 50%\nHow to claim: Use the coupon code: GOLANG \n Output:\nInput: Input: Create a detailed caption for an Instagram post about a discount. A proper caption should explain the product, offer the discount, and tell the user how to claim it. Product: Golang course\nProduct description: teaching the foundational skills for", "infer30b_after_item": "50 \n Output: Learn the foundational skills for building cloud services, web applications, DevOps tools, and backend development with our Golang course! Get 50% off when you use the coupon code: GOLANG50. \n\n", "infer13b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "50 \n Output: Are you looking to learn the foundational skills for building cloud services, web applications, DevOps tools, and backend development? Then look no further! We're offering a 50% discount on our Golang course, so you can learn the fundamentals of this powerful programming language. Simply use the coupon code: GOLANG50 to claim your discount. \n\n", "infer7b_before_item": " A post with a caption and a link to the course\n\n\"\"\"\n\n\ndef create_post(input):\n \"\"\"\n Create a post with a caption and a link to the course\n \"\"\"\n caption = input['caption']\n link = input['link']\n return {\n 'caption': caption,\n 'link': link\n }\n\n\ndef create_post_from_input(input):\n \"\"\"\n Create a post with a caption and a link to the course\n \"\"\"\n caption = input['caption']\n link = input['link']\n return {\n 'caption': caption,\n 'link': link\n }\n\n\ndef create_post_from_input_with_discount(input):\n \"\"\"\n Create a post with a caption and a link to the course\n \"\"\"\n caption = input['caption']\n link = input['link']\n discount = input['discount']\n return {\n 'caption': caption,\n 'link': link,\n 'discount': discount\n }\n", "infer7b_after_item": "50 \n Output: Get 50% off the Golang course with the coupon code GOLANG50! Learn the foundational skills for building cloud services, web applications, DevOps tools, and backend development. \n\n"} +{"id": 62, "instruction": "Create a captivating Instagram caption based on the provided details. Try to think of as many captions as you can.", "input": "I am sharing images of the first snow of the year taken from a window in an office", "infer30b_before_item": " Output: Create a captivating Instagram caption based on the provided details. Try to think of as many captions as you can. I am sharing images of the first snow of the year taken from a window in an office\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " building. \n Output: 1. The first snow of the year is here! Capturing the beauty of the snow from the window of an office building. \n2. The snow is here and it's magical! Capturing the beauty of the first snow of the year from an office window. \n3. The first snow of the year is here and it's beautiful! Capturing the beauty of the snow from an office window. \n4. The snow is here and it's so beautiful! Capturing the beauty of the first snow of the year from an office window. \n5. The first snow of the year is here and it's so magical! Capturing the beauty of the snow from an office window. \n6. The snow is here and it's so magical! Capturing the beauty of the first snow of the year from an office window. \n7. The first snow of the year is here and it's so beautiful! Capturing the beauty of the snow from an office window. \n8. The snow is here and it's so beautiful! Capturing the beauty of the first snow of the year from an office window. \n9. The", "infer13b_before_item": " Output: The caption that you think is the best.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " building. \n Output: Winter has arrived and it's time to embrace the beauty of the first snowfall! #snowday #firstsnow #winterwonderland \n\n", "infer7b_before_item": " Output: Return a list of captions that you think are the best.\n\nExample:\n\nInput:\n\nInput:\n\nOutput:\n\n[\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n \"It's snowing outside!\",\n ", "infer7b_after_item": " building. \n Output: #SnowDay #FirstSnowOfTheYear #OfficeBuilding #WindowSights #WinterWonderland #ColdWeather #SnowFlakes #SnowCoveredGround #SnowCoveredTrees #SnowCoveredRoads #SnowCoveredLake #SnowCoveredFields #SnowCoveredMountains #SnowCoveredHills #SnowCoveredPark #SnowCoveredPavement #SnowCoveredPath #SnowCoveredFields #SnowCoveredFarm #SnowCoveredFarm #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields #SnowCoveredFields"} +{"id": 63, "instruction": "You are given a topic for an Instagram post. Help the post reach a broader audience by suggesting hashtags related to the post.", "input": "Another episode of women in science is out now", "infer30b_before_item": " Output: A list of hashtags related to the post.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": "! \n Output: #womeninscience #womeninstem #womeninmath #womeninengineering #womeninresearch #womeninSTEM #womeninSTEAM #womeninSTEMcareers #womeninSTEMeducation #womeninSTEMhistory #womeninSTEMleadership #womeninSTEMnetworking #womeninSTEMprofessionals #womeninSTEMroles #womeninSTEMsuccess #womeninSTEMsupport #womeninSTEMtechnology #womeninSTEMwork #womeninSTEMworkforce #womeninSTEMworkshops #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #womeninSTEMwriters #women", "infer13b_before_item": " You are given a list of hashtags and a list of posts. You are to find the hashtags that are used in the posts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "! \n Output: #WomenInScience #Science #Scientists #WomenInSTEM #STEM #Research #Innovation #Discovery #ScienceCommunity #ScienceEducation #ScienceAdvocacy #ScienceForAll #ScienceForChange #ScienceForHumanity #ScienceForTheFuture #ScienceForThePlanet #ScienceForTheEnvironment #ScienceForTheEarth #ScienceForTheFuture #ScienceForTheWorld #ScienceForTheGenerations #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceForTheGenerationsToCome #ScienceFor", "infer7b_before_item": " Output: You are given a topic for an Instagram post. Help the post reach a broader audience by suggesting hashtags related to the post. Another episode of women in science is out now \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "! #WomenInScience #Science #WomenInTechnology #GirlsInSTEM #STEM #WomenInSTEM #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #GirlsInSTEM #GirlsInTechnology #Girls"} +{"id": 64, "instruction": "Use appropriate emojis to convey the meaning in the given text.", "input": "Cute cat\nplayful\nSoft", "infer30b_before_item": " Output: Use appropriate emojis to convey the meaning in the given text. Cute cat\nplayful\nSoft \nExplanation:\n\nInput: Input: Use appropriate emojis to convey the meaning in the given text. Cute cat\nplayful\nSoft \n Output: Output: Use appropriate emojis to convey the meaning in the given text. Cute cat\nplayful\nSoft \nExplanation:\n\nInput: Input: Use appropriate emojis to convey the meaning in the given text. Cute cat\nplayful\nSoft \n Output: Output: Use appropriate emojis to convey the meaning in the given text. Cute cat\nplayful\nSoft \nExplanation:\n\nInput: Input: Use appropriate emojis to convey the meaning in the given text. Cute cat\nplayful\nSoft \n Output: Output: Use appropriate emojis to convey the meaning in the given text. Cute cat\nplayful\nSoft \nExplanation:\n\nInput: Input: Use appropriate emojis to convey the meaning in the given text. Cute cat\nplayful\nSoft \n Output:", "infer30b_after_item": " \n Output: \ud83d\udc31 \ud83e\udd17 \ud83d\ude3b \n\n", "infer13b_before_item": " Output: Use appropriate emojis to convey the meaning in the given text. Cute cat\nSoft \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "ly\nPurring\n\nOutput: \ud83d\udc31 \ud83d\ude0a \ud83d\udc08 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ud83d\udc31 \ufffd", "infer7b_before_item": " Output: Use appropriate emojis to convey the meaning in the given text. Cute cat\nplayful\nSoft\n# 1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.", "infer7b_after_item": " fur \n Output: \ud83d\ude38\ud83d\ude39\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ud83d\udc31\ufffd\ufffd"} +{"id": 65, "instruction": "The topic of a YouTube post has been described and based on the information, you need to write a hook for starting the post. A catchy hook will keep your readers interested so they keep reading. It would be great if you could come up with as many hooks as you can.", "input": "A video showing how to make a tasty cup of coffee", "infer30b_before_item": " Output: The topic of a YouTube post has been described and based on the information, you need to write a hook for starting the post. A catchy hook will keep your readers interested so they keep reading. It would be great if you could come up with as many hooks as you can. A video showing how to make a tasty cup of coffee\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nComment: I'm voting to close this question as off-topic because it is not a question.\n\nAnswer: \\begin{blockquote}\n\nA video showing how to make a tasty cup of coffee\n\\end{blockquote}\n\n\\begin{itemize}\n\\item A video showing how to make a tasty cup of coffee\n\\item A video showing how to make a tasty cup of coffee\n\\item A video showing how to make a tasty cup of coffee\n\\item A video showing how to make a tasty cup of coffee\n\\item A video showing how to make a tasty cup of coffee\n\\item A video showing how to make a tasty cup of coffee\n\\item A", "infer30b_after_item": " has been posted. \n Output: Are you ready to make the perfect cup of coffee? \n\n", "infer13b_before_item": " Output: The topic of a YouTube post has been described and based on the information, you need to write a hook for starting the post. A catchy hook will keep your readers interested so they keep reading. It would be great if you could come up with as many hooks as you can. A video showing how to make a tasty cup of coffee \n\n\"\"\"\n\nimport re\n\n\ndef get_hook(topic):\n \"\"\"\n :type topic: str\n :rtype: str\n \"\"\"\n hooks = [\n \"A video showing how to make a tasty cup of coffee\",\n \"A video showing how to make a tasty cup of coffee\",\n \"A video showing how to make a tasty cup of coffee\",\n \"A video showing how to make a tasty cup of coffee\",\n \"A video showing how to make a tasty cup of coffee\",\n \"A video showing how to make a tasty cup of coffee\",\n \"A video showing how to make a tasty cup of coffee\",\n \"A video showing how to make a tasty cup of coffee\",\n ", "infer13b_after_item": " \n Output: Are you looking for a delicious cup of coffee that you can make at home? Then this video is for you! Learn how to make a tasty cup of coffee with just a few simple ingredients. \n\n", "infer7b_before_item": " The output is the post itself. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need to write a post that is interesting and engaging. You need", "infer7b_after_item": ". \n Output: Are you looking for a quick and easy way to make a delicious cup of coffee? Then this video is for you! \n\n"} +{"id": 66, "instruction": "Using the provided topic as a starting point, brainstorm ideas for videos that can be made about it on YouTube.", "input": "In spite of the fact that procrastination feels bad to you, why do you do it?", "infer30b_before_item": " Output: Create a list of ideas for videos that can be made about the topic.\nSteps:\n 1. Brainstorm ideas for videos that can be made about the topic.\n 2. Create a list of ideas for videos that can be made about the topic.\nMore Information:\n# **EXERCISE**\nInput: Input: Using the provided topic as a starting point, brainstorm ideas for videos that can be made about it on YouTube. In spite of the fact that procrastination feels bad to you, why do you do it? \n Output: Output: Create a list of ideas for videos that can be made about the topic.\n Steps:\n 1. Brainstorm ideas for videos that can be made about the topic.\n 2. Create a list of ideas for videos that can be made about the topic.\nMore Information:\n# **EXERCISE**\nInput: Input: Using the provided topic as a starting point, brainstorm ideas for videos that can be made about it on YouTube. In spite of the fact that procrastination feels bad to you, why do you do it? \n Output: Output: Create a list of", "infer30b_after_item": " \n Output: - How to Overcome Procrastination \n- The Psychology of Procrastination \n- Tips to Stop Procrastinating \n- The Benefits of Procrastination \n- The Science Behind Procrastination \n- How to Make Procrastination Work for You \n- The Art of Procrastination \n- The Psychological Reasons Why We Procrastinate \n- How to Break the Cycle of Procrastination \n- The Pros and Cons of Procrastination \n- How to Make Procrastination Work for You \n- The Benefits of Procrastination \n- How to Overcome Procrastination \n- The Psychology of Procrastination \n- Tips to Stop Procrastinating \n- The Science Behind Procrastination \n- How to Make Procrastination Work for You \n- The Psychological Reasons Why We Procrastinate \n- How to Break the Cycle of Procrastination \n- The Pros and Cons of Procrastination \n- The Art of Procrast", "infer13b_before_item": " Output: Create a video that you would like to see on YouTube.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Ideas for videos about procrastination on YouTube could include: \n- Tips for overcoming procrastination \n- The psychology behind procrastination \n- How to set achievable goals and stay motivated \n- The benefits of procrastination \n- The consequences of procrastination \n- How to manage your time better \n- How to stay focused and productive \n- How to break bad habits \n- How to make better decisions \n- How to deal with stress and anxiety \n- How to stay organized \n- How to prioritize tasks \n- How to set boundaries and take breaks \n- How to stay motivated and inspired \n- How to create a productive environment \n- How to manage your time effectively \n- How to stay accountable \n- How to overcome fear and self-doubt \n- How to set realistic expectations \n- How to stay motivated and focused \n- How to set goals and track progress \n- How to stay motivated and productive \n- How to manage your time better \n- How to stay organized and", "infer7b_before_item": " Using the provided topic as a starting point, brainstorm ideas for videos that can be made about it on YouTube. In spite of the fact that procrastination feels bad to you, why do you do it? \n 1. \n 2. \n 3. \n 4. \n 5. \n 6. \n 7. \n 8. \n 9. \n 10. \n 11. \n 12. \n 13. \n 14. \n 15. \n 16. \n 17. \n 18. \n 19. \n 20. \n 21. \n 22. \n 23. \n 24. \n 25. \n 26. \n 27. \n 28. \n 29. \n 30. \n 31. \n 32. \n 33. \n 34. \n 35. \n 36. \n 3", "infer7b_after_item": " \n Output: Ideas for videos about procrastination on YouTube:\n- \"The Dangers of Procrastination\"\n- \"How to Overcome Procrastination\"\n- \"The Psychology of Procrastination\"\n- \"The Benefits of Overcoming Procrastination\"\n- \"How to Stop Procrastinating\"\n- \"The Best Ways to Avoid Procrastination\"\n- \"The Best Ways to Manage Procrastination\"\n- \"The Best Ways to Beat Procrastination\"\n- \"The Best Ways to Deal with Procrastination\"\n- \"The Best Ways to Cope with Procrastination\"\n- \"The Best Ways to Conquer Procrastination\"\n- \"The Best Ways to Defeat Procrastination\"\n- \"The Best Ways to Avoid Procrastination\"\n- \"The Best Ways to Stop Procrastination\"\n- \"The Best Ways to Overcome Procrastination\"\n- \"The Best Ways to Manage Procrastination\"\n- \"The Best Ways to Beat Procr"} +{"id": 67, "instruction": "You should choose a YouTube video title based on the video's content. A video's title tells viewers what to expect from it. It should be direct, honest, and clear. The title of the video needs to capture the attention of viewers, so do not use an unclear or ambiguous one.", "input": "A research study has been conducted to determine if exercise really can \"boost\" your metabolism.", "infer30b_before_item": " Output: You should choose a YouTube video title based on the video's content. A video's title tells viewers what to expect from it. It should be direct, honest, and clear. The title of the video needs to capture the attention of viewers, so do not use an unclear or ambiguous one. A research study has been conducted to determine if exercise really can \"boost\" your metabolism. \n Expected result: Expected result: You should choose a YouTube video title based on the video's content. A video's title tells viewers what to expect from it. It should be direct, honest, and clear. The title of the video needs to capture the attention of viewers, so do not use an unclear or ambiguous one. A research study has been conducted to determine if exercise really can \"boost\" your metabolism. \n Actual result: Actual result: You should choose a YouTube video title based on the video's content. A video's title tells viewers what to expect from it. It should be direct, honest, and clear. The title of the video needs to capture the attention of viewers, so do not use an unclear or ambiguous one. A research", "infer30b_after_item": " \n Output: \"Does Exercise Really Boost Your Metabolism? Research Study Results\" \n\n", "infer13b_before_item": " Output: The output of this program is a video that is uploaded to YouTube. The video should be a 3-5 minute video that explains the results of the research study. The video should be informative and engaging. It should be clear and concise. The video should be uploaded to YouTube and the link should be shared on social media.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: \"Research Study: Does Exercise Really Boost Your Metabolism?\" \n\n", "infer7b_before_item": " Output: The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the video is the video itself. The output of the", "infer7b_after_item": " \n Output: \"Can Exercise Really Boost Your Metabolism? The Science Behind It\" \n\n"} +{"id": 68, "instruction": "Choosing a name for your product or business YouTube channel is an important part of the process. Based on the description of the product or business, you should come up with some interesting names. Take some time to brainstorm your ideas.", "input": "Here you will find videos and content that will help students prepare for the application process to graduate schools as well as how to apply to graduate schools", "infer30b_before_item": " Output: Choosing a name for your product or business YouTube channel is an important part of the process. Based on the description of the product or business, you should come up with some interesting names. Take some time to brainstorm your ideas. Here you will find videos and content that will help students prepare for the application process to graduate schools as well as how to apply to graduate schools\nDescription: Description: Choosing a name for your product or business YouTube channel is an important part of the process. Based on the description of the product or business, you should come up with some interesting names. Take some time to brainstorm your ideas. Here you will find videos and content that will help students prepare for the application process to graduate schools as well as how to apply to graduate schools\nKeywords: Keywords: Choosing a name for your product or business YouTube channel is an important part of the process. Based on the description of the product or business, you should come up with some interesting names. Take some time to brainstorm your ideas. Here you will find videos and content that will help students prepare for the application process to graduate schools as well as how to apply to graduate schools\nTitle: Title: Choosing a", "infer30b_after_item": ". \n Output: Graduate School Prep Academy, Graduate School Application Gurus, Graduate School Application Journey, Graduate School Application Pros, Graduate School Application Masters, Graduate School Application Masters Academy, Graduate School Application Masters Pros, Graduate School Application Masters Journey, Graduate School Application Masters Gurus. \n\n", "infer13b_before_item": " Output: The final product is a YouTube channel that is created and maintained by the student. The channel will have a description of the product or business and a list of videos that will be uploaded to the channel.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": ". \n Output: Graduate School Prepsters, Grad School Gurus, Grad School Pros, Grad School Prospective, Grad School Prospective Club, Grad School Prospective Academy. \n\n", "infer7b_before_item": " Output: The output of this process is a list of names that you can use for your product or business. You can use this list to create a shortlist of names that you can use for your product or business. \n# 1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.", "infer7b_after_item": ". \n Output: 1. Grad School Gurus\n2. Grad School Insiders\n3. Grad School Success\n4. Grad School Secrets\n5. Grad School Essentials\n6. Grad School Advice\n7. Grad School Tips\n8. Grad School Guides\n9. Grad School Experts\n10. Grad School Advisors\n11. Grad School Helpers\n12. Grad School Advisors\n13. Grad School Guides\n14. Grad School Experts\n15. Grad School Advisors\n16. Grad School Helpers\n17. Grad School Advisors\n18. Grad School Guides\n19. Grad School Experts\n20. Grad School Advisors\n21. Grad School Helpers\n22. Grad School Advisors\n23. Grad School Guides\n24. Grad School Experts\n25. Grad School Advisors\n26. Grad School Helpers\n27. Grad School Advisors\n28. Grad School Guides\n29. Grad School Experts\n30. Grad School Advisors\n31. Grad School Helpers\n32."} +{"id": 69, "instruction": "Find the answer that best describes the underlined SAT word. Select the correct option and explain the meaning of the underlined word.", "input": "Despite the _cacophony, the student tried to study. \nA. Loud sounds\nB. Difficult subject\nC. Late hour\nD. Low lighting", "infer30b_before_item": " Output: Find the answer that best describes the underlined SAT word. Select the correct option and explain the meaning of the underlined word. Despite the _cacophony, the student tried to study. \nA. Loud sounds\nB. Difficult subject\nC. Late hour\nD. Low lighting \nExplanation: The word _cacophony_ means loud sounds. The student was trying to study despite the loud sounds.\nInput: Input: Find the answer that best describes the underlined SAT word. Select the correct option and explain the meaning of the underlined word. The _cacophony_ of the city was too much for the country boy. \nA. Loud sounds\nB. Difficult subject\nC. Late hour\nD. Low lighting \n Output: Output: Find the answer that best describes the underlined SAT word. Select the correct option and explain the meaning of the underlined word. The _cacophony_ of the city was too much for the country boy. \nA. Loud sounds\nB. Difficult subject\nC. Late hour\nD. Low lighting \nExplanation: The word", "infer30b_after_item": " \n Output: A. Loud sounds. The word cacophony means a harsh, discordant, and unpleasant mixture of sounds. \n\n", "infer13b_before_item": " Output: Find the answer that best describes the underlined SAT word. Select the correct option and explain the meaning of the underlined word. Despite the _cacophony, the student tried to study. \nA. Loud sounds\nB. Difficult subject\nC. Late hour\nD. Low lighting \n Input: Input: Find the answer that best describes the underlined SAT word. Select the correct option and explain the meaning of the underlined word. The _cacophony of the city was deafening. \nA. Loud sounds\nB. Difficult subject\nC. Late hour\nD. Low lighting \n Output: Output: Find the answer that best describes the underlined SAT word. Select the correct option and explain the meaning of the underlined word. The _cacophony of the city was deafening. \nA. Loud sounds\nB. Difficult subject\nC. Late hour\nD. Low lighting \n Input: Input: Find the answer that best describes the underlined SAT word. Select the correct option and explain the meaning of the underlined word. The _cacophony of the city was de", "infer13b_after_item": "\n\nAnswer: B. Difficult subject \n Output: The underlined word \"cacophony\" means a loud, harsh, and discordant mixture of sounds. In this sentence, the student is trying to study despite the loud and discordant sounds that are present. \n\n", "infer7b_before_item": " Output: Select the correct option and explain the meaning of the underlined SAT word. Despite the _cacophony, the student tried to study. \nA. Loud sounds\nB. Difficult subject\nC. Late hour\nD. Low lighting \n# **SAT Vocabulary**\nThe SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability to read and understand words. The SAT is a test that measures your ability", "infer7b_after_item": " \n Output: C. Late hour \n\n"} +{"id": 70, "instruction": "Find synonyms for the given word. You need to write down how the provided synonyms differ from the original word in terms of meaning, usage, etc.", "input": "adversity", "infer30b_before_item": " Output: Find synonyms for the given word. You need to write down how the provided synonyms differ from the original word in terms of meaning, usage, etc. adversity \n Expected Output: Expected Output: Find synonyms for the given word. You need to write down how the provided synonyms differ from the original word in terms of meaning, usage, etc. adversity \n Author: Author: Find synonyms for the given word. You need to write down how the provided synonyms differ from the original word in terms of meaning, usage, etc. adversity \n */\n\npublic class _001 {\n public static void main(String[] args) {\n String[] synonyms = {\"misfortune\", \"calamity\", \"disaster\", \"tragedy\", \"catastrophe\", \"adversity\"};\n for (int i = 0; i < synonyms.length; i++) {\n System.out.println(synonyms[i]);\n }\n }\n}\n", "infer30b_after_item": " \n Output: Adversity can be replaced with words such as difficulty, hardship, and challenge. Difficulty and hardship both imply a struggle or obstacle, while challenge implies a test or trial. \n\n", "infer13b_before_item": " Output: Find antonyms for the given word. You need to write down how the provided antonyms differ from the original word in terms of meaning, usage, etc. adversity \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Adversity synonyms: hardship, misfortune, calamity, misfortune, difficulty, tribulation, trial, affliction, distress, trouble, woe, misery, calamity, misfortune, misfortune, adversity, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, misfortune, mis", "infer7b_before_item": " Output: Find the synonyms for the given word. You need to write down how the provided synonyms differ from the original word in terms of meaning, usage, etc. adversity \n */\n\npublic class Synonyms {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n String word = sc.nextLine();\n String[] synonyms = sc.nextLine().split(\" \");\n for (String synonym : synonyms) {\n System.out.println(word + \" \" + synonym);\n }\n }\n}\n", "infer7b_after_item": " \n Output: Adversity: difficulty, hardship, trouble, affliction, tribulation, distress, affliction, misfortune, misery, calamity, misery, misfortune, misery, affliction, tribulation, distress, affliction, misfortune, misery, calamity, misery, misfortune, misery, affliction, tribulation, distress, affliction, misfortune, misery, calamity, misery, misfortune, misery, affliction, tribulation, distress, affliction, misfortune, misery, calamity, misery, misfortune, misery, affliction, tribulation, distress, affliction, misfortune, misery, calamity, misery, misfortune, misery, affliction, tribulation, distress, affliction, misfortune, misery, calamity, misery, misfortune, misery, affliction, tribulation, distress, affliction, misfortune, misery, calamity, misery, misfortune"} +{"id": 71, "instruction": "Discuss the origins and history of the word that has been given to you.", "input": "oblique", "infer30b_before_item": " Output: Discuss the origins and history of the word that has been given to you. oblique\n\nInput: Input: Discuss the origins and history of the word that has been given to you. oblique\n Output: Output: Discuss the origins and history of the word that has been given to you. oblique\n\nInput: Input: Discuss the origins and history of the word that has been given to you. oblique\n Output: Output: Discuss the origins and history of the word that has been given to you. oblique\n\nInput: Input: Discuss the origins and history of the word that has been given to you. oblique\n Output: Output: Discuss the origins and history of the word that has been given to you. oblique\n\nInput: Input: Discuss the origins and history of the word that has been given to you. oblique\n Output: Output: Discuss the origins and history of the word that has been given to you. oblique\n\nInput: Input: Discuss the origins and history of the word that has been given to you. oblique\n Output: Output: Discuss the origins and history of the", "infer30b_after_item": " \n Output: The word oblique has its origins in Latin, where it was used to describe something that was slanted or sloping. It was first used in English in the 15th century, and it was used to describe something that was not straight or direct. Over time, the word has come to be used to describe something that is not straightforward or clear, or something that is not in line with the expected or accepted norms. \n\n", "infer13b_before_item": " Output: Discuss the origins and history of the word that has been given to you. oblique \n\noblique\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The word 'oblique' has its origins in the Latin word 'obliquus', which means'slanted' or 'diagonal'. It was first used in English in the 16th century to describe a slanting or diagonal line. It has since been used to describe a variety of things, such as an oblique angle, an oblique glance, or an oblique strategy. \n\n", "infer7b_before_item": " Output: Discuss the origins and history of the word that has been given to you. oblique \n*/\n\n// Initial Solution\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed.\n// I think the word is oblique because it means slanting or skewed", "infer7b_after_item": " \n Output: The word oblique has its origins in the Latin word obliquus, which means \"slanted\" or \"slanting\". It was first used in English in the 15th century and has since been used to describe a line or angle that is not perpendicular to a straight line. In mathematics, it is used to describe a line that is not perpendicular to a given line. In art, it is used to describe a line that is not perpendicular to a given line. In architecture, it is used to describe a line that is not perpendicular to a given line. In literature, it is used to describe a line that is not perpendicular to a given line. In politics, it is used to describe a line that is not perpendicular to a given line. In philosophy, it is used to describe a line that is not perpendicular to a given line. In psychology, it is used to describe a line that is not perpendicular to a given line. In economics, it is used to describe a line that is not perpendicular to a given line. In law, it is used to describe a line that is not perpendicular to a given line"} +{"id": 72, "instruction": "Give the provided brand a motto to use as a part of its marketing strategies.", "input": "The Ice cream shop is a small, quaint shop located in the heart of the historic district. They offer a variety of ice cream flavors, as well as Italian ice, gelato, and sorbet. The shop has a few tables and chairs, but most of their business is to-go. They offer a loyalty program where you can get a free scoop of ice cream after 10 purchases.", "infer30b_before_item": " Output: Give the provided brand a motto to use as a part of its marketing strategies. The Ice cream shop is a small, quaint shop located in the heart of the historic district. They offer a variety of ice cream flavors, as well as Italian ice, gelato, and sorbet. The shop has a few tables and chairs, but most of their business is to-go. They offer a loyalty program where you can get a free scoop of ice cream after 10 purchases. \n \n*/\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: \"Indulge in the Sweetness of Life - At The Ice Cream Shop!\" \n\n", "infer13b_before_item": " Output: Give the provided brand a motto to use as a part of its marketing strategies. The Ice cream shop is a small, quaint shop located in the heart of the historic district. They offer a variety of ice cream flavors, as well as Italian ice, gelato, and sorbet. The shop has a few tables and chairs, but most of their business is to-go. They offer a loyalty program where you can get a free scoop of ice cream after 10 purchases. \n\n\"\"\"\n\nimport random\n\n\ndef get_input():\n return input(\"Give the provided brand a motto to use as a part of its marketing strategies.\\n\")\n\n\ndef get_output():\n return input(\"Output: Give the provided brand a motto to use as a part of its marketing strategies.\\n\")\n\n\ndef main():\n input_ = get_input()\n output_ = get_output()\n\n if input_ == output_:\n print(\"Correct!\")\n else:\n print(\"Incorrect!\")\n\n\nif __name__ == \"__main", "infer13b_after_item": " \n Output: \"Sweet Treats for the Sweet Tooth: Enjoy the Taste of the Historic District!\" \n\n", "infer7b_before_item": " Output: Create a marketing plan for the Ice Cream Shop. The marketing plan should include the following: \n * A description of the target market \n * A description of the brand \n * A description of the brand's positioning \n * A description of the brand's personality \n * A description of the brand's promise \n * A description of the brand's motto \n * A description of the brand's logo \n * A description of the brand's tagline \n * A description of the brand's slogan \n * A description of the brand's mission statement \n * A description of the brand's vision statement \n * A description of the brand's brand identity \n * A description of the brand's brand image \n * A description of the brand's brand awareness \n * A description of the brand's brand loyalty \n * A description of the brand's brand equity \n * A description of the brand's brand extension \n * A description of the brand's brand extension \n", "infer7b_after_item": " \n Output: \"Treat yourself to a sweet treat at The Ice Cream Shop!\" \n\n"} +{"id": 73, "instruction": "A confirmation email should be written appropriately for the situation.", "input": "A meeting has been scheduled, and the sender expects the other to review the slides.", "infer30b_before_item": " Output: The recipient has reviewed the slides and is ready to meet.\nInput: Input: A meeting has been scheduled, and the sender expects the other to review the slides. \n Output: Output: The recipient has reviewed the slides and is ready to meet.\nInput: Input: A meeting has been scheduled, and the sender expects the other to review the slides. \n Output: Output: The recipient has reviewed the slides and is ready to meet.\nInput: Input: A meeting has been scheduled, and the sender expects the other to review the slides. \n Output: Output: The recipient has reviewed the slides and is ready to meet.\nInput: Input: A meeting has been scheduled, and the sender expects the other to review the slides. \n Output: Output: The recipient has reviewed the slides and is ready to meet.\nInput: Input: A meeting has been scheduled, and the sender expects the other to review the slides. \n Output: Output: The recipient has reviewed the slides and is ready to meet.\nInput: Input: A meeting has been scheduled, and the sender expects the other to review", "infer30b_after_item": "\nSubject: Meeting on Tuesday\nDear [Name],\n\nWe are looking forward to our meeting on Tuesday. Please review the slides in advance.\n\nBest,\n[Name] \n Output: Dear [Name],\n\nThank you for confirming your attendance at our meeting on Tuesday. We look forward to seeing you then. \n\nIn preparation for the meeting, please take a look at the slides we have provided. We believe they will be helpful in understanding the topic.\n\nBest,\n[Name] \n\n", "infer13b_before_item": " The output of the email should be a confirmation of the meeting. \n\nInput: Input: A confirmation email should be written appropriately for the situation. A meeting has been scheduled, and the sender expects the other to review the slides. \n Output: The output of the email should be a confirmation of the meeting. \n\nInput: Input: A confirmation email should be written appropriately for the situation. A meeting has been scheduled, and the sender expects the other to review the slides. \n Output: The output of the email should be a confirmation of the meeting. \n\nInput: Input: A confirmation email should be written appropriately for the situation. A meeting has been scheduled, and the sender expects the other to review the slides. \n Output: The output of the email should be a confirmation of the meeting. \n\nInput: Input: A confirmation email should be written appropriately for the situation. A meeting has been scheduled, and the sender expects the other to review the slides. \n Output: The output of the email should be a confirmation of the meeting. \n\nInput: Input: A confirmation email should be written appropriately for the", "infer13b_after_item": " \n Output: Dear [Name],\n\nI am writing to confirm our meeting scheduled for [Date] at [Time]. I have attached the slides for your review.\n\nI look forward to discussing them with you and getting your feedback.\n\nBest,\n[Your Name] \n\n", "infer7b_before_item": " A confirmation email should be written appropriately for the situation. A meeting has been scheduled, and the sender expects the other to review the slides. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: I am writing to confirm our meeting on [date] at [time]. I have attached the slides for our discussion. I look forward to seeing you then. \n\n"} +{"id": 74, "instruction": "You need to write an email to negotiate your salary.", "input": "", "infer30b_before_item": " Output: You need to write an email to negotiate your salary.\nInput: Input: You need to write an email to negotiate your salary. \n Output: Output: You need to write an email to negotiate your salary.\nInput: Input: You need to write an email to negotiate your salary. \n Output: Output: You need to write an email to negotiate your salary.\nInput: Input: You need to write an email to negotiate your salary. \n Output: Output: You need to write an email to negotiate your salary.\nInput: Input: You need to write an email to negotiate your salary. \n Output: Output: You need to write an email to negotiate your salary.\nInput: Input: You need to write an email to negotiate your salary. \n Output: Output: You need to write an email to negotiate your salary.\nInput: Input: You need to write an email to negotiate your salary. \n Output: Output: You need to write an email to negotiate your salary.\nInput: Input: You need to write an email to negotiate your salary. ", "infer30b_after_item": "\n Output: Dear [Name],\n\nI am writing to discuss my salary. I have been working hard to contribute to the success of the company and I believe I am deserving of a higher salary. I am confident that I can continue to make a positive impact on the company and I am confident that I can justify a higher salary.\n\nI am open to discussing the details of my salary and I am confident that we can come to an agreement that is beneficial to both of us.\n\nThank you for your time and consideration.\n\nSincerely,\n[Your Name] \n\n", "infer13b_before_item": " You need to write an email to negotiate your salary.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The The \n\n\n\n\n\n The The The", "infer13b_after_item": "\n Output: Dear [Name],\n\nI am writing to discuss my salary. I believe that my experience and qualifications make me a valuable asset to the team, and I would like to discuss a salary that reflects this. I am aware of the salary range for this position and I believe that I am worth a salary that is in the upper range.\n\nI am confident that I can bring a lot of value to the team and I am willing to negotiate a salary that is fair and reflects my skills and experience. I look forward to hearing your thoughts on this matter.\n\nThank you for your time and consideration.\n\nSincerely,\n[Your Name] \n\n", "infer7b_before_item": " You need to write an email to negotiate your salary.\nYou need to write an email to negotiate your salary.\nDear Mr. Smith,\nI am writing to you to discuss my salary. I have been working for your company for 2 years and I am very happy with my job. I am very interested in staying with your company and I would like to discuss my salary.\nI am currently earning $30,000 per year. I would like to discuss my salary with you. I would like to earn $35,000 per year.\nI am very interested in staying with your company and I would like to discuss my salary.\nThank you for your time.\nSincerely,\nYour Name\nYour Signature\n# 10. A Letter to a Friend\n**Topic:** A letter to a friend\n**Purpose:** To write a letter to a friend\n**Expected Results:** You will write a letter to a friend\n**Input:** You need to write a letter to a friend. \n Output: You need to write a letter to a friend\nYou need to write a", "infer7b_after_item": "\n Output: Dear [Name],\n\nI am writing to request a salary increase. I have been working for [Company] for [# of years] and have been a valuable asset to the team. I have been able to successfully complete all of my tasks and have been recognized for my contributions.\n\nI believe that my contributions have been worth more than the salary I am currently receiving. I would like to request a salary increase of [#]% to reflect my value to the company.\n\nI look forward to hearing from you and thank you for your time.\n\nSincerely,\n[Your Name] \n\n"} +{"id": 75, "instruction": "Write an email to attendees as a reminder that the event is coming up.", "input": "", "infer30b_before_item": " Output: The attendees receive the email. \n Precondition: Precondition: The event is scheduled. \n Postcondition: Postcondition: The attendees are reminded of the event.\nInput: Input: Write an email to attendees as a reminder that the event is coming up. \n Output: Output: The attendees receive the email. \n Precondition: Precondition: The event is scheduled. \n Postcondition: Postcondition: The attendees are reminded of the event.\nInput: Input: Write an email to attendees as a reminder that the event is coming up. \n Output: Output: The attendees receive the email. \n Precondition: Precondition: The event is scheduled. \n Postcondition: Postcondition: The attendees are reminded of the event.\nInput: Input: Write an email to attendees as a reminder that the event is coming up. \n Output: Output: The attendees receive the email. \n Precondition: Precondition: The event is scheduled. \n Postcondition: Postcondition: The attendees", "infer30b_after_item": "\n Output: Dear attendees,\n\nWe hope this email finds you well! This is a friendly reminder that our event is coming up soon. We are excited to see you all there and can't wait to share this experience with you. \n\nPlease remember to bring any items you may need for the event, such as a notebook and pen. We will provide any other materials you may need. \n\nWe look forward to seeing you soon!\n\nSincerely,\n[Your Name] \n\n", "infer13b_before_item": " Output: Email sent to attendees.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: Dear Attendees, \n\nWe hope you are all looking forward to our upcoming event! It's just around the corner, so we wanted to send a reminder to make sure you have everything you need to attend. \n\nThe event will be held on [date], at [location]. Please make sure to arrive on time and bring your ticket or confirmation email. \n\nWe are excited to see you all there! \n\nBest, \n[Your Name] \n\n", "infer7b_before_item": " Output: Write an email to attendees as a reminder that the event is coming up.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "\n Output: Dear Attendees,\n\nJust a reminder that our event is coming up soon! We are looking forward to seeing you there and having a great time. Please make sure to RSVP and let us know if you have any questions.\n\nThank you,\n[Your Name] \n\n"} +{"id": 76, "instruction": "It would be helpful if you could suggest an acronym for the repository.", "input": "A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.", "infer30b_before_item": " Output: It would be helpful if you could suggest an acronym for the repository. A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits. \n Author: Author: It would be helpful if you could suggest an acronym for the repository. A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits. \n Author: Author: It would be helpful if you could suggest an acronym for the repository. A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits. \n Author: Author: It would be helpful if you could suggest an acronym for the repository. A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits. \n Author: Author: It would be helpful if you could suggest an acronym for the repository. A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits. \n Author: Author: It would be helpful if you could suggest an", "infer30b_after_item": " \n Output: The acronym for the repository could be NISQF: NISQ Framework. \n\n", "infer13b_before_item": " Output: It would be helpful if you could suggest an acronym for the repository. A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits. \n\"\"\"\n\nimport os\nimport sys\nimport time\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow_probability as tfp\nimport tensorflow_probability.experimental.nngraph as tfnngraph\nimport tensorflow_probability.experimental.nngraph as tfnngraph\nimport tensorflow_probability.experimental.nngraph as tfnngraph\nimport tensorflow_probability.experimental.nngraph as tfnngraph\nimport tensorflow_probability.experimental.nngraph as tfnngraph\nimport tensorflow_probability.experimental.nngraph as tfnngraph\nimport tensorflow_probability.experimental.nngraph as tfnngraph\nimport tensorflow_probability.experimental.nngraph as tfnngraph\nimport tensorflow_probability.experimental.nngraph as tfnngraph\nimport tensorflow_probability.experimental.nngraph as tfn", "infer13b_after_item": " \n Output: NISQ-Circuit Framework (NISQ-CF) \n\n", "infer7b_before_item": " Output: It would be helpful if you could suggest an acronym for the repository. A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits. \n\"\"\"\nimport os\nimport sys\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\nsys.path.insert(0, os.path.abspath('..'))\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'sphinx.ext.doctest',\n 'sph", "infer7b_after_item": " \n Output: NISQ-Py. \n\n"} +{"id": 77, "instruction": "Write a step-by-step guide for resolving GitHub merge conflicts.", "input": "", "infer30b_before_item": " Output: Write a step-by-step guide for resolving GitHub merge conflicts.\n# **Exercise Solution**\nInput: Input: Write a step-by-step guide for resolving GitHub merge conflicts. \n Output: Output: Write a step-by-step guide for resolving GitHub merge conflicts.\n# **Exercise Solution**\nInput: Input: Write a step-by-step guide for resolving GitHub merge conflicts. \n Output: Output: Write a step-by-step guide for resolving GitHub merge conflicts.\n# **Exercise Solution**\nInput: Input: Write a step-by-step guide for resolving GitHub merge conflicts. \n Output: Output: Write a step-by-step guide for resolving GitHub merge conflicts.\n# **Exercise Solution**\nInput: Input: Write a step-by-step guide for resolving GitHub merge conflicts. \n Output: Output: Write a step-by-step guide for resolving GitHub merge conflicts.\n# **Exercise Solution**\nInput: Input: Write a step-by-step guide for resolving GitHub merge conflicts. \n Output: Output: Write a step-", "infer30b_after_item": "\n Output: Step 1: Open the GitHub merge conflict file.\nStep 2: Identify the changes that have been made in the conflicting file.\nStep 3: Compare the changes made in the conflicting file with the changes made in the local file.\nStep 4: Decide which changes to keep and which to discard.\nStep 5: Resolve the conflict by selecting the changes to keep and committing them to the repository.\nStep 6: Test the changes to ensure that the merge was successful. \n\n", "infer13b_before_item": " Output: Write a step-by-step guide for resolving GitHub merge conflicts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n", "infer13b_after_item": "\n Output: Step 1: Open the conflicted files in the GitHub merge tool.\nStep 2: Review the changes made by each side and decide which one to keep.\nStep 3: Select the changes you want to keep and click the \"Merge\" button.\nStep 4: Review the changes that were made and make any necessary edits.\nStep 5: Click the \"Merge\" button to accept the changes.\nStep 6: Click the \"Save\" button to save the changes. \n\n", "infer7b_before_item": " Output: Write a step-by-step guide for resolving GitHub merge conflicts.\n\n# I worked on this challenge by myself.\n# I spent 1.5 hours on this challenge.\n\n# Pseudocode\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps:\n\n# Input:\n# Output:\n# Steps", "infer7b_after_item": "\n Output: Step 1: Open the merge conflict file in your editor.\nStep 2: Review the changes made by each contributor and decide which changes to keep.\nStep 3: If you want to keep both changes, select the \"Keep Both\" option.\nStep 4: If you want to keep one of the changes, select the \"Keep This\" option.\nStep 5: If you want to discard one of the changes, select the \"Discard This\" option.\nStep 6: If you want to discard both changes, select the \"Discard Both\" option.\nStep 7: Once you have made your decision, click \"Save Merge\" to save your changes.\nStep 8: If you are satisfied with the result, click \"Merge\" to merge the changes.\nStep 9: If you are not satisfied with the result, click \"Discard\" to discard the changes.\nStep 10: If you are satisfied with the result, click \"Commit\" to commit the changes. \n\n"} +{"id": 78, "instruction": "One or more jobs can be run automatically by a workflow. The workflows are defined in the repository as YAML files and will run if an event occurs in the repository, as well as manually, or on a certain schedule.\nBring an example of a workflow that demonstrate the CI/CD features of GitHub Actions.", "input": "", "infer30b_before_item": " Output: The workflows can be used to run tests, build artifacts, and deploy to a variety of destinations.\nBring an example of a workflow that demonstrate the CI/CD features of GitHub Actions. \n Error: Error: If a workflow fails, the job will be marked as failed and the error will be displayed.\nBring an example of a workflow that demonstrate the CI/CD features of GitHub Actions. \n Warning: Warning: If a workflow fails, the job will be marked as failed and the error will be displayed.\nBring an example of a workflow that demonstrate the CI/CD features of GitHub Actions. \n Info: Info: If a workflow fails, the job will be marked as failed and the error will be displayed.\nBring an example of a workflow that demonstrate the CI/CD features of GitHub Actions. \n Debug: Debug: If a workflow fails, the job will be marked as failed and the error will be displayed.\nBring an example of a workflow that demonstrate the CI/CD features of GitHub Actions. \n Trace: Trace: If a workflow fails, the job will be marked as failed and the error will be displayed.\nBring an example of", "infer30b_after_item": "\n Output: A workflow that demonstrates the CI/CD features of GitHub Actions could be a workflow that automatically builds and deploys a web application. The workflow could be triggered by a pull request, and it could include steps such as running tests, building the application, and deploying it to a production server. \n\n", "infer13b_before_item": " Output: The workflows are defined in the repository as YAML files and will run if an event occurs in the repository, as well as manually, or on a certain schedule.\nBring an example of a workflow that demonstrate the CI/CD features of GitHub Actions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: An example of a workflow that demonstrates the CI/CD features of GitHub Actions is a workflow that builds and tests a project, then deploys it to a production environment. The workflow can be configured to automatically run when a new commit is pushed to the repository, or it can be manually triggered by a user. The workflow can also be configured to run on a certain schedule, such as daily or weekly. \n\n", "infer7b_before_item": " Output: The workflow will run the jobs and return the results.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "\n Output: GitHub Actions can be used to automate the deployment of code to a repository. For example, a workflow can be created to automatically deploy code to a repository when a pull request is created. This workflow can be created using the GitHub Actions API and can be triggered by a pull request event. The workflow can also be triggered manually or on a certain schedule. \n\n"} +{"id": 79, "instruction": "The movie tagline is the film's advertising slogan, usually printed on posters prior to the official release. It is a short description or comment on a movie to capture the essence of the movie, and ultimately make you watch the movie. Here is an example:", "input": "Movie: Joker (2019)", "infer30b_before_item": " Output: The movie tagline is the film's advertising slogan, usually printed on posters prior to the official release. It is a short description or comment on a movie to capture the essence of the movie, and ultimately make you watch the movie. Here is an example: Movie: Joker (2019)\n\nInput: Input: The movie tagline is the film's advertising slogan, usually printed on posters prior to the official release. It is a short description or comment on a movie to capture the essence of the movie, and ultimately make you watch the movie. Here is an example: Movie: Joker (2019)\n Output: Output: The movie tagline is the film's advertising slogan, usually printed on posters prior to the official release. It is a short description or comment on a movie to capture the essence of the movie, and ultimately make you watch the movie. Here is an example: Movie: Joker (2019)\n\nInput: Input: The movie tagline is the film's advertising slogan, usually printed on posters prior to the official release. It is a short description or comment", "infer30b_after_item": " Tagline: Put on a happy face. \n Output: The movie Joker (2019) has the tagline \"Put on a happy face\", which is a reference to the main character's struggle to find happiness in a world that is full of chaos and despair. The tagline captures the essence of the movie, which is about a man's journey to find his own identity and purpose in life. It is a powerful statement that encourages viewers to watch the movie and explore the character's journey. \n\n", "infer13b_before_item": " The output is the tagline of the movie.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\nMovie tagline: \"The Clown Prince of Crime\". \n Output: The movie tagline for Joker (2019) is \"The Clown Prince of Crime\". \n\n", "infer7b_before_item": " The movie tagline is the film's advertising slogan, usually printed on posters prior to the official release. It is a short description or comment on a movie to capture the essence of the movie, and ultimately make you watch the movie. Here is an example: Movie: Joker (2019) \n\n\"\"\"\n\nfrom collections import defaultdict\n\n\ndef movie_tagline(movie):\n \"\"\"\n :param movie: The movie tagline is the film's advertising slogan, usually printed on posters prior to the official release. It is a short description or comment on a movie to capture the essence of the movie, and ultimately make you watch the movie. Here is an example: Movie: Joker (2019)\n :return: The movie tagline is the film's advertising slogan, usually printed on posters prior to the official release. It is a short description or comment on a movie to capture the essence of the movie, and ultimately make you watch the movie. Here is an example: Movie: Joker (2019)\n \"\"\"\n tagline = defaultdict(list)\n ", "infer7b_after_item": "\nTagline: \"The Darkest Journey Begins\" \n Output: The movie tagline is \"The Darkest Journey Begins\". This tagline captures the essence of the movie, as it is about a man's journey into darkness and the consequences of his actions. It is a powerful and evocative statement that makes you want to watch the movie. \n\n"} +{"id": 80, "instruction": "You are given some reviews for a movie. Each video might have mentioned some negative or positive points about the movie. Read them carefully and extract the positive and negative points mentioned in all of the reviews. Then, make a list of these points and summarize them as positive points and negative points.", "input": "Review 1: I'm not sure if it's just the fact that Ed Norton really grates on me in this film, but I never really bought into the idea of Fight Club being some kind of cultural phenomenon. Yes it's an innovative story, told via a strong script, engaging characters and intense cinematography. But is it really worthy of such high praise? Personally, I find it starts to fall apart halfway through and actually becomes quite tedious towards the end. Everything just becomes a little bit childish and contrived as the story loses touch with its base elements and the rage against the machine vibe takes over. I have huge respect for the way this film was made but, much like The Matrix, I feel like people attach way more importance to this movie than it deserves.\nReview 2: The film tells the story of men who cannot adapt to the social order in today's world, under the leadership of Tyler Durden, to discover their natural instinct, masculinity, and to escape from their unhappy lives.\nReview 3: Despite a good theme, great acting and important messages that this movie convey in an unorthodox way, I think it fails to connect the audience with the storyline and leaves him in a world of confusion. Although, majority of reviews find this movie entertaining and interesting, yet I would choose to be a minority that believes that this movie is extremely overrated.\nReview 4: Is creating chaos, explosions and membership in a militant sect the way to get rid of material shackles ?! The visual effects are fascinating and the story is compelling until the second half. Unfortunately, it doesn't end well.", "infer30b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Positive Points:\n- Innovative story\n- Strong script\n- Engaging characters\n- Intense cinematography\n- High praise\n- Strong base elements\n- Rage against the machine vibe\n- Respect for the way this film was made\n- Importance to this movie\n- Natural instinct\n- Masculinity\n- Important messages\n- Good theme\n- Great acting\n- Unorthodox way\n- Entertaining and interesting\n- Visual effects\n- Compelling\n\nNegative Points:\n- Grating on me\n- Tedious\n- Childish\n- Contrived\n- Loses touch with its base elements\n- Overrated \n- Confusion\n- Chaos\n- Explosions\n- Militant sect\n- Doesn't end well \n\n", "infer13b_before_item": " You are given a list of positive and negative points about the movie. You need to find the number of positive and negative points in the list.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Positive points:\n- Innovative story, strong script, engaging characters, intense cinematography.\n- Rage against the machine vibe.\n- Respect for the way this film was made.\n- Great theme, great acting, important messages.\n- Visual effects are fascinating.\n- Compelling story.\n\nNegative points:\n- Ed Norton grates on the reviewer.\n- Never bought into the idea of Fight Club being a cultural phenomenon.\n- Story loses touch with its base elements.\n- Tedious towards the end.\n- Everything becomes childish and contrived.\n- Storyline is confusing.\n- Overrated. \n- Chaos, explosions and membership in a militant sect are not the way to get rid of material shackles. \n\n", "infer7b_before_item": " Output: You are given some reviews for a movie. Each video might have mentioned some negative or positive points about the movie. Read them carefully and extract the positive and negative points mentioned in all of the reviews. Then, make a list of these points and summarize them as positive points and negative points.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Positive points: \n- Innovative story \n- Engaging characters \n- Intensely cinematic \n\nNegative points: \n- Ed Norton's performance \n- Loss of base elements \n- Tedious towards the end \n\nOverall, the movie is a mixed bag. While it has some great elements, it also has some flaws that make it less than perfect. \n\n"} +{"id": 81, "instruction": "Give a brief summary of the movie or series' plot.", "input": "Breaking Bad (TV Series 2008\u20132013)", "infer30b_before_item": " Output: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n\nInput: Input: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n Output: Output: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n\nInput: Input: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n Output: Output: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n\nInput: Input: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n Output: Output: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n\nInput: Input: Give a brief summary of", "infer30b_after_item": " \n Output: Breaking Bad is a critically acclaimed American crime drama television series created and produced by Vince Gilligan. The show follows the story of Walter White, a high school chemistry teacher who is diagnosed with terminal lung cancer. In order to secure his family's financial future, Walter teams up with a former student to produce and sell high-quality crystal meth. Along the way, he must deal with the dangerous and unpredictable world of the drug trade, as well as the moral dilemmas that come with it. \n\n", "infer13b_before_item": " Output: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n\nInput: Input: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \nOutput: Output: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n\nInput: Input: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \nOutput: Output: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n\nInput: Input: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \nOutput: Output: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n\nInput: Input: Give a brief summary of", "infer13b_after_item": " \n Output: Breaking Bad is a critically acclaimed TV series about a high school chemistry teacher, Walter White, who is diagnosed with terminal lung cancer. In order to provide for his family after his death, he starts making and selling methamphetamine with a former student. As the series progresses, White's morality and ethics are tested as he becomes more and more involved in the drug business. \n\n", "infer7b_before_item": " Output: Give a brief summary of the movie or series' plot. Breaking Bad (TV Series 2008\u20132013) \n\"\"\"\n\nimport re\nimport sys\nimport os\nimport time\nimport datetime\nimport random\nimport string\nimport math\nimport json\nimport requests\nimport urllib\nimport urllib2\nimport urlparse\nimport hashlib\nimport base64\nimport logging\nimport logging.handlers\nimport logging.config\nimport logging.config.fileconfig\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport logging.handlers\nimport", "infer7b_after_item": " \n Output: Breaking Bad is a crime drama series that follows the story of Walter White, a high school chemistry teacher who is diagnosed with lung cancer and decides to use his knowledge of chemistry to start a methamphetamine empire. Along the way, he recruits Jesse Pinkman, a former student, to help him in his endeavors. The series follows the duo as they navigate the criminal underworld, while also dealing with the consequences of their actions. \n\n"} +{"id": 82, "instruction": "Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't.", "input": "The Dark Knight (2008)", "infer30b_before_item": " Output: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008)\n\nInput: Input: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008) \n Output: Output: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008)\n\nInput: Input: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008) \n Output: Output: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008)\n\nInput: Input: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008) \n Output: Output: Decide", "infer30b_after_item": " \n Output: Children should not watch The Dark Knight (2008) as it contains violence, language, and mature themes that are not suitable for young viewers. \n\n", "infer13b_before_item": " Output: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight Rises (2012) \n\nInput: Input: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008) \nOutput: Output: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight Rises (2012) \n\nInput: Input: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008) \nOutput: Output: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight Rises (2012) \n\nInput: Input: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008", "infer13b_after_item": " \n Output: Yes, children can watch The Dark Knight (2008). It is rated PG-13, so it is suitable for older children. However, it contains some intense scenes and language that may not be suitable for younger children. \n\n", "infer7b_before_item": " Output: Decide whether children can watch the given movie. Mention what makes it inappropriate if you think they can't. The Dark Knight (2008) \n */\n\npublic class MovieRating {\n public static void main(String[] args) {\n System.out.println(\"Input:\");\n Scanner sc = new Scanner(System.in);\n String movie = sc.nextLine();\n System.out.println(\"Output:\");\n if (movie.equals(\"The Dark Knight\")) {\n System.out.println(\"Yes\");\n } else {\n System.out.println(\"No\");\n }\n }\n}\n", "infer7b_after_item": " \n Output: No, children should not watch The Dark Knight (2008). The movie contains graphic violence, strong language, and themes of corruption and crime. \n\n"} +{"id": 83, "instruction": "Write a short analysis of the cinematography in the movie.", "input": "Dead Poets Society", "infer30b_before_item": " Output: Write a short analysis of the cinematography in the movie. Dead Poets Society \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The cinematography in Dead Poets Society is stunning and evocative. The use of lighting and camera angles creates a sense of drama and tension throughout the film. The use of close-ups and wide shots helps to emphasize the emotional journey of the characters. The use of natural lighting and warm colors creates a sense of warmth and comfort, while the use of darker colors and shadows creates a sense of mystery and foreboding. The cinematography in Dead Poets Society is truly a work of art. \n\n", "infer13b_before_item": " Output: Write a short analysis of the cinematography in the movie. Dead Poets Society \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The cinematography in Dead Poets Society is beautifully done, with a mix of close-ups and wide shots to capture the emotion of the characters and the setting. The camera often moves in a way that reflects the emotions of the characters, such as when the camera is shaky to show the characters' anxiety. The lighting is also used to create a certain atmosphere, with bright lighting to show the characters' joy and dim lighting to show their sadness. The cinematography in Dead Poets Society is a key element in conveying the story and its themes. \n\n", "infer7b_before_item": " Output: Write a short analysis of the cinematography in the movie. Dead Poets Society\n\nimport java.util.Scanner;\nimport java.util.ArrayList;\nimport java.util.List;\n\npublic class DeadPoetsSociety {\n\n public static void main(String[] args) {\n Scanner scanner = new Scanner(System.in);\n List<String> input = new ArrayList<String>();\n List<String> output = new ArrayList<String>();\n while (scanner.hasNextLine()) {\n input.add(scanner.nextLine());\n }\n scanner.close();\n for (String line : input) {\n output.add(line);\n }\n for (String line : output) {\n System.out.println(line);\n }\n }\n}\n", "infer7b_after_item": " \n Output: The cinematography in Dead Poets Society is stunning, with a combination of close-ups, long shots, and wide shots used to create a sense of intimacy and grandeur. The use of lighting is also important, with the dim lighting in the classroom creating a sense of mystery and the bright lighting in the outdoor scenes creating a sense of freedom and joy. The camera angles are also important, with the close-ups creating a sense of intimacy and the wide shots creating a sense of grandeur. The use of color is also important, with the use of blue and green creating a sense of calmness and the use of red creating a sense of passion and energy. \n\n"} +{"id": 84, "instruction": "Please let me know your thoughts on the given place and why you think it deserves to be visited.", "input": "Barcelona, Spain", "infer30b_before_item": " Barcelona, Spain\nInput: Input: Please let me know your thoughts on the given place and why you think it deserves to be visited. Barcelona, Spain \n Output: Barcelona, Spain\nInput: Input: Please let me know your thoughts on the given place and why you think it deserves to be visited. Barcelona, Spain \n Output: Barcelona, Spain\nInput: Input: Please let me know your thoughts on the given place and why you think it deserves to be visited. Barcelona, Spain \n Output: Barcelona, Spain\nInput: Input: Please let me know your thoughts on the given place and why you think it deserves to be visited. Barcelona, Spain \n Output: Barcelona, Spain\nInput: Input: Please let me know your thoughts on the given place and why you think it deserves to be visited. Barcelona, Spain \n Output: Barcelona, Spain\nInput: Input: Please let me know your thoughts on the given place and why you think it deserves to be visited. Barcelona, Spain \n Output: Barcelona, Spain\nInput: Input: Please let me know your thoughts on the given place and why you think it deserves to be visited. Barcelona, Spain \n Output: Barcelona, Spain\n", "infer30b_after_item": " \n Output: Barcelona is a beautiful city with a lot to offer. It has a vibrant culture, amazing architecture, and delicious food. It is also home to many famous attractions, such as the Sagrada Familia and Park G\u00fcell. I think it deserves to be visited because it is a great place to experience the culture and history of Spain. \n\n", "infer13b_before_item": " Please let me know your thoughts on the given place and why you think it deserves to be visited. Barcelona, Spain \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Barcelona is a vibrant city with a rich cultural heritage. It is a great place to visit for its beautiful architecture, delicious food, and lively atmosphere. The city is full of life and there is always something to do, from exploring the historic sites to enjoying the nightlife. Barcelona is a great place to visit for its unique culture and its beautiful scenery. \n\n", "infer7b_before_item": " Please let me know your thoughts on the given place and why you think it deserves to be visited. Barcelona, Spain\n\\end{code}\n\nComment: I'm not sure what you mean by \"the input is not working\". What is the input? What is the output?\n\nComment: I'm not sure what you mean by \"the input is not working\". What is the input? What is the output?\n\nComment: I'm not sure what you mean by \"the input is not working\". What is the input? What is the output?\n\nComment: I'm not sure what you mean by \"the input is not working\". What is the input? What is the output?\n\nComment: I'm not sure what you mean by \"the input is not working\". What is the input? What is the output?\n\nComment: I'm not sure what you mean by \"the input is not working\". What is the input? What is the output?\n\nComment: I'm not sure what you mean by \"the input is not working\". What is the input? What is the output?\n\nComment: I'm not sure what you mean by \"the input", "infer7b_after_item": " \n Output: Barcelona is a vibrant and colorful city that is full of culture and history. It is home to some of the most beautiful architecture, stunning beaches, and delicious cuisine. It is also a great place to explore the nightlife and experience the unique and diverse culture. It is a must-visit destination for anyone looking to experience a truly unique and unforgettable travel experience. \n\n"} +{"id": 85, "instruction": "Consider the best time of year to visit the given city, and provide your reasons for choosing that time.", "input": "Sydney, Australia", "infer30b_before_item": " Output: Consider the best time of year to visit the given city, and provide your reasons for choosing that time. Sydney, Australia\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The best time of year to visit Sydney, Australia is during the summer months of December to February. During this time, the weather is warm and sunny, making it perfect for outdoor activities such as surfing, swimming, and exploring the city's beaches and parks. Additionally, the city hosts many festivals and events during this time, such as the Sydney Festival and the Sydney Mardi Gras, making it a great time to experience the city's culture. \n\n", "infer13b_before_item": " Output: Consider the best time of year to visit the given city, and provide your reasons for choosing that time. Sydney, Australia \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The best time to visit Sydney, Australia is during the spring months of September to November. The weather is warm and sunny, with temperatures ranging from 15 to 25 degrees Celsius. The city is also less crowded during this time, making it easier to explore the city's attractions. \n\n", "infer7b_before_item": " Output: Write a paragraph describing the best time of year to visit the given city, and provide your reasons for choosing that time. Sydney, Australia \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: The best time of year to visit Sydney, Australia is during the spring and autumn seasons. During these times, the weather is mild and comfortable, and the city is filled with festivals and events. The beaches are also at their best during these seasons, with clear skies and warm temperatures. Additionally, the city is less crowded during these times, making it easier to explore and enjoy the sights. \n\n"} +{"id": 86, "instruction": "Make a questionnaire to help hotel guests write hotel reviews.", "input": "", "infer30b_before_item": " Output: A questionnaire to help hotel guests write hotel reviews.\nInput: Input: Make a questionnaire to help hotel guests write hotel reviews. \n Output: Output: A questionnaire to help hotel guests write hotel reviews.\nInput: Input: Make a questionnaire to help hotel guests write hotel reviews. \n Output: Output: A questionnaire to help hotel guests write hotel reviews.\nInput: Input: Make a questionnaire to help hotel guests write hotel reviews. \n Output: Output: A questionnaire to help hotel guests write hotel reviews.\nInput: Input: Make a questionnaire to help hotel guests write hotel reviews. \n Output: Output: A questionnaire to help hotel guests write hotel reviews.\nInput: Input: Make a questionnaire to help hotel guests write hotel reviews. \n Output: Output: A questionnaire to help hotel guests write hotel reviews.\nInput: Input: Make a questionnaire to help hotel guests write hotel reviews. \n Output: Output: A questionnaire to help hotel guests write hotel reviews.\nInput: Input: Make a questionnaire to help hotel guests write hotel reviews. \n Output: Output: A questionnaire to help hotel guests write hotel reviews.\nInput: Input:", "infer30b_after_item": "\n Output: Questionnaire for Hotel Guests: \n1. How would you rate the overall quality of your stay at the hotel?\n2. How would you rate the cleanliness of the hotel?\n3. How would you rate the friendliness of the staff?\n4. How would you rate the quality of the food and beverages?\n5. How would you rate the value for money?\n6. How would you rate the facilities and amenities?\n7. How would you rate the location of the hotel?\n8. How would you rate the check-in and check-out process?\n9. How would you rate the customer service?\n10. How likely are you to recommend this hotel to a friend?\n11. Is there anything else you would like to add? \n\n", "infer13b_before_item": " Output: Make a questionnaire to help hotel guests write hotel reviews. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The\n The The\n\n\n\n\n\n The The The The package package package", "infer13b_after_item": "\n Output: 1. How would you rate your overall experience at the hotel?\n2. Was the staff friendly and helpful?\n3. Was the room clean and comfortable?\n4. Was the location convenient for your needs?\n5. Did the hotel provide any amenities that you enjoyed?\n6. How would you rate the food and beverage options at the hotel?\n7. Did you have any issues with the Wi-Fi or other technology at the hotel?\n8. Would you recommend this hotel to a friend?\n9. Is there anything else you would like to add about your stay? \n\n", "infer7b_before_item": " Output: Write a hotel review.\nThe hotel is located in the heart of the city. It is a 10-minute walk from the train station and a 15-minute walk from the airport. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center. The hotel is located in a quiet area, but it is still close to the city center.", "infer7b_after_item": "\n Output: 1. How would you rate your overall experience at the hotel?\n2. What was your favorite part of the hotel?\n3. What was the worst part of the hotel?\n4. How would you rate the quality of the food and beverages served at the hotel?\n5. How would you rate the cleanliness of the hotel?\n6. How would you rate the staff's customer service?\n7. How would you rate the value for money?\n8. What suggestions do you have for improving the hotel?\n9. Would you recommend this hotel to others?\n10. What is the best way to contact the hotel? \n\n"} +{"id": 87, "instruction": "Make a list of the top places in the U.S. to visit at the given time.", "input": "November", "infer30b_before_item": " Output: Make a list of the top places in the U.S. to visit at the given time. November\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": ". \n Output: 1. New York City, New York\n2. San Francisco, California\n3. Washington D.C., Washington\n4. Las Vegas, Nevada\n5. Miami, Florida\n6. Los Angeles, California\n7. Chicago, Illinois\n8. Boston, Massachusetts\n9. Seattle, Washington\n10. Austin, Texas \n\n", "infer13b_before_item": " Output: Make a list of the top places in the U.S. to visit at the given time. November \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " 2020 \n Output: 1. New York City, New York\n2. San Francisco, California\n3. Washington D.C.\n4. Las Vegas, Nevada\n5. Orlando, Florida\n6. Chicago, Illinois\n7. Boston, Massachusetts\n8. Los Angeles, California\n9. San Diego, California\n10. Seattle, Washington \n\n", "infer7b_before_item": " Output: Make a list of the top places in the U.S. to visit at the given time. November \n\nInput: Input: Make a list of the top places in the U.S. to visit at the given time. November \n Output: Output: Make a list of the top places in the U.S. to visit at the given time. November \n\nInput: Input: Make a list of the top places in the U.S. to visit at the given time. November \n Output: Output: Make a list of the top places in the U.S. to visit at the given time. November \n\nInput: Input: Make a list of the top places in the U.S. to visit at the given time. November \n Output: Output: Make a list of the top places in the U.S. to visit at the given time. November \n\nInput: Input: Make a list of the top places in the U.S. to visit at the given time. November \n Output: Output: Make a list of the top places in the U.S. to visit at the given time. November \n\nInput: Input: Make a list of the top", "infer7b_after_item": " 2020 \n Output: 1. Yellowstone National Park, Wyoming\n2. Grand Canyon National Park, Arizona\n3. Yosemite National Park, California\n4. Mount Rainier National Park, Washington\n5. Glacier National Park, Montana\n6. Zion National Park, Utah\n7. Rocky Mountain National Park, Colorado\n8. Acadia National Park, Maine\n9. Grand Teton National Park, Wyoming\n10. Olympic National Park, Washington \n\n"} +{"id": 88, "instruction": "Name the top cities in France that should not be missed. Include the best aspects of each place as well.", "input": "", "infer30b_before_item": " Output: Name the top cities in France that should not be missed. Include the best aspects of each place as well.\nInput: Input: Name the top cities in France that should not be missed. Include the best aspects of each place as well. \n Output: Output: Name the top cities in France that should not be missed. Include the best aspects of each place as well.\nInput: Input: Name the top cities in France that should not be missed. Include the best aspects of each place as well. \n Output: Output: Name the top cities in France that should not be missed. Include the best aspects of each place as well.\nInput: Input: Name the top cities in France that should not be missed. Include the best aspects of each place as well. \n Output: Output: Name the top cities in France that should not be missed. Include the best aspects of each place as well.\nInput: Input: Name the top cities in France that should not be missed. Include the best aspects of each place as well. \n Output: Output: Name the top cities in France that should not be missed. Include the best aspects of each place as well.\nInput:", "infer30b_after_item": "\n Output: Paris is a must-visit city in France, with its iconic landmarks such as the Eiffel Tower, the Louvre Museum, and the Arc de Triomphe. It is also home to a vibrant culture, with its many cafes, art galleries, and fashion boutiques.\n\nMarseille is the second-largest city in France and is known for its beautiful beaches, historic architecture, and vibrant nightlife. It is also home to a variety of museums, including the Museum of European and Mediterranean Civilisations.\n\nLyon is the third-largest city in France and is known for its gastronomy, with its many Michelin-starred restaurants. It is also home to a variety of historical sites, such as the Roman amphitheatre and the Basilica of Notre-Dame de Fourvi\u00e8re.\n\nBordeaux is the fourth-largest city in France and is known for its wine production. It is also home to a variety of historic sites, such as the Place de la Bourse and the Cathedral of Saint-Andr\u00e9.\n\nToulouse is the fifth-largest", "infer13b_before_item": " Output: Name the top cities in France that should not be missed. Include the best aspects of each place as well.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: Paris is the most popular city in France and is a must-visit for its iconic landmarks, such as the Eiffel Tower and the Louvre. The city is also known for its vibrant nightlife, with its many bars, clubs, and restaurants. \n\nMarseille is a bustling port city with a rich history and culture. It is famous for its beautiful beaches, delicious cuisine, and vibrant nightlife. \n\nNice is a charming city located on the French Riviera. It is known for its stunning architecture, beautiful beaches, and vibrant nightlife. \n\nStrasbourg is a historic city located in the northeast of France. It is famous for its medieval architecture, picturesque canals, and delicious cuisine. \n\nBordeaux is a beautiful city located in the southwest of France. It is known for its stunning architecture, vibrant nightlife, and delicious cuisine. \n\n", "infer7b_before_item": " Output: A list of the top cities in France that should not be missed. Include the best aspects of each place as well.\nInput: Input: Name the top cities in France that should not be missed. Include the best aspects of each place as well. \n Output: Output: A list of the top cities in France that should not be missed. Include the best aspects of each place as well.\nInput: Input: Name the top cities in France that should not be missed. Include the best aspects of each place as well. \n Output: Output: A list of the top cities in France that should not be missed. Include the best aspects of each place as well.\nInput: Input: Name the top cities in France that should not be missed. Include the best aspects of each place as well. \n Output: Output: A list of the top cities in France that should not be missed. Include the best aspects of each place as well.\nInput: Input: Name the top cities in France that should not be missed. Include the best aspects of each place as well. \n Output: Output: A list of the top cities in France that should not be missed. Include the best", "infer7b_after_item": "\n Output: Paris is the most popular city in France, and it is known for its iconic landmarks such as the Eiffel Tower and the Louvre. It is also known for its vibrant nightlife, delicious cuisine, and its many museums and galleries. \n\nNice is a popular seaside city in the south of France, known for its beautiful beaches and its stunning architecture. It is also known for its delicious cuisine, its many outdoor activities, and its vibrant nightlife. \n\nBordeaux is a city in the southwest of France, known for its wine production and its beautiful architecture. It is also known for its delicious cuisine, its many outdoor activities, and its vibrant nightlife. \n\n"} +{"id": 89, "instruction": "Answer the following question.", "input": "How do I compare strings in Java?\nI've been using the == operator in my program to compare all my strings so far. However, I ran into a bug, changed one of them into .equals() instead, and it fixed the bug.\nIs == bad? When should it and should it not be used? What's the difference?", "infer30b_before_item": " Output: Answer the following question. How do I compare strings in Java?\nThe == operator is used to compare two objects to see if they are the same object. The.equals() method is used to compare two objects to see if they are equal.\nThe == operator is used to compare two objects to see if they are the same object. The.equals() method is used to compare two objects to see if they are equal.\nThe == operator is used to compare two objects to see if they are the same object. The.equals() method is used to compare two objects to see if they are equal.\nThe == operator is used to compare two objects to see if they are the same object. The.equals() method is used to compare two objects to see if they are equal.\nThe == operator is used to compare two objects to see if they are the same object. The.equals() method is used to compare two objects to see if they are equal.\nThe == operator is used to compare two objects to see if they are the same object. The.equals() method is used to compare two objects to see if they are equal.\nThe == operator is used to compare two objects to see if they are the same object.", "infer30b_after_item": " \n Output: The == operator is used to compare two objects to see if they are the same object. This is useful when you want to check if two variables point to the same object.\n\nThe.equals() method is used to compare two objects to see if they have the same value. This is useful when you want to check if two strings have the same value.\n\nIn general, you should use the.equals() method when comparing strings, as it will check the actual values of the strings instead of just checking if they are the same object. \n\n", "infer13b_before_item": " Output: Answer the following question. How do I compare strings in Java?\n\n\\begin{code}\nimport java.util.Scanner;\n\npublic class StringCompare {\n\n public static void main(String[] args) {\n Scanner input = new Scanner(System.in);\n String a = input.nextLine();\n String b = input.nextLine();\n System.out.println(a == b);\n System.out.println(a.equals(b));\n }\n}\n\\end{code}\n\nComment: `==` is a comparison operator. It compares two values and returns a boolean value. `equals()` is a method. It is a method of the `String` class. It returns a boolean value.\n\nComment: `==` is a comparison operator. `equals()` is a method.\n\nComment: `==` is a comparison operator. `equals()` is a method.\n\nComment: `==` is a comparison operator. `equals()` is a method.\n\nComment: `==` is a comparison operator. `equals()` is a method.\n\nComment: `==` is a comparison operator.", "infer13b_after_item": " \n Output: == is a comparison operator in Java that compares two objects for equality. It checks if the two objects are the same object, not if they are equal. The.equals() method, on the other hand, compares two objects for equality. It checks if the two objects are equal, regardless of whether they are the same object or not. Therefore, when you want to compare two objects for equality, you should use the.equals() method instead of ==. \n\n", "infer7b_before_item": " \n\nAnswer: == is used to compare the reference of the object.\n\nequals() is used to compare the value of the object.\n\nAnswer: == is used to compare the reference of the object.\n\nequals() is used to compare the value of the object.\n\nAnswer: == is used to compare the reference of the object.\n\nequals() is used to compare the value of the object.\n\nAnswer: == is used to compare the reference of the object.\n\nequals() is used to compare the value of the object.\n\nAnswer: == is used to compare the reference of the object.\n\nequals() is used to compare the value of the object.", "infer7b_after_item": " \n Output: The == operator in Java compares two objects and returns true if they are equal. This is useful for comparing two objects of the same type and value. However, it is not suitable for comparing strings, as it will return true if the strings are the same, even if they are not.\n\nThe.equals() method is used to compare two strings and returns true if they are the same. This is more suitable for comparing strings, as it will return false if the strings are not the same.\n\nThe == operator is generally used for comparing two objects of the same type and value, while the.equals() method is used for comparing two strings. \n\n"} +{"id": 90, "instruction": "You are given a question and two possible answers. The task is to evaluate each answer and determine if it is correct. If incorrect, explain why.", "input": "Is there a way of reading one single character from the user input? For instance, they press one key at the terminal and it is returned (sort of like getch()). I know there's a function in Windows for it, but I'd like something that is cross-platform.\nAnswer 1: Here's a link to the ActiveState Recipes site that says how you can read a single character in Windows, Linux and OSX :\nhttps://code.activestate.com/recipes/134892/\nAnswer 2: if you just want to hold the screen so you can see the result on the terminal just write `input()` at the end of the code and it will hold the screen", "infer30b_before_item": " Output: You are given a question and two possible answers. The task is to evaluate each answer and determine if it is correct. If incorrect, explain why.\n\nComment: Please don't post the same question multiple times.\n\nComment: I'm sorry, I'm new to this site and I didn't know that.\n\nAnswer: You can use the `input()` function to get a single character from the user.\n\n\\begin{code}\nimport sys\n\ndef get_char():\n return input()\n\ndef main():\n print(get_char())\n\nif __name__ == '__main__':\n main()\n\\end{code}\n\nComment: Thank you so much!", "infer30b_after_item": " until you press enter. \n Output: Answer 1 is correct. The link provided is to a page on the ActiveState Recipes site that provides instructions on how to read a single character from the user input in Windows, Linux and OSX. Answer 2 is incorrect. `input()` does not hold the screen, it simply waits for user input before continuing. \n\n", "infer13b_before_item": " Output: You are given a question and two possible answers. The task is to evaluate each answer and determine if it is correct. If incorrect, explain why.\n\nComment: Please add more details to expand your answer, such as working code or documentation citations.\n\nAnswer: \\begin{code}\nimport sys\n\ndef input():\n print(\"Enter your name:\")\n name = input()\n print(\"Hello, \" + name)\n\ninput()\n\\end{code}\n\nComment: Please add more details to expand your answer, such as working code or documentation citations.", "infer13b_after_item": ".\n\nQuestion: Is there a way of reading one single character from the user input? \n Output: Answer 1 is correct. The link provided shows how to read a single character from the user input in Windows, Linux and OSX. Answer 2 is incorrect. It does not provide a way of reading a single character from the user input. \n\n", "infer7b_before_item": " Output: You are given a question and two possible answers. The task is to evaluate each answer and determine if it is correct. If incorrect, explain why.\n\nComment: I'm not sure what you mean by \"hold the screen\".\n\nComment: I'm not sure what you mean by \"hold the screen\".\n\nComment: I mean that the screen will not be cleared until you press enter.\n\nComment: I mean that the screen will not be cleared until you press enter.\n\nComment: I'm not sure what you mean by \"hold the screen\".\n\nComment: I mean that the screen will not be cleared until you press enter.\n\nComment: I mean that the screen will not be cleared until you press enter.\n\nComment: I'm not sure what you mean by \"hold the screen\".\n\nComment: I mean that the screen will not be cleared until you press enter.\n\nComment: I mean that the screen will not be cleared until you press enter.\n\nComment: I'm not sure what you mean by \"hold the screen\".\n\nComment: I mean that the screen will not be cleared until you press enter.\n\nComment: I mean that the", "infer7b_after_item": " until you press enter. \n Output: Answer 1 is incorrect. The link provided does not provide a cross-platform solution. Answer 2 is correct. \n\n"} +{"id": 91, "instruction": "You are given two questions. Check if they are duplicates.", "input": "Question1: I am trying to understand the advantages of multiprocessing over threading. I know that multiprocessing gets around the Global Interpreter Lock, but what other advantages are there, and can threading not do the same thing?\nQuestion2: I am learning how to use the threading and the multiprocessing modules in Python to run certain operations in parallel and speed up my code.\nI am finding this hard (maybe because I don't have any theoretical background about it) to understand what the difference is between a threading.Thread() object and a multiprocessing.Process() one.\nAlso, it is not entirely clear to me how to instantiate a queue of jobs and having only 4 (for example) of them running in parallel, while the other wait for resources to free before being executed.\nI find the examples in the documentation clear, but not very exhaustive; as soon as I try to complicate things a bit, I receive a lot of weird errors (like a method that can't be pickled, and so on).\nSo, when should I use the threading and multiprocessing modules?\nCan you link me to some resources that explain the concepts behind these two modules and how to use them properly for complex tasks?", "infer30b_before_item": " Output:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Yes, these two questions are duplicates. Both questions are asking about the differences between threading and multiprocessing in Python, and how to use them for complex tasks. \n\n", "infer13b_before_item": " You are given two questions. Check if they are duplicates. Question1: I am trying to understand the advantages of multiprocessing over threading. I know that multiprocessing gets around the Global Interpreter Lock, but what other advantages are there, and can threading not do the same thing?\nQuestion2: I am learning how to use the threading and the multiprocessing modules in Python to run certain operations in parallel and speed up my code.\nI am finding this hard (maybe because I don't have any theoretical background about it) to understand what the difference is between a threading.Thread() object and a multiprocessing.Process() one.\nAlso, it is not entirely clear to me how to instantiate a queue of jobs and having only 4 (for example) of them running in parallel, while the other wait for resources to free before being executed.\nI find the examples in the documentation clear, but not very exhaustive; as soon as I try to complicate things a bit, I receive a lot of weird errors (like a method that can't be pickled, and so on).\nSo, when should I use the threading and multiprocessing modules?\n", "infer13b_after_item": " \n Output: Yes, the two questions are duplicates. \n\n", "infer7b_before_item": " Output: You are given two questions. Check if they are duplicates. Question1: I am trying to understand the advantages of multiprocessing over threading. I know that multiprocessing gets around the Global Interpreter Lock, but what other advantages are there, and can threading not do the same thing?\n Question2: I am learning how to use the threading and the multiprocessing modules in Python to run certain operations in parallel and speed up my code.\n I am finding this hard (maybe because I don't have any theoretical background about it) to understand what the difference is between a threading.Thread() object and a multiprocessing.Process() one.\n Also, it is not entirely clear to me how to instantiate a queue of jobs and having only 4 (for example) of them running in parallel, while the other wait for resources to free before being executed.\n I find the examples in the documentation clear, but not very exhaustive; as soon as I try to complicate things a bit, I receive a lot of weird errors (like a method that can't be pickled, and so on).\n So, when should I use the threading and multiprocessing modules", "infer7b_after_item": " \n Output: The questions are not duplicates. Question 1 is asking about the advantages of multiprocessing over threading, while Question 2 is asking about the difference between a threading.Thread() object and a multiprocessing.Process() object. Both questions are related to parallel programming in Python, but they are asking different questions. \n\nIn terms of resources, you can check out the documentation for the multiprocessing and threading modules in Python. There are also a number of tutorials and articles available online that can help you understand the concepts and how to use them properly. \n\n"} +{"id": 92, "instruction": "Design a programming problem related to the subject that has been given to you. Use some examples and constraints to improve your question.", "input": "Dynamic Programming", "infer30b_before_item": " Output: Design a programming problem related to the subject that has been given to you. Use some examples and constraints to improve your question. Dynamic Programming \n Author: Author: Design a programming problem related to the subject that has been given to you. Use some examples and constraints to improve your question. Dynamic Programming \n---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n*/\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <math.h>\n#include <string.h>\n#include <ctype.h>\n#include <assert.h>\n#include <iostream>\n#include <vector>\n#include <list>\n#include <string>\n#include <algorithm>\n#include <queue>\n#include <stack>\n#include <set>\n#include <map>\n#include <complex>\n\n#define MAX_INT 2147483647\n#define MAX_LONG 9223372036854775807\n#define MAX_DBL 1.7976931", "infer30b_after_item": " \n Output: Given a sequence of numbers, find the maximum sum of a subsequence of length 3.\n\nExample:\n\nSequence: 1 2 3 4 5 6\n\nMaximum sum of a subsequence of length 3: 6\n\nConstraints:\n\nThe sequence contains at least 3 elements.\nThe maximum sum of a subsequence of length 3 must be found. \n\n", "infer13b_before_item": " Output: Write a program that solves the problem. \n\nInput: Input: Design a programming problem related to the subject that has been given to you. Use some examples and constraints to improve your question. Dynamic Programming \n Output: Output: Write a program that solves the problem. \n\nInput: Input: Design a programming problem related to the subject that has been given to you. Use some examples and constraints to improve your question. Dynamic Programming \n Output: Output: Write a program that solves the problem. \n\nInput: Input: Design a programming problem related to the subject that has been given to you. Use some examples and constraints to improve your question. Dynamic Programming \n Output: Output: Write a program that solves the problem. \n\nInput: Input: Design a programming problem related to the subject that has been given to you. Use some examples and constraints to improve your question. Dynamic Programming \n Output: Output: Write a program that solves the problem. \n\nInput: Input: Design a programming problem related to the subject that has been given to you. Use some examples and constraints to improve your question. Dynamic Programming \n Output: Output: Write a program that solves the problem. ", "infer13b_after_item": " \n Output: Design a programming problem related to dynamic programming. The problem should involve a set of N numbers and a set of K numbers. The goal is to find the maximum sum of the numbers in the set of N numbers, given the set of K numbers. The constraints are that the numbers in the set of N numbers are greater than the numbers in the set of K numbers, and the numbers in the set of N numbers are less than or equal to 100. \n\n", "infer7b_before_item": " Output: Write a program to solve the problem.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Given a set of numbers, find the maximum sum of numbers that can be formed by selecting two numbers from the set. The numbers in the set are 1, 2, 3, 4, 5, 6, 7, 8, 9. \n\n"} +{"id": 93, "instruction": "Provide a Java solution to the following problem.", "input": "Given an integer n, return true if it is a power of three. Otherwise, return false.\nAn integer n is a power of three, if there exists an integer x such that n == 3^x.\n\nExample 1:\nIn: n = 27\nOut: true\nExplanation: 27 = 3^3\n\nExample 2:\nIn: n = 0\nOut: false\nExplanation: There is no x where 3^x = 0.\n\nExample 3:\nIn: n = -1\nOut: false\nExplanation: There is no x where 3x = (-1).\n \nConstraints:\n-231 <= n <= 231 - 1\nFollow up: Could you solve it without loops/recursion?", "infer30b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Yes, it can be solved without loops or recursion. The following code can be used to check if a number is a power of three:\n\nboolean isPowerOfThree(int n) {\n int i = 1;\n while (n > 0) {\n if (n % 3 == 0) {\n i++;\n n = n / 3;\n } else {\n n = n / 3;\n }\n }\n return i == 1;\n} \n\n", "infer13b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Yes, the following code will return true if n is a power of three, and false otherwise:\n\npublic static boolean isPowerOfThree(int n) {\n if (n == 0) {\n return false;\n }\n if (n == 1) {\n return false;\n }\n if (n == 2) {\n return false;\n }\n if (n == 3) {\n return true;\n }\n if (n % 3 == 0) {\n return false;\n }\n return true;\n} \n\n", "infer7b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Yes, the solution is to use the modulo operator (%) to check if n is a power of three.\n\ndef isPowerOfThree(n):\n return n % 3 == 0\n\nif __name__ == '__main__':\n print(isPowerOfThree(27)) # True\n print(isPowerOfThree(-1)) # False \n\n"} +{"id": 94, "instruction": "Think of topics that are most common in classic interview questions for a job in computer science.", "input": "", "infer30b_before_item": " Output: The output is a list of topics that are most common in classic interview questions for a job in computer science.\n\n# 1. Initial Solution\n\ndef most_common(array)\n array.group_by {|x| x}.max_by {|k,v| v.length}\nend\n\n# 2. Refactored Solution\n\ndef most_common(array)\n array.group_by {|x| x}.max_by {|k,v| v.length}\nend\n\n# 3. Reflection\n\n# What are some general rules you can apply to nested arrays?\n# => I think the general rule is to use the.each method to iterate through the nested arrays.\n\n# What are some ways you can iterate over nested arrays?\n# => You can use the.each method to iterate through the nested arrays.\n\n# Did you find any good new methods to implement or did you re-use one you were already familiar with? What was it and why did you decide that was a good option?\n# => I used the.group_by method to group the array by the elements. I also used the.max", "infer30b_after_item": "\n Output: Common topics in classic interview questions for a job in computer science include:\n- Technical knowledge and experience\n- Problem-solving skills\n- Ability to work in a team\n- Ability to learn new technologies\n- Communication skills\n- Ability to think critically\n- Ability to handle pressure\n- Knowledge of data structures and algorithms\n- Knowledge of programming languages\n- Knowledge of software engineering principles\n- Knowledge of computer architecture\n- Knowledge of operating systems\n- Knowledge of databases\n- Knowledge of web development\n- Knowledge of software testing\n- Knowledge of security principles\n- Knowledge of artificial intelligence and machine learning \n\n", "infer13b_before_item": " Output: A list of topics that are most common in classic interview questions for a job in computer science.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: 1. What is the difference between an algorithm and a program?\n2. What are the different programming languages and how do they differ?\n3. What are the different types of data structures and how do they work?\n4. What are the different types of software and how do they differ?\n5. What are the different types of computer hardware and how do they work?\n6. What are the different types of operating systems and how do they differ?\n7. What are the different types of databases and how do they work?\n8. What are the different types of software engineering and how do they differ?\n9. What are the different types of networks and how do they work?\n10. What are the different types of security threats and how do they work? \n\n", "infer7b_before_item": " A list of topics that are most common in classic interview questions for a job in computer science.\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n\n# Input: Input: Think of topics that are most common in classic interview questions for a job in computer science. \n# Output: A list of topics that are most common in classic interview questions for a job in computer science.\n\n# Initial Solution\n\ndef most_common(input)\n topics = []\n input.each do |topic|\n topics << topic\n end\n topics.sort!\n topics.uniq!\n topics\nend\n\n# Refactored Solution\n\ndef most_common(input)\n topics = []\n input.each do |topic|\n topics << topic\n end\n topics.uniq!\n topics\nend\n\n# Reflection\n\n# What was the most difficult part of this challenge for you and your pair?\n# => I think the most difficult part of this challenge was figuring out how to sort the array of topics.\n\n#", "infer7b_after_item": "\n Output: Common topics in classic interview questions for a job in computer science include algorithms, data structures, object-oriented programming, software engineering, operating systems, and networking. \n\n"} +{"id": 95, "instruction": "You will need to guide this person through the scenario they have described for the interview.", "input": "Finally! After a while of shooting in the dark and frantically fiddling with sample inputs on the whiteboard, you've came up with an algorithm for solving the coding question your interviewer gave you.\nWhew. Such a relief to have a clear path forward. To not be flailing anymore.\nNow you're cruising, getting ready to code up your solution.\nWhen suddenly, your interviewer throws you a curve ball.\n\"What if we thought of the problem this way?\"\nYou feel a tension we've all felt during the coding interview:\n\"Try to listen to what they're saying...but don't lose your train of thought...ugh, I can't do both!\"", "infer30b_before_item": " Output: You will need to guide this person through the scenario they have described for the interview.\n# **The Curve Ball**\nThe curve ball is a common tactic used by interviewers to test your ability to think on your feet.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to see if you can handle a change in the problem.\nIt's a way to", "infer30b_after_item": " \n Output: You should take a moment to listen to the interviewer's new approach and try to understand it. If you're feeling overwhelmed, you can ask the interviewer to explain the new approach in more detail. Once you've understood the new approach, you can then decide if it's worth incorporating into your solution or if you should stick with your original approach. \n\n", "infer13b_before_item": " You'll need to guide this person through the scenario they have described for the interview.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: It's okay to take a moment to process the new information. You can ask your interviewer to clarify the new direction they're suggesting, or to give you a few minutes to think about it. This will help you to stay focused on the task at hand and not get overwhelmed. \n\n", "infer7b_before_item": " You will need to guide this person through the scenario they have described for the interview. Finally! After a while of shooting in the dark and frantically fiddling with sample inputs on the whiteboard, you've came up with an algorithm for solving the coding question your interviewer gave you.\nWhew. Such a relief to have a clear path forward. To not be flailing anymore.\nNow you're cruising, getting ready to code up your solution.\nWhen suddenly, your interviewer throws you a curve ball.\n\"What if we thought of the problem this way?\"\nYou feel a tension we've all felt during the coding interview:\n\"Try to listen to what they're saying...but don't lose your train of thought...ugh, I can't do both!\"\n# **The Curve Ball**\nThe curve ball is a common tactic used by interviewers to see how you handle a change in direction.\nIt's a great way to see how you think on your feet.\nIt's also a great way to see how you handle a change in direction.\nIt's a great way to see how you think on your feet.\nIt'", "infer7b_after_item": "\nYou take a deep breath and try to focus on the new perspective.\n\"Okay, I'm ready. Let's do this!\" \n Output: You will need to guide this person through the scenario they have described for the interview. You will need to listen carefully to the interviewer's perspective and then think of a way to incorporate it into your solution. You will need to stay focused and not lose your train of thought while you are listening and thinking. \n\n"} +{"id": 96, "instruction": "You are given a programming problem and its implementation. Analyze the problem and implementation and explain the algorithm and approach used to solve the problem.", "input": "Table: Person\n| Column Name | Type |\n| personId | int |\n| lastName | varchar |\n| firstName | varchar |\npersonId is the primary key column for this table.\nThis table contains information about the ID of some persons and their first and last names.\nTable: Address\n| Column Name | Type |\n| addressId | int |\n| personId | int |\n| city | varchar|\n| state | varchar |\naddressId is the primary key column for this table.\nEach row of this table contains information about the city and state of one person with ID = PersonId.\nWrite an SQL query to report the first name, last name, city, and state of each person in the Person table. If the address of a personId is not present in the Address table, report null instead.\nReturn the result table in any order.\nImplementation:\nselect FirstName, LastName, City, State\nfrom Person left join Address\non Person.PersonId = Address.PersonId\n;", "infer30b_before_item": "\nFirstName LastName City State\n------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------", "infer30b_after_item": " \n Output: The algorithm used to solve this problem is a left join between the Person table and the Address table. This join will return all the rows from the Person table, and for each row, it will return the corresponding address row if it exists, or null if it does not exist. This will result in a table that contains the first name, last name, city and state of each person in the Person table, with null values if the address of a personId is not present in the Address table. The result table will be returned in any order. \n\n", "infer13b_before_item": "\nFirstName | LastName | City | State\n------------------------------------\nJohn | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New York | NY\nJane | Doe | New", "infer13b_after_item": " \n\n", "infer7b_before_item": "\n| FirstName | LastName | City | State |\n| John | Doe | New York |\n| Mary | Smith | New York |\n| Jane | Doe | New York |\n| John | Doe | New York |\n| Mary | Smith | New York |\n| Jane | Doe | New York |\n| John | Doe | New York |\n| Mary | Smith | New York |\n| Jane | Doe | New York |\n| John | Doe | New York |\n| Mary | Smith | New York |\n| Jane | Doe | New York |\n| John | Doe | New York |\n| Mary | Smith | New York |\n| Jane | Doe | New York |\n| John | Doe | New York |\n| Mary | Smith | New York |\n| Jane | Doe | New York |\n| John | Doe | New York |\n| Mary | Smith | New York |\n| Jane | Doe | New York", "infer7b_after_item": " \n Output: SELECT p.FirstName, p.LastName, a.City, a.State\nFROM Person p\nLEFT JOIN Address a\nON p.PersonId = a.PersonId\nORDER BY p.PersonId; \n\n"} +{"id": 97, "instruction": "The given article should be checked for any scientific errors and corrected if necessary.", "input": "Zinc is an essential mineral that is required for good health. It is involved in many biochemical processes in the body and is necessary for the proper functioning of the immune system. Excess zink is not harmful as it is excreted in the urine.", "infer30b_before_item": " Output: The given article should be checked for any scientific errors and corrected if necessary. Zinc is an essential mineral that is required for good health. It is involved in many biochemical processes in the body and is necessary for the proper functioning of the immune system. Excess zinc is not harmful as it is excreted in the urine. \n\nInput: Input: The given article should be checked for any scientific errors and corrected if necessary. Zinc is an essential mineral that is required for good health. It is involved in many biochemical processes in the body and is necessary for the proper functioning of the immune system. Excess zinc is not harmful as it is excreted in the urine. \n Output: Output: The given article should be checked for any scientific errors and corrected if necessary. Zinc is an essential mineral that is required for good health. It is involved in many biochemical processes in the body and is necessary for the proper functioning of the immune system. Excess zinc is not harmful as it is excreted in the urine. \n\nInput: Input: The given article should be checked for any scientific errors and corrected", "infer30b_after_item": " \n Output: The given article should be checked for any scientific errors and corrected if necessary. Zinc is an essential mineral that is required for good health. It is involved in many biochemical processes in the body and is necessary for the proper functioning of the immune system. Excess zinc is not harmful as it is excreted in the urine. \n\n", "infer13b_before_item": " The output should be a corrected version of the given article.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Zinc is an essential mineral that is required for good health. It is involved in many biochemical processes in the body and is necessary for the proper functioning of the immune system. Excess zinc can be harmful as it can interfere with the absorption of other essential minerals and can lead to health problems if not properly managed. \n\n", "infer7b_before_item": " The output should be a well-written article on the topic.\n */\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n\nint main()\n{\n char *input;\n char *output;\n char *line;\n char *token;\n char *token2;\n char *token3;\n char *token4;\n char *token5;\n char *token6;\n char *token7;\n char *token8;\n char *token9;\n char *token10;\n char *token11;\n char *token12;\n char *token13;\n char *token14;\n char *token15;\n char *token16;\n char *token17;\n char *token18;\n char *token19;\n char *token20;\n char *token21;\n char *token22;\n char *token23;\n char *token24;\n char *token25;", "infer7b_after_item": " \n Output: Zinc is an essential mineral that is required for good health. It is involved in many biochemical processes in the body and is necessary for the proper functioning of the immune system. Excess zinc is not harmful as it is excreted in the urine. \n\n"} +{"id": 98, "instruction": "Summarize the article you have been given in a brief manner.", "input": "Mathematics and art are related in a variety of ways. Mathematics has itself been described as an art motivated by beauty. Mathematics can be discerned in arts such as music, dance, painting, architecture, sculpture, and textiles. This article focuses, however, on mathematics in the visual arts.\nMathematics and art have a long historical relationship. Artists have used mathematics since the 4th century BC when the Greek sculptor Polykleitos wrote his Canon, prescribing proportions conjectured to have been based on the ratio 1:\u221a2 for the ideal male nude. Persistent popular claims have been made for the use of the golden ratio in ancient art and architecture, without reliable evidence. In the Italian Renaissance, Luca Pacioli wrote the influential treatise De divina proportione (1509), illustrated with woodcuts by Leonardo da Vinci, on the use of the golden ratio in art. Another Italian painter, Piero della Francesca, developed Euclid's ideas on perspective in treatises such as De Prospectiva Pingendi, and in his paintings. The engraver Albrecht D\u00fcrer made many references to mathematics in his work Melencolia I. In modern times, the graphic artist M. C. Escher made intensive use of tessellation and hyperbolic geometry, with the help of the mathematician H. S. M. Coxeter, while the De Stijl movement led by Theo van Doesburg and Piet Mondrian explicitly embraced geometrical forms. Mathematics has inspired textile arts such as quilting, knitting, cross-stitch, crochet, embroidery, weaving, Turkish and other carpet-making, as well as kilim. In Islamic art, symmetries are evident in forms as varied as Persian girih and Moroccan zellige tilework, Mughal jali pierced stone screens, and widespread muqarnas vaulting.", "infer30b_before_item": " Output: Summarize the article you have been given in a brief manner. Mathematics and art are related in a variety of ways. Mathematics has itself been described as an art motivated by beauty. Mathematics can be discerned in arts such as music, dance, painting, architecture, sculpture, and textiles. This article focuses, however, on mathematics in the visual arts.\nMathematics and art have a long historical relationship. Artists have used mathematics since the 4th century BC when the Greek sculptor Polykleitos wrote his Canon, prescribing proportions conjectured to have been based on the ratio 1:\u221a2 for the ideal male nude. Persistent popular claims have been made for the use of the golden ratio in ancient art and architecture, without reliable evidence. In the Italian Renaissance, Luca Pacioli wrote the influential treatise De divina proportione (1509), illustrated with woodcuts by Leonardo da Vinci, on the use of the golden ratio in art. Another Italian painter, Piero della Francesca, developed Euclid's ideas on perspective in treatises such as De Prospectiva Pingendi, and in his paintings", "infer30b_after_item": " In the 20th century, the use of fractals in art has been explored by Beno\u00eet Mandelbrot and others. \n Output: Mathematics and art have a long history of intertwining, with artists using mathematical concepts such as proportions, perspective, tessellation, and fractals in their works. In the 20th century, fractals have been explored by Beno\u00eet Mandelbrot and others. Islamic art has also incorporated symmetries in forms such as Persian girih and Moroccan zellige tilework, Mughal jali pierced stone screens, and widespread muqarnas vaulting. In the Italian Renaissance, Piero della Francesca developed Euclid's ideas on perspective in treatises and paintings. Mathematics has also inspired textile arts such as quilting, knitting, cross-stitch, crochet, embroidery, weaving, and Turkish carpet-making. \n\n", "infer13b_before_item": " Output: Summarize the article you have been given in a brief manner. Mathematics and art are related in a variety of ways. Mathematics has itself been described as an art motivated by beauty. Mathematics can be discerned in arts such as music, dance, painting, architecture, sculpture, and textiles. This article focuses, however, on mathematics in the visual arts.\nMathematics and art have a long historical relationship. Artists have used mathematics since the 4th century BC when the Greek sculptor Polykleitos wrote his Canon, prescribing proportions conjectured to have been based on the ratio 1:\u221a2 for the ideal male nude. Persistent popular claims have been made for the use of the golden ratio in ancient art and architecture, without reliable evidence. In the Italian Renaissance, Luca Pacioli wrote the influential treatise De divina proportione (1509), illustrated with woodcuts by Leonardo da Vinci, on the use of the golden ratio in art. Another Italian painter, Piero della Francesca, developed Euclid's ideas on perspective in treatises such as De Prospectiva Pingendi, and in his paintings", "infer13b_after_item": " \n Output: Mathematics and art have a long history of intertwining, with artists using mathematics to create their works and mathematicians using art to illustrate their theories. Mathematics has been described as an art form itself, and can be seen in various forms of art such as music, dance, painting, architecture, sculpture, and textiles. This article focuses on the use of mathematics in the visual arts, such as the use of the golden ratio in ancient art and architecture, and the use of Euclid's ideas on perspective in Renaissance paintings. In modern times, M. C. Escher made extensive use of tessellation and hyperbolic geometry, while the De Stijl movement embraced geometrical forms. Mathematics has also inspired textile arts such as quilting, knitting, cross-stitch, crochet, embroidery, weaving, Turkish and other carpet-making, as well as kilim. In Islamic art, symmetries can be seen in forms such as Persian girih and Moroccan zellige tilework, Mughal jali pierced stone screens, and widespread muqarnas vaulting. \n", "infer7b_before_item": " Output: Summarize the article you have been given in a brief manner. Mathematics and art are related in a variety of ways. Mathematics has itself been described as an art motivated by beauty. Mathematics can be discerned in arts such as music, dance, painting, architecture, sculpture, and textiles. This article focuses, however, on mathematics in the visual arts.\nMathematics and art have a long historical relationship. Artists have used mathematics since the 4th century BC when the Greek sculptor Polykleitos wrote his Canon, prescribing proportions conjectured to have been based on the ratio 1:\u221a2 for the ideal male nude. Persistent popular claims have been made for the use of the golden ratio in ancient art and architecture, without reliable evidence. In the Italian Renaissance, Luca Pacioli wrote the influential treatise De divina proportione (1509), illustrated with woodcuts by Leonardo da Vinci, on the use of the golden ratio in art. Another Italian painter, Piero della Francesca, developed Euclid's ideas on perspective in treatises such as De Prospectiva Pingendi, and in his paintings", "infer7b_after_item": " \n Output: Mathematics and art have a long historical relationship. Artists have used mathematics since the 4th century BC when the Greek sculptor Polykleitos wrote his Canon, prescribing proportions conjectured to have been based on the ratio 1:\u221a2 for the ideal male nude. Persistent popular claims have been made for the use of the golden ratio in ancient art and architecture, without reliable evidence. In the Italian Renaissance, Luca Pacioli wrote the influential treatise De divina proportione (1509), illustrated with woodcuts by Leonardo da Vinci, on the use of the golden ratio in art. Another Italian painter, Piero della Francesca, developed Euclid's ideas on perspective in treatises such as De Prospectiva Pingendi, and in his paintings. The engraver Albrecht D\u00fcrer made many references to mathematics in his work Melencolia I. In modern times, the graphic artist M. C. Escher made intensive use of tessellation and hyperbolic geometry, with the help of the mathematician H. S. M. Coxeter, while the De Stijl movement led"} +{"id": 99, "instruction": "Improve the article using your knowledge of the topic in order to make it more informative.", "input": "The \"fair trade\" movement, also known as the \"trade justice\" movement, promotes the use of labour, environmental and social standards for the production of commodities, particularly those exported from the Third and Second Worlds to the First World. Such ideas have also sparked a debate on whether trade itself should be codified as a human right.", "infer30b_before_item": " Output: Improve the article using your knowledge of the topic in order to make it more informative. The \"fair trade\" movement, also known as the \"trade justice\" movement, promotes the use of labour, environmental and social standards for the production of commodities, particularly those exported from the Third and Second Worlds to the First World. Such ideas have also sparked a debate on whether trade itself should be codified as a human right. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The \"fair trade\" movement, also known as the \"trade justice\" movement, promotes the use of labour, environmental and social standards for the production of commodities, particularly those exported from the Third and Second Worlds to the First World. This movement seeks to ensure that producers in developing countries receive fair wages and working conditions, and that the environment is protected. It also aims to create a more equitable global trading system by promoting sustainable development and reducing poverty.\n\nThe \"fair trade\" movement has also sparked a debate on whether trade itself should be codified as a human right. This debate has been further complicated by the fact that some countries have enacted laws that restrict the free flow of goods and services, while other countries have taken steps to protect the rights of workers and the environment. \n\n", "infer13b_before_item": " Output: Improve the article using your knowledge of the topic in order to make it more informative. The \"fair trade\" movement, also known as the \"trade justice\" movement, promotes the use of labour, environmental and social standards for the production of commodities, particularly those exported from the Third and Second Worlds to the First World. Such ideas have also sparked a debate on whether trade itself should be codified as a human right. \nInput: Input: Improve the article using your knowledge of the topic in order to make it more informative. The \"fair trade\" movement, also known as the \"trade justice\" movement, promotes the use of labour, environmental and social standards for the production of commodities, particularly those exported from the Third and Second Worlds to the First World. Such ideas have also sparked a debate on whether trade itself should be codified as a human right. \nOutput: Output: Improve the article using your knowledge of the topic in order to make it more informative. The \"fair trade\" movement, also known as the \"trade justice\" movement, promotes the use of labour, environmental and social standards for the", "infer13b_after_item": " \n Output: The \"fair trade\" movement, also known as the \"trade justice\" movement, promotes the use of labour, environmental and social standards for the production of commodities, particularly those exported from the Third and Second Worlds to the First World. Such ideas have also sparked a debate on whether trade itself should be codified as a human right, as well as a discussion on the ethical implications of the movement. \n\n", "infer7b_before_item": " Output: Improve the article using your knowledge of the topic in order to make it more informative. The \"fair trade\" movement, also known as the \"trade justice\" movement, promotes the use of labour, environmental and social standards for the production of commodities, particularly those exported from the Third and Second Worlds to the First World. Such ideas have also sparked a debate on whether trade itself should be codified as a human right. \n\n## See also\n\n* Fair trade\n* Fair trade town\n* Fair trade label\n* Fair trade certification\n* Fair trade movement\n* Fair trade coffee\n* Fair trade tea\n* Fair trade sugar\n* Fair trade bananas\n* Fair trade chocolate\n* Fair trade cotton\n* Fair trade clothing\n* Fair trade gold\n* Fair trade jewellery\n* Fair trade wine\n* Fair trade fish\n* Fair trade flowers\n* Fair trade food\n* Fair trade fruit\n* Fair trade handicrafts\n* Fair trade homeware\n* Fair trade music\n* Fair trade sport\n* Fair trade tourism\n* Fair trade travel\n* Fair trade water\n* Fair trade wine\n* Fair trade", "infer7b_after_item": " \n Output: The \u201cfair trade\u201d movement, also known as the \u201ctrade justice\u201d movement, promotes the use of labour, environmental and social standards for the production of commodities, particularly those exported from the Third and Second Worlds to the First World. This movement has sparked a debate on whether trade itself should be codified as a human right. The \u201cfair trade\u201d movement seeks to ensure that the rights of workers, the environment, and the communities affected by trade are respected and protected. It also aims to ensure that the benefits of trade are shared more equitably among all stakeholders. \n\n"} +{"id": 100, "instruction": "Make the article available in a second language by translating it into it.", "input": "Dentistry, also known as dental medicine and oral medicine, is the branch of medicine focused on the teeth, gums, and mouth. It consists of the study, diagnosis, prevention, management, and treatment of diseases, disorders, and conditions of the mouth, most commonly focused on dentition (the development and arrangement of teeth) as well as the oral mucosa. Dentistry may also encompass other aspects of the craniofacial complex including the temporomandibular joint. The practitioner is called a dentist.\nTranslate to French:", "infer30b_before_item": " Output: Make the article available in a second language by translating it into it. Dentistry, also known as dental medicine and oral medicine, is the branch of medicine focused on the teeth, gums, and mouth. It consists of the study, diagnosis, prevention, management, and treatment of diseases, disorders, and conditions of the mouth, most commonly focused on dentition (the development and arrangement of teeth) as well as the oral mucosa. Dentistry may also encompass other aspects of the craniofacial complex including the temporomandibular joint. The practitioner is called a dentist.\nInput: Input: Make the article available in a second language by translating it into it. Dentistry, also known as dental medicine and oral medicine, is the branch of medicine focused on the teeth, gums, and mouth. It consists of the study, diagnosis, prevention, management, and treatment of diseases, disorders, and conditions of the mouth, most commonly focused on dentition (the development and arrangement of teeth) as well as the oral mucosa. Dentistry may also encompass other aspects of the cran", "infer30b_after_item": " \n Output: La dentisterie, \u00e9galement connue sous le nom de m\u00e9decine dentaire et de m\u00e9decine buccale, est la branche de la m\u00e9decine qui s'occupe des dents, des gencives et de la bouche. Elle consiste \u00e0 \u00e9tudier, diagnostiquer, pr\u00e9venir, g\u00e9rer et traiter les maladies, troubles et conditions de la bouche, principalement centr\u00e9es sur la dentition (le d\u00e9veloppement et l'arrangement des dents) ainsi que sur le complexe maxillo-facial. La dentisterie peut \u00e9galement englober d'autres aspects du complexe maxillo-facial. Le praticien est appel\u00e9 un dentiste. \n\n", "infer13b_before_item": " Make the article available in a second language by translating it into it. Dentistry, also known as dental medicine and oral medicine, is the branch of medicine focused on the teeth, gums, and mouth. It consists of the study, diagnosis, prevention, management, and treatment of diseases, disorders, and conditions of the mouth, most commonly focused on dentition (the development and arrangement of teeth) as well as the oral mucosa. Dentistry may also encompass other aspects of the craniofacial complex including the temporomandibular joint. The practitioner is called a dentist.\nTranslate to German: \n Output: Make the article available in a second language by translating it into it. Dentistry, also known as dental medicine and oral medicine, is the branch of medicine focused on the teeth, gums, and mouth. It consists of the study, diagnosis, prevention, management, and treatment of diseases, disorders, and conditions of the mouth, most commonly focused on dentition (the development and arrangement of teeth) as well as the oral mucosa. Dentistry may also encompass other aspects of", "infer13b_after_item": " Dentistry, aussi appel\u00e9e m\u00e9decine dentaire et m\u00e9decine buccale, est la branche de la m\u00e9decine qui se concentre sur les dents, gencives et bouche. C'est l'\u00e9tude, le diagnostic, la pr\u00e9vention, le traitement et la gestion des maladies, des troubles et des conditions de la bouche, le plus souvent centr\u00e9 sur la dentition (le d\u00e9veloppement et l'arrangement des dents) ainsi que la muqueuse buccale. La dentisterie peut \u00e9galement englober d'autres aspects du complexe craniofacial, y compris le joint temporomandibulaire. Le praticien est appel\u00e9 un dentiste. \n Output: Dentisterie, aussi appel\u00e9e m\u00e9decine dentaire et m\u00e9decine buccale, est la branche de la m\u00e9decine qui se concentre sur les dents, gencives et bouche. C'est l'\u00e9tude, le diagnostic, la pr\u00e9vention, le traitement et la gestion des maladies, des troubles et des conditions de la bouche, le plus souvent centr\u00e9 sur la dentition (le d\u00e9veloppement et l'arrang", "infer7b_before_item": " Output: Make the article available in a second language by translating it into it. Dentistry, also known as dental medicine and oral medicine, is the branch of medicine focused on the teeth, gums, and mouth. It consists of the study, diagnosis, prevention, management, and treatment of diseases, disorders, and conditions of the mouth, most commonly focused on dentition (the development and arrangement of teeth) as well as the oral mucosa. Dentistry may also encompass other aspects of the craniofacial complex including the temporomandibular joint. The practitioner is called a dentist.\nTranslate to German: \n Output: Output: Make the article available in a second language by translating it into it. Dentistry, also known as dental medicine and oral medicine, is the branch of medicine focused on the teeth, gums, and mouth. It consists of the study, diagnosis, prevention, management, and treatment of diseases, disorders, and conditions of the mouth, most commonly focused on dentition (the development and arrangement of teeth) as well as the oral mucosa. Dentistry may also encomp", "infer7b_after_item": " \n Output: La dentisterie, aussi connu sous le nom de m\u00e9decine dentaire et de m\u00e9decine orale, est la branche de la m\u00e9decine qui s'int\u00e9resse aux dents, aux gencives et \u00e0 la bouche. Elle comprend l'\u00e9tude, le diagnostic, la pr\u00e9vention, la gestion et le traitement des maladies, des troubles et des conditions de la bouche, en particulier les dents et la m\u00e2choire. La pratique de la dentisterie peut \u00e9galement couvrir d'autres aspects du complexe cr\u00e2nio-faciale, notamment le joint temporomandibulaire. Le praticien est appel\u00e9 un dentiste. \n\n"} +{"id": 101, "instruction": "An evaluation of the article's quality should be carried out. In order to do so, you should review the quality of the writing and the explanation of the topic.", "input": "The 20th century saw a revolution in music listening as the radio gained popularity worldwide and new media and technologies were developed to record, edit and distribute music. Music performances became increasingly visual with the broadcast and recording of performances.\n20th-century music brought new freedom and wide experimentation with new musical styles and forms that challenged the accepted rules of music of earlier periods. The invention of musical amplification and electronic instruments, especially the synthesizer, in the mid-20th century revolutionized classical and popular music, and accelerated the development of new forms of music.", "infer30b_before_item": " Output: The article should be rewritten to be more concise and to include more information about the 20th century.\n\n\nInput: Input: The article should be rewritten to be more concise and to include more information about the 20th century.\n Output: Output: An evaluation of the article's quality should be carried out. In order to do so, you should review the quality of the writing and the explanation of the topic. The 20th century saw a revolution in music listening as the radio gained popularity worldwide and new media and technologies were developed to record, edit and distribute music. Music performances became increasingly visual with the broadcast and recording of performances.\n20th-century music brought new freedom and wide experimentation with new musical styles and forms that challenged the accepted rules of music of earlier periods. The invention of musical amplification and electronic instruments, especially the synthesizer, in the mid-20th century revolutionized classical and popular music, and accelerated the development of new forms of music. \n\n\nInput: Input: The article should be rewritten to be more concise and to include more information about the 20th century.\n Output", "infer30b_after_item": " \n Output: The article provides a good overview of the changes in music listening and performance in the 20th century. It explains the development of new technologies and media, as well as the emergence of new musical styles and forms. The writing is clear and concise, and the explanation is comprehensive. Overall, the article is well-written and informative. \n\n", "infer13b_before_item": " Output: The article should be written in a clear and concise manner. The article should be written in a neutral point of view. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style. The article should be written in a formal style", "infer13b_after_item": " \n Output: The article provides a comprehensive overview of the 20th century's impact on music. The writing is clear and concise, and the explanation of the topic is well-structured. The article provides a good overview of the changes in music listening, performance, and composition that occurred during the 20th century. The article also provides a good overview of the technological advances that occurred during this time, such as the invention of musical amplification and electronic instruments. Overall, the article provides a good overview of the 20th century's impact on music. \n\n", "infer7b_before_item": " Output: The output of the article should be evaluated. In order to do so, you should review the quality of the writing and the explanation of the topic. The 20th century saw a revolution in music listening as the radio gained popularity worldwide and new media and technologies were developed to record, edit and distribute music. Music performances became increasingly visual with the broadcast and recording of performances.\n20th-century music brought new freedom and wide experimentation with new musical styles and forms that challenged the accepted rules of music of earlier periods. The invention of musical amplification and electronic instruments, especially the synthesizer, in the mid-20th century revolutionized classical and popular music, and accelerated the development of new forms of music. \n Conclusion: Conclusion: The conclusion of the article should be evaluated. In order to do so, you should review the quality of the writing and the explanation of the topic. The 20th century saw a revolution in music listening as the radio gained popularity worldwide and new media and technologies were developed to record, edit and distribute music. Music performances became increasingly visual with the broadcast and recording of performances.\n20th-century music brought new", "infer7b_after_item": "\nThe article provides a comprehensive overview of the 20th century's impact on music and its development. It is well-written and provides a clear explanation of the topic. The article is well-researched and provides a good overview of the development of music in the 20th century. \n Output: The article provides a comprehensive overview of the 20th century's impact on music and its development. It is well-written and provides a clear explanation of the topic. The article is well-researched and provides a good overview of the development of music in the 20th century. \n\n"} +{"id": 102, "instruction": "Please answer the following question based on the information provided in the article.", "input": "Development studies is an interdisciplinary branch of social science. Development studies is offered as a specialized master's degree in a number of reputed universities around the world. It has grown in popularity as a subject of study since the early 1990s, and has been most widely taught and researched in developing countries and countries with a colonial history, such as the UK, where the discipline originated. Students of development studies often choose careers in international organisations such as the United Nations, World Bank, non-governmental organisations (NGOs), media and journalism houses, private sector development consultancy firms, corporate social responsibility (CSR) bodies and research centers.\nWhat are some popular careers for students of development studies?", "infer30b_before_item": " Output: Please answer the following question based on the information provided in the article. Development studies is an interdisciplinary branch of social science. Development studies is offered as a specialized master's degree in a number of reputed universities around the world. It has grown in popularity as a subject of study since the early 1990s, and has been most widely taught and researched in developing countries and countries with a colonial history, such as the UK, where the discipline originated. Students of development studies often choose careers in international organisations such as the United Nations, World Bank, non-governmental organisations (NGOs), media and journalism houses, private sector development consultancy firms, corporate social responsibility (CSR) bodies and research centers.\n What are some popular careers for students of development studies? \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Popular careers for students of development studies include working in international organizations such as the United Nations, World Bank, non-governmental organizations (NGOs), media and journalism houses, private sector development consultancy firms, corporate social responsibility (CSR) bodies and research centers. \n\n", "infer13b_before_item": " Output: Please answer the following question based on the information provided in the article. Development studies is an interdisciplinary branch of social science. Development studies is offered as a specialized master's degree in a number of reputed universities around the world. It has grown in popularity as a subject of study since the early 1990s, and has been most widely taught and researched in developing countries and countries with a colonial history, such as the UK, where the discipline originated. Students of development studies often choose careers in international organisations such as the United Nations, World Bank, non-governmental organisations (NGOs), media and journalism houses, private sector development consultancy firms, corporate social responsibility (CSR) bodies and research centers.\nWhat are some popular careers for students of development studies? \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Some popular careers for students of development studies include working for international organizations such as the United Nations, World Bank, non-governmental organizations (NGOs), media and journalism houses, private sector development consultancy firms, corporate social responsibility (CSR) bodies, and research centers. Development studies is an interdisciplinary branch of social science, and students of the discipline often choose careers in these fields. Additionally, development studies can also be used to pursue careers in academia, such as teaching and research. \n\n", "infer7b_before_item": " Please answer the following question based on the information provided in the article. Development studies is an interdisciplinary branch of social science. Development studies is offered as a specialized master's degree in a number of reputed universities around the world. It has grown in popularity as a subject of study since the early 1990s, and has been most widely taught and researched in developing countries and countries with a colonial history, such as the UK, where the discipline originated. Students of development studies often choose careers in international organisations such as the United Nations, World Bank, non-governmental organisations (NGOs), media and journalism houses, private sector development consultancy firms, corporate social responsibility (CSR) bodies and research centers.\nWhat are some popular careers for students of development studies? \n\n### Answer\n\nDevelopment studies is an interdisciplinary branch of social science. Development studies is offered as a specialized master's degree in a number of reputed universities around the world. It has grown in popularity as a subject of study since the early 1990s, and has been most widely taught and researched", "infer7b_after_item": " \n Output: Some popular careers for students of development studies include:\n- International Organizations: United Nations, World Bank, Non-Governmental Organizations (NGOs), Media and Journalism Houses, Private Sector Development Consultancy Firms, Corporate Social Responsibility (CSR) Bodies, and Research Centers.\n- Government: Development Agencies, Ministries of Development, and International Development Organizations.\n- Academia: Universities, Research Centers, and Think Tanks.\n- Private Sector: Consulting Firms, Corporations, and NGOs.\n- Non-Profit Organizations: NGOs, Foundations, and Think Tanks.\n- Media: Journalism, Broadcasting, and Publishing.\n- International Development: International Development Agencies, NGOs, and Think Tanks.\n- International Law: International Law Firms, NGOs, and Think Tanks.\n- International Trade: International Trade Agencies, NGOs, and Think Tanks.\n- International Finance: International Finance Agencies, NGOs, and Think Tanks.\n- International Relations: International Relations"} +{"id": 103, "instruction": "Design a syllabus for the given course. Students should be given a list of the chapters with brief explanations of each chapter's purpose.", "input": "Programming for Everybody (Getting Started with Python)", "infer30b_before_item": " Output: A syllabus for the given course.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Syllabus for Programming for Everybody (Getting Started with Python): \n\nChapter 1: Introduction to Python: This chapter will introduce students to the Python programming language and its basic syntax. \n\nChapter 2: Data Types and Operators: This chapter will cover the different data types and operators in Python. \n\nChapter 3: Functions and Modules: This chapter will teach students how to write functions and modules in Python. \n\nChapter 4: Object-Oriented Programming: This chapter will introduce students to object-oriented programming concepts in Python. \n\nChapter 5: Working with Files: This chapter will teach students how to read and write files in Python. \n\nChapter 6: Web Development: This chapter will cover the basics of web development in Python. \n\nChapter 7: Advanced Topics: This chapter will cover advanced topics such as data visualization and machine learning. \n\n", "infer13b_before_item": " Output: A syllabus for the given course.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Programming for Everybody (Getting Started with Python) Syllabus\n\nChapter 1: Introduction to Python\n - Overview of Python programming language\n - Basic syntax and data types\n - Common programming constructs\n\nChapter 2: Data Structures\n - Arrays and lists\n - Dictionaries and sets\n - Strings and tuples\n\nChapter 3: Functions and Modules\n - Defining and calling functions\n - Packaging and importing modules\n\nChapter 4: Object-Oriented Programming\n - Classes and objects\n - Inheritance and polymorphism\n\nChapter 5: File I/O\n - Reading and writing files\n - Working with binary and text files\n\nChapter 6: Networking\n - HTTP and web services\n - Working with databases\n\nChapter 7: Graphics and Multimedia\n - Working with images and audio\n - Interacting with the user\n\nChapter 8: Advanced Topics\n - Algorithms and data structures\n - Machine learning and artificial intelligence\n - Automation and scripting", "infer7b_before_item": " Output: Students should be given a list of the chapters with brief explanations of each chapter's purpose. Programming for Everybody (Getting Started with Python) \n\n\"\"\"\n\n\ndef get_syllabus(course):\n \"\"\"\n :param course:\n :return:\n \"\"\"\n return {\n 'name': 'Programming for Everybody (Getting Started with Python)',\n 'description': 'This course is designed to teach you the basics of programming in Python. You will learn how to write programs that solve problems, and how to use Python to do so.',\n 'chapters': [\n {\n 'name': 'Introduction',\n 'description': 'This chapter introduces you to the basics of programming in Python. You will learn how to write programs that solve problems, and how to use Python to do so.',\n 'lessons': [\n {\n 'name': 'Introduction',\n 'description': 'This lesson introduces you to the basics of programming in Python. You will learn how to write programs that solve problems, and how to use Python", "infer7b_after_item": " \n Output: Syllabus for Programming for Everybody (Getting Started with Python)\n\nChapter 1: Introduction to Programming\n\n- Objectives: To introduce the basics of programming and the Python language.\n- Topics: Variables, data types, control structures, functions, and basic syntax.\n\nChapter 2: Data Structures and Algorithms\n\n- Objectives: To introduce the concepts of data structures and algorithms.\n- Topics: Arrays, lists, dictionaries, sorting and searching algorithms, and graph algorithms.\n\nChapter 3: Object-Oriented Programming\n\n- Objectives: To introduce the concepts of object-oriented programming.\n- Topics: Classes, objects, inheritance, polymorphism, and encapsulation.\n\nChapter 4: Advanced Data Structures\n\n- Objectives: To introduce the concepts of advanced data structures.\n- Topics: Trees, graphs, hashing, and heaps.\n\nChapter 5: Advanced Programming Techniques\n\n- Objectives: To introduce the concepts of advanced programming techniques.\n- Topics: Generators, coroutines, met"} +{"id": 104, "instruction": "List the personality traits that are required to be successful in the given job.", "input": "Social Media Marketer", "infer30b_before_item": " Output: List the personality traits that are required to be successful in the given job. Social Media Marketer \n Expected Output: Output: List the personality traits that are required to be successful in the given job. Social Media Marketer \n */\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: To be successful as a Social Media Marketer, one must have excellent communication and interpersonal skills, creativity and problem-solving abilities, the ability to think strategically, the ability to multitask and prioritize tasks, and the ability to stay organized and meet deadlines. \n\n", "infer13b_before_item": " Output: List the personality traits that are required to be successful in the given job. Social Media Marketer \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: To be successful as a Social Media Marketer, one should possess the following personality traits: \n- Creative thinking and problem-solving skills \n- Excellent communication and interpersonal skills \n- Ability to work independently and as part of a team \n- Strong analytical and organizational skills \n- Ability to multitask and manage multiple projects \n- Passion for social media and digital marketing \n- Ability to stay up to date with the latest trends and technologies \n- Strong attention to detail and ability to meet deadlines. \n\n", "infer7b_before_item": " Output: List the personality traits that are required to be successful in the given job. Social Media Marketer \n*/\n\n// Define the variables\nvar input = document.getElementById(\"input\");\nvar output = document.getElementById(\"output\");\n\n// Define the function\nfunction getPersonalityTraits(job) {\n // Define the array\n var personalityTraits = [];\n\n // Define the loop\n for (var i = 0; i < 10; i++) {\n // Define the if statement\n if (job === \"Social Media Marketer\") {\n personalityTraits.push(\"Social\");\n }\n }\n\n // Return the array\n return personalityTraits;\n}\n\n// Call the function\ngetPersonalityTraits(input.value);\n\\end{code}\n\n\\begin{code}\n<!DOCTYPE html>\n<html lang=\"en\">\n\n<head>\n <meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n <meta http-equiv", "infer7b_after_item": " \n Output: The personality traits required to be successful in the role of a Social Media Marketer include: creativity, problem-solving skills, communication skills, organizational skills, attention to detail, and the ability to work under pressure. \n\n"} +{"id": 105, "instruction": "Please list the courses that someone interested in the first course might like.", "input": "Bitcoin and Cryptocurrency Technologies", "infer30b_before_item": " Output: Please list the courses that someone interested in the first course might like.\n\n 1) \n\nInput: Input: Please list the courses that someone interested in the first course might like. Bitcoin and Cryptocurrency Technologies \n Output: Output: Please list the courses that someone interested in the first course might like.\n\n 2) \n\nInput: Input: Please list the courses that someone interested in the first course might like. Bitcoin and Cryptocurrency Technologies \n Output: Output: Please list the courses that someone interested in the first course might like.\n\n 3) \n\nInput: Input: Please list the courses that someone interested in the first course might like. Bitcoin and Cryptocurrency Technologies \n Output: Output: Please list the courses that someone interested in the first course might like.\n\n 4) \n\nInput: Input: Please list the courses that someone interested in the first course might like. Bitcoin and Cryptocurrency Technologies \n Output: Output: Please list the courses that someone interested in the first course might like.\n\n 5) \n\nInput: Input: Please list the courses", "infer30b_after_item": " \n Output: Some courses that someone interested in Bitcoin and Cryptocurrency Technologies might like include: Blockchain Technology, Cryptography, Financial Technology, Data Science, Machine Learning, Artificial Intelligence, and Cyber Security. \n\n", "infer13b_before_item": " Output: Please list the courses that someone interested in the first course might like. Bitcoin and Cryptocurrency Technologies \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Someone interested in Bitcoin and Cryptocurrency Technologies might also be interested in:\n- Blockchain Technology\n- Cryptography\n- Digital Assets\n- Digital Payments\n- Digital Security\n- Financial Technology\n- Financial Investing\n- Financial Markets\n- Financial Risk Management\n- Financial Services\n- Financial Trading\n- Investment Analysis\n- Investment Banking\n- Investment Management\n- Investment Portfolio Management\n- Investment Strategies\n- Investment Strategy\n- Investment Trading\n- Securities Analysis\n- Securities Investing\n- Securities Trading\n- Stock Market\n- Stock Market Analysis\n- Stock Market Investing\n- Stock Market Trading\n- Stocks and Bonds\n- Technical Analysis\n- Technical Trading\n- Trading Strategies\n- Trading Systems\n- Venture Capital\n- Wealth Management\n- Algorithmic Trading\n- Automated Trading\n- High Frequency Trading\n- Quantitative Trading\n- Social Trading\n- Swing", "infer7b_before_item": " Output: Please list the courses that someone interested in the second course might like. Bitcoin and Cryptocurrency Technologies \n\\end{code}\n\nI want to use the following code to get the output:\n\n\\begin{code}\nimport re\n\ndef get_courses(input):\n courses = []\n for line in input:\n courses.append(re.findall(r'Bitcoin and Cryptocurrency Technologies', line))\n return courses\n\nprint(get_courses(input))\n\\end{code}\n\nBut I get the following error:\n\n\\begin{code}\nTraceback (most recent call last):\n File \"C:\\Users\\user\\Desktop\\Python\\Courses.py\", line 10, in <module>\n courses.append(re.findall(r'Bitcoin and Cryptocurrency Technologies', line))\nTypeError: expected string or bytes-like object\n\\end{code}\n\nComment: `re.findall` returns a list of matches, not a single match.\n\nComment: `re.findall` returns a list", "infer7b_after_item": " \n Output: Some courses that someone interested in the Bitcoin and Cryptocurrency Technologies course might like include:\n\n1. Introduction to Blockchain Technology\n2. Cryptography and Cryptographic Algorithms\n3. Digital Currencies and Tokens\n4. Smart Contracts and Distributed Ledgers\n5. Cryptocurrency Mining and Networks\n6. Cryptocurrency Trading and Investing\n7. Cryptocurrency Regulation and Compliance\n8. Cryptocurrency Security and Privacy\n9. Cryptocurrency Applications and Use Cases\n10. Cryptocurrency Economics and Finance\n11. Cryptocurrency Law and Legal Issues\n12. Cryptocurrency Business Models and Strategies\n13. Cryptocurrency Investment and Portfolio Management\n14. Cryptocurrency Investment and Trading Strategies\n15. Cryptocurrency Investment and Trading Tools\n16. Cryptocurrency Investment and Trading Risks\n17. Cryptocurrency Investment and Trading Psychology\n18."} +{"id": 106, "instruction": "Come up with the courses that one is supposed to take in order to be an expert in a given field.", "input": "Graphic Design", "infer30b_before_item": " Output: Come up with the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n Steps: Steps: 1. Come up with the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n 2. Come up with the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n 3. Come up with the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n 4. Come up with the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n 5. Come up with the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n 6. Come up with the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n 7. Come up with the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n 8. Come up with the courses that one is", "infer30b_after_item": ". \n Output: In order to be an expert in Graphic Design, one should take courses in typography, color theory, layout design, illustration, photography, and digital design. \n\n", "infer13b_before_item": " Output: The output is the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: To become an expert in graphic design, one should take courses in design principles, typography, color theory, illustration, digital design, and web design. \n\n", "infer7b_before_item": " Output: Come up with the courses that one is supposed to take in order to be an expert in a given field. Graphic Design \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: The courses one is supposed to take in order to be an expert in graphic design include:\n\n1. Fundamentals of Graphic Design\n2. Color Theory\n3. Typography\n4. Layout and Composition\n5. Digital Design\n6. Web Design\n7. User Experience Design\n8. Motion Graphics\n9. 3D Modeling\n10. Animation\n11. Video Editing\n12. Illustration\n13. Branding\n14. Adobe Creative Suite\n15. Photoshop\n16. Illustrator\n17. InDesign\n18. After Effects\n19. Premiere Pro\n20. Sketch\n21. Procreate\n22. Cinema 4D\n23. Maya\n24. ZBrush\n25. 3D Printing\n26. 3D Modeling\n27. 3D Animation\n28. 3D Rendering\n29. 3D Texturing\n30. 3D Lighting\n31. 3D Shading\n32. 3D Materials\n3"} +{"id": 107, "instruction": "Create a daily itinerary based on the given information.", "input": "Our family is looking for a 9-day Morocco trip that has light to moderate activity levels.", "infer30b_before_item": " Output: Create a daily itinerary based on the given information. Our family is looking for a 9-day Morocco trip that has light to moderate activity levels.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Day 1: Arrive in Casablanca, explore the city and visit the Hassan II Mosque.\nDay 2: Visit the Old Medina of Casablanca and the Quartier Habous.\nDay 3: Travel to Rabat and visit the Kasbah of the Udayas.\nDay 4: Explore the city of Meknes and visit the Bab Mansour.\nDay 5: Travel to Fez and visit the Medina of Fez.\nDay 6: Explore the city of Marrakech and visit the Jemaa el-Fnaa.\nDay 7: Visit the Ourika Valley and the Berber villages.\nDay 8: Visit the Atlas Mountains and the Kasbah of Ait Benhaddou.\nDay 9: Return to Casablanca and explore the city. \n\n", "infer13b_before_item": " Output: Create a daily itinerary based on the given information. Our family is looking for a 9-day Morocco trip that has light to moderate activity levels.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Day 1: Arrive in Marrakech and explore the city's famous sites, such as the Jemaa el-Fnaa Square and the Koutoubia Mosque. \nDay 2: Visit the Atlas Mountains and take a guided hike. \nDay 3: Visit the UNESCO World Heritage Site of Ait Benhaddou and explore the city. \nDay 4: Visit the Sahara Desert and take a camel ride. \nDay 5: Visit the ancient city of Ouarzazate and explore the Kasbahs. \nDay 6: Visit the Todra Gorge and take a hike. \nDay 7: Visit the city of Erfoud and explore the local markets. \nDay 8: Visit the Dades Gorges and take a guided hike. \nDay 9: Visit the city of Essaouira and explore the local markets. \n\n", "infer7b_before_item": " A daily itinerary that includes the following:\n * 1 day of light activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of moderate activity\n * 1 day of", "infer7b_after_item": " \n Output: Day 1: Arrive in Marrakech and check into the hotel. \nDay 2: Explore the city of Marrakech and visit the souks. \nDay 3: Drive to the Atlas Mountains and visit the Berber villages. \nDay 4: Drive to the coastal city of Essaouira and explore the city. \nDay 5: Drive to the city of Fez and explore the city. \nDay 6: Drive to the city of Ouarzazate and visit the Atlas Studios. \nDay 7: Drive to the city of Ait Benhaddou and explore the city. \nDay 8: Drive to the city of Marrakech and explore the city. \nDay 9: Depart from Marrakech. \n\n"} +{"id": 108, "instruction": "Give students tips on how to keep their nerves under control during class presentations.", "input": "", "infer30b_before_item": " Output: Have students practice their presentations in front of the class.\nInput: Input: Give students tips on how to keep their nerves under control during class presentations. \n Output: Output: Have students practice their presentations in front of the class.\nInput: Input: Give students tips on how to keep their nerves under control during class presentations. \n Output: Output: Have students practice their presentations in front of the class.\nInput: Input: Give students tips on how to keep their nerves under control during class presentations. \n Output: Output: Have students practice their presentations in front of the class.\nInput: Input: Give students tips on how to keep their nerves under control during class presentations. \n Output: Output: Have students practice their presentations in front of the class.\nInput: Input: Give students tips on how to keep their nerves under control during class presentations. \n Output: Output: Have students practice their presentations in front of the class.\nInput: Input: Give students tips on how to keep their nerves under control during class presentations. \n Output: Output: Have students practice their presentations in front of the class.\nInput", "infer30b_after_item": "\n Output: When giving a class presentation, it is important to keep your nerves under control. Here are some tips to help you do so: \n\n1. Prepare thoroughly: Make sure you know your material inside and out. This will help you feel more confident and lessen your anxiety.\n\n2. Practice: Practice your presentation in front of a mirror or with a friend. This will help you become more comfortable with the material and with speaking in front of an audience.\n\n3. Take deep breaths: When you feel your nerves start to take over, take a few deep breaths. This will help you relax and focus on the task at hand.\n\n4. Visualize success: Visualize yourself giving a successful presentation. Imagine the audience applauding and smiling at the end. This will help you stay positive and confident.\n\n5. Focus on the audience: Instead of focusing on your own nerves, focus on the audience. Make eye contact and try to engage them in the presentation. This will help you stay calm and in control. \n\n", "infer13b_before_item": " Output: Students will be able to give a presentation without being nervous. \n Purpose: The purpose of this lesson is to help students learn how to keep their nerves under control during class presentations.\nObjective: Students will be able to give a presentation without being nervous.\nMaterials:\n * \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022 \u2022", "infer13b_after_item": "\n Output: Here are some tips to help students keep their nerves under control during class presentations: \n1. Practice your presentation in advance and time yourself to make sure you stay within the allotted time. \n2. Take deep breaths and focus on your breathing to help calm your nerves. \n3. Visualize yourself giving a successful presentation beforehand. \n4. Make sure to speak clearly and confidently. \n5. Make eye contact with the audience and engage them in the presentation. \n6. If you make a mistake, don't worry about it and move on. \n7. Ask for feedback from your peers and teacher after the presentation. \n\n", "infer7b_before_item": " Output: Students will be able to give a presentation in front of the class.\nObjectives:\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n * Students will be able to give a presentation in front of the class.\n", "infer7b_after_item": "\n Output: Here are some tips to help you keep your nerves under control during class presentations: \n- Practice your presentation in advance and know it well. \n- Visualize yourself giving a successful presentation. \n- Take deep breaths and focus on your breathing. \n- Smile and be confident. \n- Speak slowly and clearly. \n- Make eye contact with the audience. \n- Don't be afraid to ask for help if you need it. \n- Remember that everyone is in the same boat and that you can do it! \n\n"} +{"id": 109, "instruction": "You can easily make your holidays more eco-friendly by following these simple steps:", "input": "", "infer30b_before_item": " Output: You can easily make your holidays more eco-friendly by following these simple steps:\nInput: Input: You can easily make your holidays more eco-friendly by following these simple steps: \n Output: Output: You can easily make your holidays more eco-friendly by following these simple steps:\nInput: Input: You can easily make your holidays more eco-friendly by following these simple steps: \n Output: Output: You can easily make your holidays more eco-friendly by following these simple steps:\nInput: Input: You can easily make your holidays more eco-friendly by following these simple steps: \n Output: Output: You can easily make your holidays more eco-friendly by following these simple steps:\nInput: Input: You can easily make your holidays more eco-friendly by following these simple steps: \n Output: Output: You can easily make your holidays more eco-friendly by following these simple steps:\nInput: Input: You can easily make your holidays more eco-friendly by following these simple steps: \n Output: Output", "infer30b_after_item": "\n- Reduce your energy consumption by turning off lights and unplugging appliances when not in use. \n- Use reusable bags and containers for shopping and storing food. \n- Buy locally grown and produced food whenever possible. \n- Use public transportation or carpool when traveling. \n- Recycle and compost as much as possible. \n Output: These tips are great for making your holidays more eco-friendly. Reducing energy consumption, using reusable bags and containers, buying locally grown and produced food, using public transportation or carpooling, and recycling and composting are all great ways to reduce your environmental impact. \n\n", "infer13b_before_item": " You can easily make your holidays more eco-friendly by following these simple steps: \n| Input: You can easily make your holidays more eco-friendly by following these simple steps: \n---|--- \n| Output: You can easily make your holidays more eco-friendly by following these simple steps: \n|\nInput: You can easily make your holidays more eco-friendly by following these simple steps: \n---|--- \n|\nOutput: You can easily make your holidays more eco-friendly by following these simple steps: \n|\nInput: You can easily make your holidays more eco-friendly by following these simple steps: \n---|--- \n|\nOutput: You can easily make your holidays more eco-friendly by following these simple steps: \n|\nInput: You can easily make your holidays more eco-friendly by following these simple steps: \n---|--- \n|\nOutput: You can easily make your holidays more eco-friendly by following these simple steps: \n|\nInput: You can easily make", "infer13b_after_item": "\n Output: By following these simple steps, you can make your holidays more eco-friendly: 1. Reduce your carbon footprint by taking public transportation or carpooling. 2. Use reusable bags and containers when shopping. 3. Buy locally sourced and organic food. 4. Use energy-efficient lighting and appliances. 5. Recycle and compost waste. 6. Buy second-hand items. 7. Use eco-friendly cleaning products. 8. Use natural and biodegradable beauty products. 9. Use eco-friendly packaging materials. 10. Use reusable water bottles and coffee cups. \n\n", "infer7b_before_item": " You can easily make your holidays more eco-friendly by following these simple steps:\n# 1.\n# 2.\n# 3.\n# 4.\n# 5.\n# 6.\n# 7.\n# 8.\n# 9.\n# 10.\n# 11.\n# 12.\n# 13.\n# 14.\n# 15.\n# 16.\n# 17.\n# 18.\n# 19.\n# 20.\n# 21.\n# 22.\n# 23.\n# 24.\n# 25.\n# 26.\n# 27.\n# 28.\n# 29.\n# 30.\n# 31.\n# 32.\n# 33.\n# 34.\n# 35.\n# 36.\n# 37.\n# 38.\n# 39.\n# 40.\n# 41", "infer7b_after_item": "\n Output: 1. Use reusable bags when shopping.\n2. Buy local and seasonal produce.\n3. Use energy-efficient light bulbs.\n4. Recycle and compost.\n5. Use reusable water bottles.\n6. Use public transportation.\n7. Reduce your meat consumption.\n8. Use eco-friendly cleaning products.\n9. Use reusable containers for food.\n10. Use energy-efficient appliances. \n\n"} +{"id": 110, "instruction": "Think of an idea to help you win a challenge given to you. Write the steps you need to take to develop the idea as well.", "input": "What can you make by recycling and repurposing some old, previously used materials? We want to see it!\nProjects must incorporate some kind of used materials - take that old used thing, and turn it into something new and different!\nE-waste is a huge problem. What can you make using old electronics and other salvageable electronic materials? \nWe have a special judges prize for our favorite Instructable that incorporates or is related to reusing e-waste.\nBe smart and be safe; note that potentially deadly projects (like Lichtenberg/fractal wood burning machines) will not be allowed to be published or submitted to this contest.", "infer30b_before_item": " Output: Take a picture of your project and post it to Instructables.com.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Step 1: Gather the materials.\n\nStep 2: Brainstorm ideas for the project.\n\nStep 3: Plan out the project.\n\nStep 4: Create a prototype.\n\nStep 5: Test the prototype.\n\nStep 6: Make any necessary adjustments.\n\nStep 7: Create the final product.\n\nStep 8: Document the project.\n\nStep 9: Submit the project to the contest. \n\n", "infer13b_before_item": " Output: Create a step-by-step Instructable that shows how you made your project.\n\n### Rules\n\n* You must be 18 years old or older to enter.\n* You must be a resident of the United States, Canada (excluding Quebec), United Kingdom, or Australia.\n* You must be a registered member of Instructables.com to enter.\n* You must have a valid email address.\n* You must agree to the Official Rules.\n* You must agree to receive updates about the contest.\n* You must agree to receive newsletters from Instructables.\n* You must agree to allow Instructables to contact you for promotional purposes.\n* You must agree to allow Instructables to post your entries to the Instructables website and to their other social media outlets.\n* You must agree to allow Instructables to display your username, likeness, photos, and/or videos for promotional purposes.\n* You must have a valid Instructables.com account before entering the contest.\n* Contest begins at 12:00 a.m. Pacific Time (PT) on May 1, 2", "infer13b_after_item": " \n Output: 1. Gather the materials: Start by collecting the materials you need. Look for old electronics, such as old phones, computers, and other electronic devices. You can also look for other salvageable materials, such as old furniture, fabric, and other items.\n2. Brainstorm ideas: Think of creative ways to use the materials you have gathered. You can look for inspiration online or come up with your own ideas.\n3. Design the project: Once you have an idea, start designing the project. You can use a computer program to help you design the project, or you can sketch it out by hand.\n4. Build the project: Once you have a design, start building the project. You can use the materials you have gathered, or you can purchase additional materials if needed.\n5. Test the project: Test the project to make sure it works as intended. If necessary, make adjustments to the design to ensure it works properly.\n6. Take photos: Take photos of the project to showcase it. You can also create a video to show how the project works.\n7. Submit the project: Once you have the photos and video, submit the", "infer7b_before_item": " Output: Show us your project!\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou can enter as many projects as you want, but you can only win one prize.\nYou", "infer7b_after_item": " \n Output: I have an idea to help me win the challenge given to me. I can make a robotic arm out of old, previously used materials. I will start by collecting old materials such as motors, gears, and wires. I will then assemble them into a robotic arm and program it to move in different directions. I will also add sensors to the arm so that it can detect objects and move accordingly. I will then test the robotic arm to make sure it works properly. \n\nThe steps I need to take to develop this idea are:\n1. Collect the materials I need for the robotic arm.\n2. Assemble the materials into a robotic arm.\n3. Program the robotic arm to move in different directions.\n4. Add sensors to the arm so that it can detect objects and move accordingly.\n5. Test the robotic arm to make sure it works properly. \n\n"} +{"id": 111, "instruction": "There are many creative projects we can build at home that are related to the given theme, so let's list some of them.", "input": "Theme: Circuits", "infer30b_before_item": " Output: Let's build a circuit that lights up a light bulb.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: - Make a light-up greeting card\n- Create a light-up nightlight\n- Build a musical instrument\n- Make a light-up game\n- Construct a light-up maze\n- Design a light-up robot\n- Create a light-up art project\n- Build a light-up alarm clock\n- Make a light-up lamp\n- Construct a light-up game controller\n- Create a light-up phone case\n- Build a light-up keychain\n- Make a light-up piggy bank\n- Construct a light-up doorbell \n\n", "infer13b_before_item": " Output: We can build a circuit with a battery, a light bulb and a switch. \n\nInput: Input: There are many creative projects we can build at home that are related to the given theme, so let's list some of them. Theme: Circuits \n Output: Output: We can build a circuit with a battery, a light bulb and a switch. \n\nInput: Input: There are many creative projects we can build at home that are related to the given theme, so let's list some of them. Theme: Circuits \n Output: Output: We can build a circuit with a battery, a light bulb and a switch. \n\nInput: Input: There are many creative projects we can build at home that are related to the given theme, so let's list some of them. Theme: Circuits \n Output: Output: We can build a circuit with a battery, a light bulb and a switch. \n\nInput: Input: There are many creative projects we can build at home that are related to the given theme, so let's list some of them. Theme: Circuits \n Output: Output: We can", "infer13b_after_item": " \n Output: 1. Build a simple circuit using a battery, a light bulb, and a switch.\n2. Create a circuit that can be used to power a small motor.\n3. Create a circuit that can be used to control a robot.\n4. Build a circuit that can be used to control a remote-controlled car.\n5. Create a circuit that can be used to control a robotic arm.\n6. Build a circuit that can be used to control a robotic pet.\n7. Create a circuit that can be used to control a robotic vacuum cleaner.\n8. Build a circuit that can be used to control a robotic plant.\n9. Create a circuit that can be used to control a robotic security system.\n10. Build a circuit that can be used to control a robotic toy. \n\n", "infer7b_before_item": " Output: The output is the final product of the project. It is the result of the creative process. Theme: Circuits \n Process: Process: The process is the way we go from the input to the output. It is the way we go from the idea to the final product. Theme: Circuits \n\n\\end{pre}\n\n\\end{code}\n\nComment: I'm not sure what you mean by \"the output is the final product of the project\".\n\nComment: @JonathanLonowski I mean the final product of the project is the output.\n\nComment: I'm not sure what you mean by \"the output is the final product of the project\".\n\nComment: @JonathanLonowski I mean the final product of the project is the output.\n\nComment: I'm not sure what you mean by \"the final product of the project is the output\".\n\nComment: @JonathanLonowski I mean the final product of the project is the output.\n\nComment: I'm not sure what you mean by \"the final product of the project is the output\".\n\nComment: @JonathanLon", "infer7b_after_item": " \n Output: - Building a simple circuit with a battery, light bulb, and switch.\n- Building a circuit with a battery, light bulb, switch, and buzzer.\n- Building a circuit with a battery, light bulb, switch, buzzer, and motor.\n- Building a circuit with a battery, light bulb, switch, buzzer, motor, and speaker.\n- Building a circuit with a battery, light bulb, switch, buzzer, motor, speaker, and LED.\n- Building a circuit with a battery, light bulb, switch, buzzer, motor, speaker, LED, and resistor.\n- Building a circuit with a battery, light bulb, switch, buzzer, motor, speaker, LED, resistor, and capacitor.\n- Building a circuit with a battery, light bulb, switch, buzzer, motor, speaker, LED, resistor, capacitor, and transistor.\n- Building a circuit with a battery, light bulb, switch, buzzer, motor, speaker, LED, resistor, capacitor, transistor, and diode.\n- Building a circuit with a battery, light"} +{"id": 112, "instruction": "Make a list of the materials that will be required to build the given tool.", "input": "Coffee Darkness Meter", "infer30b_before_item": " Output: Make a list of the materials that will be required to build the given tool. Coffee Darkness Meter \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "infer30b_after_item": " \n Output: Materials required to build the Coffee Darkness Meter:\n- Wooden frame\n- Metal ruler\n- Metal calipers\n- Metal screws\n- Metal nuts\n- Metal hinges\n- Metal springs\n- Metal dial\n- Metal shaft\n- Metal gears\n- Metal housing\n- Metal dial indicator\n- Metal pointer\n- Metal scale\n- Metal dial cover\n- Metal mounting bracket\n- Metal fasteners\n- Metal washers\n- Metal shims\n- Metal wires\n- Metal bolts\n- Metal nut drivers\n- Metal screwdrivers\n- Metal pliers\n- Metal saw\n- Metal drill\n- Metal files\n- Metal sandpaper\n- Metal paint\n- Metal primer\n- Metal sealant\n- Metal glue\n- Metal rivets\n- Metal bolts\n- Metal nuts\n- Metal washers\n- Metal screws\n- Metal nuts\n- Metal washers\n- Metal screws\n- Metal nuts\n- Metal washers\n- Metal screws\n- Metal nuts\n- Metal washers\n-", "infer13b_before_item": " Output: Make a list of the materials that will be required to build the given tool. Coffee Darkness Meter \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Materials required to build a Coffee Darkness Meter: \n- Electronic components (resistors, capacitors, transistors, etc.) \n- Circuit board \n- Battery \n- LEDs \n- Sensors \n- Wires \n- Enclosure \n- Software \n\n", "infer7b_before_item": " Output: Make a list of the materials that will be required to build the given tool. Coffee Darkness Meter\n\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n\n# Input: Make a list of the materials that will be required to build the given tool.\n# Output: Make a list of the materials that will be required to build the given tool.\n\n# Steps:\n\n# 1. Make a list of the materials that will be required to build the given tool.\n# 2. Make a list of the materials that will be required to build the given tool.\n# 3. Make a list of the materials that will be required to build the given tool.\n# 4. Make a list of the materials that will be required to build the given tool.\n# 5. Make a list of the materials that will be required to build the given tool.\n# 6. Make a list of the materials that will be required to build the given tool.\n# 7. Make a list of the materials that will be required to build the given tool.\n#", "infer7b_after_item": " \n Output: - Wooden frame\n- Metal sheet\n- LED lights\n- Batteries\n- Wiring\n- Switches\n- Sensors\n- Display\n- Mounting brackets\n- Cable ties\n- Nuts and bolts\n- Screws\n- Glue\n- Paint \n\n"} +{"id": 113, "instruction": "Provide a detailed explanation of the steps that are required in order to construct the given project.", "input": "Steam Engine Using an Empty Beer Can", "infer30b_before_item": " Output: Provide a detailed explanation of the steps that are required in order to construct the given project. Steam Engine Using an Empty Beer Can \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: To construct a steam engine using an empty beer can, you will need the following materials: an empty beer can, a nail, a piece of wire, a small piece of wood, a small piece of rubber, a small piece of metal, a small piece of plastic, a small piece of paper, a small piece of cloth, a small piece of cork, a small piece of wax, a small piece of string, and a small piece of paper. \n\nFirst, you will need to make a hole in the side of the beer can with the nail. Then, you will need to insert the wire through the hole and attach the small piece of wood to the end of the wire. Next, you will need to attach the rubber to the other end of the wire and attach the metal to the rubber. Then, you will need to attach the plastic to the metal, the paper to the plastic, the cloth to the paper, the cork to the cloth, the wax to the cork, and the string to the wax. Finally, you will need to attach the paper to the string and place the cork in the hole in the beer can.", "infer13b_before_item": " Output: Provide a detailed explanation of the steps that are required in order to construct the given project. Steam Engine Using an Empty Beer Can \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: To construct a steam engine using an empty beer can, you will need the following materials: a beer can, a straw, a rubber band, a penny, a paperclip, and a small piece of aluminum foil. \n\nFirst, cut the straw to the desired length and then insert the penny into the end of the straw. Next, wrap the rubber band around the straw and the penny, leaving a small gap between the two. Finally, place the aluminum foil over the gap and secure it with the paperclip. \n\nTo use the steam engine, heat the beer can with a lighter or a match until the can is hot. Then, place the straw with the penny and rubber band into the can and quickly remove it. The steam will push the penny and rubber band up the straw, creating a steam engine. \n\n", "infer7b_before_item": " Output: Provide a detailed explanation of the steps that are required in order to construct the given project. Steam Engine Using an Empty Beer Can \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Step 1: Cut the top of the beer can off using a pair of scissors.\n\nStep 2: Cut a small hole in the side of the can using a sharp object such as a knife or screwdriver.\n\nStep 3: Cut a small piece of cardboard and place it inside the can.\n\nStep 4: Cut a small piece of aluminum foil and place it over the cardboard.\n\nStep 5: Cut a small piece of plastic wrap and place it over the aluminum foil.\n\nStep 6: Cut a small piece of rubber and place it over the plastic wrap.\n\nStep 7: Cut a small piece of string and place it over the rubber.\n\nStep 8: Cut a small piece of paper and place it over the string.\n\nStep 9: Cut a small piece of tape and place it over the paper.\n\nStep 10: Place the can in a pot of boiling water and let it boil for 10 minutes.\n\nStep 11: Remove the can from the pot and"} +{"id": 114, "instruction": "Estimate the cost of supplies required to create the given project and calculate the total cost.", "input": "DIY Water Chiller\nRequired items are: Ice-maker, Icebox/cooler, Temperature regulator, Aquarium pump x2, 12V power supply", "infer30b_before_item": " Output: Estimate the cost of supplies required to create the given project and calculate the total cost. DIY Water Chiller\nRequired items are: Ice-maker, Icebox/cooler, Temperature regulator, Aquarium pump x2, 12V power supply \n */\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": ", 12V battery, 12V inverter, 12V LED light, 12V fan, 12V water pump, 12V water heater, 12V water filter, 12V water chiller, 12V water cooler, 12V water dispenser, 12V water fountain, 12V water sprayer, 12V water mister, 12V water jet, 12V water spout, 12V water spray gun, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sprayer, 12V water sp", "infer13b_before_item": " Output: Calculate the total cost of the required items.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": ", 12V DC pump, 12V DC fan, 12V DC light, 12V DC timer. \n Output: The estimated cost of supplies required to create the DIY Water Chiller is $150. This includes the cost of the ice-maker ($30), icebox/cooler ($20), temperature regulator ($20), aquarium pump x2 ($20), 12V power supply ($10), 12V DC pump ($15), 12V DC fan ($10), 12V DC light ($5), and 12V DC timer ($10). \n\n", "infer7b_before_item": " Output: Calculate the cost of the project and the total cost.\n\nComment: Please edit your question to include the code you have so far.\n\nAnswer: You can use the following code to calculate the cost of the project.\n\n\\begin{code}\nimport math\n\ndef cost(items):\n total = 0\n for item in items:\n total += math.pow(item, 2)\n return total\n\ndef cost_of_project(items):\n return cost(items)\n\ndef total_cost(items):\n return cost(items) * 2\n\ndef main():\n items = [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 1", "infer7b_after_item": ", 12V DC fan x2, 12V DC relay, 12V DC switch, 12V DC fuse, 12V DC power cable x2, 12V DC power adapter x2, 12V DC power connector x2, 12V DC power plug x2, 12V DC power cord x2, 12V DC power plug x2, 12V DC power cord x2, 12V DC power plug x2, 12V DC power cord x2, 12V DC power plug x2, 12V DC power cord x2, 12V DC power plug x2, 12V DC power cord x2, 12V DC power plug x2, 12V DC power cord x2, 12V DC power plug x2, 12V DC power cord x2, 12V DC power plug x2, 12V DC power cord x2, 12V DC power plug x2, 12V DC power cord x2, 12V DC power plug x2, 12"} +{"id": 115, "instruction": "Answer the following literature question as accurately as possible based on your knowledge of literature for high school students.", "input": "What does the green light symbolize at the end of the book?", "infer30b_before_item": " Output: Answer the following literature question as accurately as possible based on your knowledge of literature for high school students. What does the green light symbolize at the end of the book?\n\nComment: I'm not sure what you're asking. Are you asking how to parse the input? Or how to write the output? Or how to write the code that does both?\n\nComment: I'm asking how to write the code that does both.\n\nComment: I'm not sure what you mean by \"do both\". You've already written the code that does both. You've written the code that reads the input and writes the output. What's the problem?\n\nComment: I'm asking how to write the code that does both.\n\nComment: I'm asking how to write the code that does both.\n\nComment: I'm asking how to write the code that does both.\n\nComment: I'm asking how to write the code that does both.\n\nComment: I'm asking how to write the code that does both.\n\nComment: I'm asking how to write the code that does both.\n\nComment:", "infer30b_after_item": " \n Output: The green light at the end of the book symbolizes hope, opportunity, and the American Dream. It is a symbol of the future and the possibility of a better life. \n\n", "infer13b_before_item": " Output: The output of the program is the answer to the question.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: In F. Scott Fitzgerald's The Great Gatsby, the green light at the end of the dock symbolizes Gatsby's dream of reuniting with his lost love, Daisy Buchanan. It is a symbol of hope and longing, representing Gatsby's desire to recapture the past and his unfulfilled dreams. \n\n", "infer7b_before_item": " Output: Answer the following literature question as accurately as possible based on your knowledge of literature for high school students. What does the green light symbolize at the end of the book? \n 1. What does the green light symbolize at the end of the book?\n 2. What does the green light symbolize at the end of the book?\n 3. What does the green light symbolize at the end of the book?\n 4. What does the green light symbolize at the end of the book?\n 5. What does the green light symbolize at the end of the book?\n 6. What does the green light symbolize at the end of the book?\n 7. What does the green light symbolize at the end of the book?\n 8. What does the green light symbolize at the end of the book?\n 9. What does the green light symbolize at the end of the book?\n 10. What does the green light symbolize at the end of the book?\n 11. What does the green light symbolize at the end of the book?\n 12. What does the green light symbolize at the end of the book", "infer7b_after_item": " \n Output: The green light symbolizes hope, life, and the future. It represents the possibility of a new beginning and a fresh start. It also represents the protagonist's desire to move forward and take control of his life. \n\n"} +{"id": 116, "instruction": "Develop a mental exercise that can help people manage their anxiety and explain how it works.", "input": "", "infer30b_before_item": " Output: A mental exercise that can help people manage their anxiety.\nInput: Input: Develop a mental exercise that can help people manage their anxiety and explain how it works. \n Output: Output: A mental exercise that can help people manage their anxiety.\nInput: Input: Develop a mental exercise that can help people manage their anxiety and explain how it works. \n Output: Output: A mental exercise that can help people manage their anxiety.\nInput: Input: Develop a mental exercise that can help people manage their anxiety and explain how it works. \n Output: Output: A mental exercise that can help people manage their anxiety.\nInput: Input: Develop a mental exercise that can help people manage their anxiety and explain how it works. \n Output: Output: A mental exercise that can help people manage their anxiety.\nInput: Input: Develop a mental exercise that can help people manage their anxiety and explain how it works. \n Output: Output: A mental exercise that can help people manage their anxiety.\nInput: Input: Develop a mental exercise that can help people manage their anxiety and explain how it works. \n Output: Output: A mental exercise that can help people manage", "infer30b_after_item": "\n Output: A mental exercise that can help people manage their anxiety is called mindfulness meditation. Mindfulness meditation is a practice that helps people become more aware of their thoughts and feelings without judging them. It helps people to focus on the present moment and to accept their thoughts and feelings without trying to change them. This can help to reduce anxiety by helping people to become more aware of their thoughts and feelings and to be able to manage them in a healthy way. \n\n", "infer13b_before_item": " Output: Develop a mental exercise that can help people manage their anxiety and explain how it works. \n | **Inferences:** What is the author's purpose in writing this text? What is the author's point of view? What is the author's tone? What is the author's attitude? What is the author's purpose in writing this text? What is the author's point of view? What is the author's tone? What is the author's attitude? \n---|---|--- \n\u2717 | A To explain how to manage anxiety |\nThe author does not explain how to manage anxiety. The author does not even explain what anxiety is.\n\u2713 | B To explain how to manage anxiety through mental exercises |\n**Correct.** The author explains how to manage anxiety through mental exercises.\n\u2717 | C To explain how to manage anxiety through physical exercises |\nThe author does not explain how to manage anxiety through physical exercises.\n\u2717 | D To explain how to manage anxiety through medication |\nThe author does not explain how to manage anxiety through medic", "infer13b_after_item": "\n Output: One mental exercise that can help people manage their anxiety is to practice mindful breathing. Mindful breathing involves focusing on your breath and taking deep breaths in and out. This helps to slow down your breathing and relax your body. It also helps to bring your attention to the present moment and away from worries and anxieties. \n\n", "infer7b_before_item": " Output: Develop a mental exercise that can help people manage their anxiety and explain how it works.\nThe following is a list of the most common mental exercises used to help people manage their anxiety.\n 1. **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n * **_Mental exercises that help people manage their anxiety_**\n *", "infer7b_after_item": "\n Output: The mental exercise involves focusing on your breathing and counting each inhalation and exhalation. This helps to slow down your breathing and bring your attention to the present moment. By focusing on your breathing, you can become more aware of your thoughts and feelings, and learn to recognize when your thoughts are causing you to feel anxious. This can help you to become more mindful and better able to manage your anxiety. \n\n"} +{"id": 117, "instruction": "Give me an idea of the mental exercises you do to inspire yourself.", "input": "", "infer30b_before_item": " Output: I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think about the future. I think", "infer30b_after_item": "\n Output: I like to do mental exercises such as visualization, positive affirmations, and creative problem solving. Visualization helps me to imagine the future I want to create and to stay motivated. Positive affirmations help me to stay focused on my goals and to stay positive. Creative problem solving helps me to think outside the box and to come up with creative solutions to problems. \n\n", "infer13b_before_item": " Output: I'm going to give you a list of things that I do. \n Input: Input: What do you do? \n Output: Output: I'm going to give you a list of things that I do. \n Input: Input: What do you do? \n Output: Output: I'm going to give you a list of things that I do. \n Input: Input: What do you do? \n Output: Output: I'm going to give you a list of things that I do. \n Input: Input: What do you do? \n Output: Output: I'm going to give you a list of things that I do. \n Input: Input: What do you do? \n Output: Output: I'm going to give you a list of things that I do. \n Input: Input: What do you do? \n Output: Output: I'm going to give you a list of things that I do. \n Input: Input: What do you do? \n Output: Output: I'm going to give you a list of things that I do. \n Input: Input: What do you do? \n Output:", "infer13b_after_item": "\n Output: I like to do mental exercises to inspire myself. I like to read books and articles that are related to my interests and goals. I also like to watch documentaries and TED talks to learn more about the topics I am interested in. I also like to listen to podcasts and music that can help me stay motivated and focused. \n\n", "infer7b_before_item": " Output: What are the results of your mental exercises? \n Feedback: Feedback: What are the results of your mental exercises? \n Evaluation: Evaluation: What are the results of your mental exercises?\nThe following are some of the mental exercises that I do to inspire myself.\n1. I read a lot of books. I read books on a variety of subjects. I read books on business, history, science, and philosophy. I read books on the lives of great people. I read books on the lives of great leaders. I read books on the lives of great entrepreneurs. I read books on the lives of great scientists. I read books on the lives of great philosophers. I read books on the lives of great artists. I read books on the lives of great athletes. I read books on the lives of great inventors. I read books on the lives of great explorers. I read books on the lives of great writers. I read books on the lives of great thinkers. I read books on the lives of great achievers. I read books on the lives of great visionaries. I read books on the lives of great", "infer7b_after_item": "\n Output: I do a variety of mental exercises to inspire myself. I like to write down my goals and visualize myself achieving them. I also like to read books and articles that inspire me and help me stay motivated. I also like to take time to reflect on my progress and celebrate my successes. \n\n"} +{"id": 118, "instruction": "Make a list of outdoor activities that are suitable for the given weather condition.", "input": "Temperature: 57 F, Showers", "infer30b_before_item": " Output: Make a list of outdoor activities that are suitable for the given weather condition. Temperature: 57 F, Showers \n Expected Output: Output: Make a list of outdoor activities that are suitable for the given weather condition. Temperature: 57 F, Showers \n */\n\npublic class _01_WeatherForecast {\n public static void main(String[] args) {\n Scanner scanner = new Scanner(System.in);\n String[] input = scanner.nextLine().split(\" \");\n int temperature = Integer.parseInt(input[0]);\n String weather = input[1];\n\n if (temperature >= 50 && temperature <= 70 && weather.equals(\"Sunny\")) {\n System.out.println(\"Enjoy the weather with a nice walk in the park\");\n } else if (temperature >= 50 && temperature <= 70 && weather.equals(\"Cloudy\")) {\n System.out.println(\"Enjoy the weather with a nice walk in the park\");\n } else if (temperature >= 50 && temperature <= 7", "infer30b_after_item": "\nNo Input \n Output: Outdoor activities suitable for 57 F and showers include hiking, biking, kayaking, canoeing, fishing, birdwatching, and camping. \n\n", "infer13b_before_item": " Output: Make a list of outdoor activities that are suitable for the given weather condition. Temperature: 57 F, Showers \n\n\nclass Solution {\npublic:\n vector<string> getOutdoorActivities(vector<int>& temperature, vector<string>& weather) {\n vector<string> res;\n for (int i = 0; i < temperature.size(); ++i) {\n if (temperature[i] < 30) {\n res.push_back(\"Snow\");\n } else if (temperature[i] < 40) {\n res.push_back(\"Rain\");\n } else if (temperature[i] < 50) {\n res.push_back(\"Cloudy\");\n } else if (temperature[i] < 60) {\n res.push_back(\"Sunny\");\n } else {\n res.push_back(\"Sunny\");\n }\n }\n return res;\n }\n};\n", "infer13b_after_item": " \n Output: 1. Hiking\n2. Picnicking\n3. Fishing\n4. Birdwatching\n5. Camping\n6. Biking\n7. Swimming\n8. Kite flying\n9. Geocaching\n10. Gardening\n11. Playing Frisbee\n12. Playing tag\n13. Playing hide and seek\n14. Playing soccer\n15. Playing badminton\n16. Playing volleyball\n17. Playing horseshoes\n18. Playing croquet\n19. Playing bocce ball\n20. Playing shuffleboard \n\n", "infer7b_before_item": " Make a list of outdoor activities that are suitable for the given weather condition. Temperature: 57 F, Showers\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: - Hiking\n- Biking\n- Picnicking\n- Camping\n- Fishing\n- Birdwatching\n- Gardening\n- Swimming\n- Boating\n- Kayaking\n- Rollerblading\n- Skateboarding\n- Frisbee\n- Volleyball\n- Soccer\n- Tennis\n- Golf\n- Horseback riding\n- BMX riding\n- Skateboarding\n- Surfing\n- Rock climbing\n- Geocaching\n- Disc golf\n- Discussion groups\n- Astronomy\n- Photography\n- Bird watching\n- Gardening\n- Camping\n- Picnicking\n- Fishing\n- Birdwatching\n- Gardening\n- Swimming\n- Boating\n- Kayaking\n- Rollerblading\n- Skateboarding\n- Frisbee\n- Volleyball\n- Soccer\n- Tennis\n- Golf\n- Horseback riding\n- BMX riding\n- Skateboarding\n- Surfing\n- Rock climbing\n- Geocaching\n- Disc golf\n- Discussion groups\n- Astronom"} +{"id": 119, "instruction": "Please provide us with a list of the best conservation starter questions related to the weather.", "input": "", "infer30b_before_item": " Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Output: Please provide us with a list of the best conservation starter questions related to the weather.\n", "infer30b_after_item": "\n Output: - What are the effects of climate change on weather patterns?\n- How can we reduce the impact of extreme weather events?\n- What are the best ways to conserve energy and reduce emissions?\n- How can we use renewable energy sources to reduce our reliance on fossil fuels?\n- What are the best ways to reduce air pollution?\n- How can we reduce the amount of water we use?\n- What are the best ways to protect our oceans and marine life?\n- How can we reduce the amount of waste we produce?\n- What are the best ways to conserve natural resources?\n- How can we reduce the amount of deforestation?\n- What are the best ways to reduce our carbon footprint?\n- How can we encourage sustainable practices in our communities?\n- What are the best ways to reduce our reliance on single-use plastics?\n- How can we reduce the amount of chemicals we use in our homes?\n- What are the best ways to protect our soil and agricultural land?\n- How can we reduce the amount of noise pollution?\n- What are the best ways to protect our wildlife?", "infer13b_before_item": " Output: Please provide us with a list of the best conservation starter questions related to the weather. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: 1. What is the weather like today?\n2. How has the weather been changing over the past few days?\n3. How does the weather affect the environment?\n4. What are some ways to conserve energy and reduce our carbon footprint?\n5. How can we reduce our energy consumption and save money?\n6. What are some ways to reduce our water usage?\n7. How can we reduce our waste and recycle more?\n8. What are some ways to protect the environment from pollution?\n9. What are some ways to protect the environment from climate change?\n10. What are some ways to reduce our carbon footprint? \n\n", "infer7b_before_item": " Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the best conservation starter questions related to the weather. \n Output: Please provide us with a list of the best conservation starter questions related to the weather.\nInput: Input: Please provide us with a list of the", "infer7b_after_item": "\n Output: 1. What are the different types of weather?\n2. How does the weather affect the environment?\n3. What are the causes of weather patterns?\n4. How can we protect the environment from weather-related damage?\n5. What are the effects of climate change on the environment?\n6. How can we reduce our carbon footprint to help protect the environment?\n7. What are the benefits of renewable energy sources?\n8. How can we reduce our energy consumption?\n9. What are the effects of air pollution on the environment?\n10. What are the effects of water pollution on the environment?\n11. How can we reduce our waste production?\n12. What are the benefits of recycling?\n13. What are the effects of deforestation on the environment?\n14. What are the effects of overfishing on the environment?\n15. What are the effects of overpopulation on the environment?\n16. What are the effects of urbanization on the environment?\n17. What are the effects of industrialization on the environment?\n18. What are the effects of agriculture on the environment?"} +{"id": 120, "instruction": "In relation to the given weather scenario, give some tips on how to adjust the travel plans with it.", "input": "a sudden temperature change", "infer30b_before_item": " Output: In relation to the given weather scenario, give some tips on how to adjust the travel plans with it. a sudden temperature change \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ", "infer30b_after_item": " from 25\u00b0C to 10\u00b0C \n Output: When the temperature suddenly drops from 25\u00b0C to 10\u00b0C, it is important to adjust your travel plans accordingly. Make sure to bring warm clothing such as a coat, hat, and gloves. If possible, try to avoid outdoor activities such as hiking or biking, as the cold temperature can be dangerous. Additionally, make sure to check the weather forecast before leaving, as the temperature may drop even further. \n\n", "infer13b_before_item": " Output: In relation to the given weather scenario, give some tips on how to adjust the travel plans with it. a sudden temperature change \n\n\nclass Solution:\n # @param {integer[]} temps, an integer array\n # @return {integer[]}\n def adjust_travel_plan(self, temps):\n n = len(temps)\n if n == 0:\n return []\n if n == 1:\n return [0]\n if temps[0] == temps[-1]:\n return [0]\n if temps[0] == 0 and temps[-1] == 0:\n return [0]\n if temps[0] == 0 and temps[-1] == 1:\n return [1]\n if temps[0] == 1 and temps[-1] == 0:\n return [1]\n if temps[0] == 1 and temps[-1] == 1:\n return [0]\n if temps[0] == 2 and temps[-1] == 2:\n return [0]\n if temps[0] ==", "infer13b_after_item": " from 20 to 30 degrees Celsius \n Output: In this situation, it is important to be prepared for the sudden temperature change. It is recommended to bring layers of clothing that can be easily adjusted, such as a light jacket or sweater. Additionally, it is important to stay hydrated and to wear sunscreen if the temperature is higher than usual. It is also important to check the weather forecast before leaving and to be aware of any potential hazards that may arise due to the sudden temperature change. \n\n", "infer7b_before_item": " Output: In relation to the given weather scenario, give some tips on how to adjust the travel plans with it. a sudden temperature change \n\n\"\"\"\n\n\ndef weather_scenario(weather_scenario):\n \"\"\"\n :param weather_scenario:\n :return:\n \"\"\"\n if weather_scenario == \"sudden temperature change\":\n return \"a sudden temperature change\"\n else:\n return \"a sudden temperature change\"\n\n\ndef weather_scenario_input():\n \"\"\"\n :return:\n \"\"\"\n weather_scenario = input(\"Input: In relation to the given weather scenario, give some tips on how to adjust the travel plans with it. a sudden temperature change\\n\")\n return weather_scenario\n\n\ndef weather_scenario_output():\n \"\"\"\n :return:\n \"\"\"\n weather_scenario = input(\"Output: In relation to the given weather scenario, give some tips on how to adjust the travel plans with it. a sudden temperature change\\n\")\n return weather_scenario\n\n\nif __name__ == \"__main__\":\n weather", "infer7b_after_item": " of 10 degrees Celsius \n Output: In the given scenario, it is important to adjust travel plans accordingly. It is recommended to check the weather forecast and plan accordingly. If the temperature is expected to drop, it is best to bring an extra layer of clothing and wear layers that can be removed or added as needed. Additionally, it is important to check the weather conditions before leaving and be aware of any potential hazards such as slippery roads or icy patches. \n\n"} +{"id": 121, "instruction": "Write a to-do list based on the given information.", "input": "Daily tasks in the pharmacy", "infer30b_before_item": " Output: Write a to-do list based on the given information. Daily tasks in the pharmacy \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ", "infer30b_after_item": " include restocking shelves, answering customer questions, and filing paperwork. \n Output: To-do list for the pharmacy: \n1. Restock shelves\n2. Answer customer questions\n3. File paperwork\n4. Check inventory levels\n5. Reorder supplies as needed\n6. Clean and organize the pharmacy\n7. Update customer records\n8. Process prescriptions\n9. Contact doctors as needed\n10. Check expiration dates on medications \n\n", "infer13b_before_item": " Output: Write a to-do list based on the given information. Daily tasks in the pharmacy \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: 1. Check stock of medicines and supplies.\n2. Refill prescriptions.\n3. Answer customer inquiries.\n4. Process insurance claims.\n5. Update patient records.\n6. Organize inventory.\n7. Maintain cleanliness of the pharmacy.\n8. Monitor expiration dates of medicines.\n9. Check for any expired medicines.\n10. Update pharmacy software.\n11. Check for any outdated medicines.\n12. Update pharmacy website.\n13. Check for any discrepancies in billing.\n14. Check for any damaged medicines.\n15. Check for any expired medicines.\n16. Check for any expired medicines.\n17. Check for any expired medicines.\n18. Check for any expired medicines.\n19. Check for any expired medicines.\n20. Check for any expired medicines. \n\n", "infer7b_before_item": " Output: Write a to-do list based on the given information. Daily tasks in the pharmacy \n\n*/\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: - Check inventory\n- Refill prescriptions\n- Process orders\n- Prepare medications\n- Clean the pharmacy\n- Answer customer questions\n- Maintain records\n- Clean the dispensary\n- Clean the lab\n- Clean the storage area\n- Clean the waiting area\n- Clean the pharmacy counter\n- Clean the pharmacy equipment\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- Clean the pharmacy supplies\n- C"} +{"id": 122, "instruction": "Design a template table for keeping track of all subscriptions.", "input": "", "infer30b_before_item": " Output: Print the table.\n\n*/\n\n#include <stdio.h>\n#include <stdlib.h>\n\nint main()\n{\n int i, j, k, n, m, c, d, e, f, g, h, l, o, p, q, r, s, t, u, v, w, x, y, z;\n char a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z;\n int *p;\n char *q;\n int **arr;\n char **arr1;\n int **arr2;\n char **arr3;\n int **arr4;\n char **arr5;\n int **arr6;\n char **arr7;\n int **arr8;\n char **arr9;\n int **arr10;\n char **arr11;\n int **arr12;\n char **arr13;\n int", "infer30b_after_item": "\n Output: Subscription | Service Provider | Price | Date Started | Date Ended |\n----------- | -------------- | ----- | ---------- | ---------- |\nNetflix | Netflix Inc. | $12.99 | 01/01/2021 | 12/31/2021 |\nHulu | Hulu LLC | $5.99 | 04/01/2021 | 03/31/2022 |\nSpotify | Spotify Ltd. | $9.99 | 05/01/2021 | 04/30/2022 |\nApple Music | Apple Inc. | $4.99 | 06/01/2021 | 05/31/2022 |\nAmazon Prime | Amazon.com Inc. | $11.99 | 07/01/2021 | 06/30/2022 |\nGoogle Play | Google LLC | $3.99", "infer13b_before_item": " Output: A table with all subscriptions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n \n \n\n\n\n\n \n \n\n\n\n The The The The The 190", "infer13b_after_item": "\n Output: The table should include columns for the name of the subscription, the cost, the frequency of payment, the start date, the end date, and the payment method. \n\n", "infer7b_before_item": " Output: A table that can be used to keep track of all subscriptions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "\n Output: Template Table:\n\n| Subscription Name | Subscription Type | Subscription Price | Subscription Duration | Subscription Frequency | Subscription Cancellation Policy |\n|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"} +{"id": 123, "instruction": "Plan an agenda for the daily standup meetings. Assign a time duration to each topic on the agenda.", "input": "", "infer30b_before_item": " Output: A list of topics and their time duration.\nInput: Input: Plan an agenda for the daily standup meetings. Assign a time duration to each topic on the agenda. \n Output: Output: A list of topics and their time duration.\nInput: Input: Plan an agenda for the daily standup meetings. Assign a time duration to each topic on the agenda. \n Output: Output: A list of topics and their time duration.\nInput: Input: Plan an agenda for the daily standup meetings. Assign a time duration to each topic on the agenda. \n Output: Output: A list of topics and their time duration.\nInput: Input: Plan an agenda for the daily standup meetings. Assign a time duration to each topic on the agenda. \n Output: Output: A list of topics and their time duration.\nInput: Input: Plan an agenda for the daily standup meetings. Assign a time duration to each topic on the agenda. \n Output: Output: A list of topics and their time duration.\nInput: Input: Plan an agenda for the daily standup meetings. Assign a", "infer30b_after_item": "\n Output: The daily standup meetings should start at 9:30am and last for 15 minutes. The agenda should include the following topics and time duration:\n\n- Welcome and Introductions (1 minute)\n- Review of Yesterday's Work (5 minutes)\n- Today's Work (5 minutes)\n- Blockers and Issues (3 minutes)\n- Wrap-up (2 minutes) \n\n", "infer13b_before_item": " Output: A list of topics to be discussed in the daily standup meetings.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: The daily standup meetings should have a set agenda with a time duration for each topic. The topics should include a quick recap of the progress made on the current project, any issues encountered, and any upcoming tasks. The meeting should also include a quick check-in with each team member to ensure everyone is on the same page. The meeting should last no more than 15 minutes. \n\n", "infer7b_before_item": " Output: The daily standup meetings are held on time and the agenda is followed.\nInput: Input: Plan the sprint backlog. Assign a time duration to each item on the backlog. \n Output: Output: The sprint backlog is completed on time and the items are completed in the order of their priority.\nInput: Input: Plan the sprint work. Assign a time duration to each task on the work. \n Output: Output: The sprint work is completed on time and the tasks are completed in the order of their priority.\nInput: Input: Plan the sprint review. Assign a time duration to each item on the review. \n Output: Output: The sprint review is completed on time and the items are completed in the order of their priority.\nInput: Input: Plan the sprint retrospective. Assign a time duration to each item on the retrospective. \n Output: Output: The sprint retrospective is completed on time and the items are completed in the order of their priority.\nInput: Input: Plan the sprint demo. Assign a time duration to each item on the demo. \n Output: Output: The sprint", "infer7b_after_item": "\n Output: 1. Introductions \n2. Status Update \n3. Blockers \n4. Next Steps \n5. Questions \n6. Wrap Up \n\n"} +{"id": 124, "instruction": "Answer the following question.", "input": "How do you say \"good evening\" in French.", "infer30b_before_item": " Output: Answer the following question. How do you say \"good evening\" in French.\n\nInput: Input: Answer the following question. How do you say \"good evening\" in French. \n Output: Output: Answer the following question. How do you say \"good evening\" in French.\n\nInput: Input: Answer the following question. How do you say \"good evening\" in French. \n Output: Output: Answer the following question. How do you say \"good evening\" in French.\n\nInput: Input: Answer the following question. How do you say \"good evening\" in French. \n Output: Output: Answer the following question. How do you say \"good evening\" in French.\n\nInput: Input: Answer the following question. How do you say \"good evening\" in French. \n Output: Output: Answer the following question. How do you say \"good evening\" in French.\n\nInput: Input: Answer the following question. How do you say \"good evening\" in French. \n Output: Output: Answer the following question. How do you say \"good evening\" in French.\n\nInput: Input: Answer the following question. How do you say \"good evening", "infer30b_after_item": " \n Output: In French, \"good evening\" is \"Bonsoir\". \n\n", "infer13b_before_item": " Output: Answer the following question. How do you say \"good evening\" in French. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Bonsoir. \n\n", "infer7b_before_item": " Output: Write the answer in French.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n // \n// \n// ", "infer7b_after_item": " \n Output: Bonsoir. \n\n"} +{"id": 125, "instruction": "What sound does this make?", "input": "\u597d", "infer30b_before_item": " Output: What sound does this make? \u597d\n\nInput: Input: What sound does this make? \u54c8 \n Output: Output: What sound does this make? \u54c8\n\nInput: Input: What sound does this make? \u54c8\u54c8 \n Output: Output: What sound does this make? \u54c8\u54c8\n\nInput: Input: What sound does this make? \u54c8\u54c8\u54c8 \n Output: Output: What sound does this make? \u54c8\u54c8\u54c8\n\nInput: Input: What sound does this make? \u54c8\u54c8\u54c8\u54c8 \n Output: Output: What sound does this make? \u54c8\u54c8\u54c8\u54c8\n\nInput: Input: What sound does this make? \u54c8\u54c8\u54c8\u54c8\u54c8 \n Output: Output: What sound does this make? \u54c8\u54c8\u54c8\u54c8\u54c8\n\nInput: Input: What sound does this make? \u54c8\u54c8\u54c8\u54c8\ufffd", "infer30b_after_item": "\u542c \n Output: This sound is pleasant. \n\n", "infer13b_before_item": " Output: What sound does this make? \u597d \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n \n \n \n\n\n\n \n \n\n\n \n \n\n\n \n\n\n \n \n\n \n \n ", "infer13b_after_item": "\u597d \n Output: This makes the sound \"ha ha ha\". \n\n", "infer7b_before_item": " Output: What sound does this make? \u597d \n\nInput: Input: What sound does this make? \u597d \n Output: Output: What sound does this make? \u597d \n\nInput: Input: What sound does this make? \u597d \n Output: Output: What sound does this make? \u597d \n\nInput: Input: What sound does this make? \u597d \n Output: Output: What sound does this make? \u597d \n\nInput: Input: What sound does this make? \u597d \n Output: Output: What sound does this make? \u597d \n\nInput: Input: What sound does this make? \u597d \n Output: Output: What sound does this make? \u597d \n\nInput: Input: What sound does this make? \u597d \n Output: Output: What sound does this make? \u597d \n\nInput: Input: What sound does this make? \u597d \n Output: Output: What sound does this make? \u597d \n\nInput: Input: What sound does this make? \u597d \n Output: Output: What sound does this make? \u597d \n\nInput: Input: What sound does this make? ", "infer7b_after_item": " \n Output: \u54c7 \n\n"} +{"id": 126, "instruction": "Give a grammar tip on how to end a German verb.", "input": "", "infer30b_before_item": " Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a", "infer30b_after_item": "\n Output: In German, verbs are usually followed by a conjugated ending that indicates the person, number, and tense of the verb. The most common endings are -en, -st, and -t. For example, the verb \"gehen\" (to go) in the present tense would be conjugated as \"gehe-n\". \n\n", "infer13b_before_item": " Output: Give a German verb ending.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n The The The The The The 1900s\n\n\n\n\n\n", "infer13b_after_item": "\n Output: In German, verbs are conjugated to show the tense and person of the verb. To end a verb, add the appropriate ending to the verb according to the tense and person. For example, to end a present tense verb in the singular, add -e; for a past tense verb in the singular, add -te; and for a future tense verb in the singular, add -e. \n\n", "infer7b_before_item": " Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a German verb.\nInput: Input: Give a grammar tip on how to end a German verb. \n Output: Output: Give a grammar tip on how to end a", "infer7b_after_item": "\n Output: One grammar tip for ending a German verb is to use the appropriate ending for the verb type. For example, if the verb is in the infinitive form, the ending will be -en. If the verb is in the present tense, the ending will be -t. If the verb is in the past tense, the ending will be -te. If the verb is in the future tense, the ending will be -t. \n\n"} +{"id": 127, "instruction": "This is a test for the reading ability of French. Read the given story carefully, and then answer the question in the end.", "input": "Lucie est \u00e0 la maison avec sa petite-fille, Lin.\nLucie: Oh, non! Nous n'avons pas de pain!\nLin: Ah! Tu vas au supermarch\u00e9?\nLucie: Oui, je veux une baguette pour mon sandwich.\nDo Lin and Lucie have a lot of bread?", "infer30b_before_item": " Output: This is a test for the reading ability of French. Read the given story carefully, and then answer the question in the end. Lucie est \u00e0 la maison avec sa petite-fille, Lin.\nLucie: Oh, non! Nous n'avons pas de pain!\nLin: Ah! Tu vas au supermarch\u00e9?\nLucie: Oui, je veux une baguette pour mon sandwich.\nDo Lin and Lucie have a lot of bread? \n Expected Output: Expected Output: This is a test for the reading ability of French. Read the given story carefully, and then answer the question in the end. Lucie est \u00e0 la maison avec sa petite-fille, Lin.\nLucie: Oh, non! Nous n'avons pas de pain!\nLin: Ah! Tu vas au supermarch\u00e9?\nLucie: Oui, je veux une baguette pour mon sandwich.\nDo Lin and Lucie have a lot of bread? \n Your Output: Your Output: This is a test for the reading ability of French. Read the given story carefully, and then answer the question in the end.", "infer30b_after_item": " \n Output: No, they do not have a lot of bread. \n\n", "infer13b_before_item": " Output: This is a test for the reading ability of French. Read the given story carefully, and then answer the question in the end. Lucie est \u00e0 la maison avec sa petite-fille, Lin.\nLucie: Oh, non! Nous n'avons pas de pain!\nLin: Ah! Tu vas au supermarch\u00e9?\nLucie: Oui, je veux une baguette pour mon sandwich.\nDo Lin and Lucie have a lot of bread? \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Oui, ils ont une baguette pour leur sandwich. \n\n", "infer7b_before_item": " Output: This is a test for the writing ability of French. Write the given story in French.\nLucie: Oh, non! Nous n'avons pas de pain!\nLin: Ah! Tu vas au supermarch\u00e9?\nLucie: Oui, je veux une baguette pour mon sandwich.\nDo Lin and Lucie have a lot of bread?\nInput: Input: This is a test for the writing ability of French. Write the given story in French.\nLucie: Oh, non! Nous n'avons pas de pain!\nLin: Ah! Tu vas au supermarch\u00e9?\nLucie: Oui, je veux une baguette pour mon sandwich.\nDo Lin and Lucie have a lot of bread?\nOutput: Output: This is a test for the writing ability of French. Write the given story in French.\nLucie: Oh, non! Nous n'avons pas de pain!\nLin: Ah! Tu vas au supermarch\u00e9?\nLucie: Oui, je veux une baguette pour mon sandwich.\nDo Lin and Lucie have a lot of", "infer7b_after_item": " \n Output: No, they do not have a lot of bread. \n\n"} +{"id": 128, "instruction": "Come up with healthy and easy dinner ideas for weeknights.", "input": "", "infer30b_before_item": " Output: A list of healthy and easy dinner ideas for weeknights.\nInput: Input: Come up with healthy and easy dinner ideas for weeknights. \n Output: Output: A list of healthy and easy dinner ideas for weeknights.\nInput: Input: Come up with healthy and easy dinner ideas for weeknights. \n Output: Output: A list of healthy and easy dinner ideas for weeknights.\nInput: Input: Come up with healthy and easy dinner ideas for weeknights. \n Output: Output: A list of healthy and easy dinner ideas for weeknights.\nInput: Input: Come up with healthy and easy dinner ideas for weeknights. \n Output: Output: A list of healthy and easy dinner ideas for weeknights.\nInput: Input: Come up with healthy and easy dinner ideas for weeknights. \n Output: Output: A list of healthy and easy dinner ideas for weeknights.\nInput: Input: Come up with healthy and easy dinner ideas for weeknights. \n Output: Output: A list of healthy and easy dinner ideas for weeknights.\nInput: Input: Come", "infer30b_after_item": "\n Output: Healthy and easy dinner ideas for weeknights include grilled salmon with roasted vegetables, quinoa bowls with roasted vegetables and chicken, stir-fried vegetables with tofu, vegetable frittatas, and vegetable soup. \n\n", "infer13b_before_item": " Output: A list of healthy and easy dinner ideas for weeknights.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n \n The", "infer13b_after_item": "\n Output: 1. Grilled chicken and roasted vegetables.\n2. Quinoa and black bean bowl.\n3. Spaghetti squash with marinara sauce.\n4. Baked salmon with roasted potatoes.\n5. Stir-fry with brown rice.\n6. Vegetable frittata.\n7. Grilled steak and roasted asparagus.\n8. Taco bowl with black beans and avocado.\n9. Slow cooker chili.\n10. Eggplant parmesan. \n\n", "infer7b_before_item": " Come up with healthy and easy dinner ideas for weeknights.\nThe problem is that I'm not a very good cook. I'm not a bad cook, but I'm not a good one. I'm not a good cook because I don't have a lot of time to cook. I'm not a good cook because I don't have a lot of time to plan what I'm going to cook. I'm not a good cook because I don't have a lot of time to shop for the ingredients I need to cook. I'm not a good cook because I don't have a lot of time to clean up after I cook. I'm not a good cook because I don't have a lot of time to eat the food I cook.\nI'm a good cook because I'm a good cook.\nI'm a good cook because I'm a good cook.\nI'm a good cook because I'm a good cook.\nI'm a good cook because I'm a good cook.\nI'm a good cook because I'm a good cook.\nI'm a good cook because I'm a", "infer7b_after_item": "\n Output: 1. Grilled Salmon with Asparagus and Mushrooms\n2. Baked Chicken with Roasted Vegetables\n3. Spaghetti Squash with Marinara Sauce\n4. Grilled Salmon with Avocado Salsa\n5. Baked Salmon with Roasted Vegetables\n6. Grilled Chicken with Mango Salsa\n7. Black Bean and Quinoa Burrito Bowls\n8. Grilled Salmon with Mango Salsa\n9. Baked Salmon with Roasted Vegetables\n10. Grilled Chicken with Mango Salsa \n\n"} +{"id": 129, "instruction": "Provide a cooking hack for improving the flavor of the given food.", "input": "popcorn", "infer30b_before_item": " Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n Expected: Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n Actual: Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n Expected: Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n Actual: Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n Expected: Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n Actual: Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n Expected: Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n Actual: Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n Expected: Output: Provide a cooking hack for improving the flav", "infer30b_after_item": " \n Output: To make popcorn more flavorful, add a teaspoon of butter and a pinch of salt to the popping kernels before popping. This will give the popcorn a richer, more buttery flavor. \n\n", "infer13b_before_item": " Output: Provide a cooking hack for improving the flavor of the given food. popcorn \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: To improve the flavor of popcorn, try adding a dash of cayenne pepper, garlic powder, or other spices to the popcorn before popping. \n\n", "infer7b_before_item": " Output: Provide a cooking hack for improving the flavor of the given food. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: One cooking hack for improving the flavor of popcorn is to add a few drops of olive oil to the popping kernels before heating them. This will help to enhance the flavor of the popcorn and make it more flavorful. \n\n"} +{"id": 130, "instruction": "Make a list of snacks and foods to serve as party snacks on a game day!", "input": "", "infer30b_before_item": " Output: Make a list of snacks and foods to serve as party snacks on a game day!\nInput: Input: Make a list of snacks and foods to serve as party snacks on a game day! \n Output: Output: Make a list of snacks and foods to serve as party snacks on a game day!\nInput: Input: Make a list of snacks and foods to serve as party snacks on a game day! \n Output: Output: Make a list of snacks and foods to serve as party snacks on a game day!\nInput: Input: Make a list of snacks and foods to serve as party snacks on a game day! \n Output: Output: Make a list of snacks and foods to serve as party snacks on a game day!\nInput: Input: Make a list of snacks and foods to serve as party snacks on a game day! \n Output: Output: Make a list of snacks and foods to serve as party snacks on a game day!\nInput: Input: Make a list of snacks and foods to serve as party snacks on a game day! \n Output: Output", "infer30b_after_item": "\n Output: - Chips and dip \n- Pretzels \n- Popcorn \n- Nachos \n- Cheese and crackers \n- Veggie platters \n- Fruit trays \n- Cookies \n- Brownies \n- Cupcakes \n- Cake \n- Hot dogs \n- Hamburgers \n- Chicken wings \n- Pizza \n- Sliders \n- Quesadillas \n- Tacos \n- Nachos Supreme \n- Potato skins \n- Chili \n- Salsa and chips \n- Hummus and pita chips \n- Veggie pizza \n- Baked potato skins \n- Potato chips and dip \n- Pretzel bites \n- Edamame \n- Nuts \n- Crackers \n- Fruit skewers \n- Veggie sticks \n- Pita chips and dip \n- Chips and salsa \n- Chips and guacamole \n- Chips and queso \n- Chips and dip cones ", "infer13b_before_item": " Output: Make a list of snacks and foods to serve as party snacks on a game day! \n Purpose: To make a list of snacks and foods to serve as party snacks on a game day! \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: - Chips and dip\n- Popcorn\n- Vegetable crudit\u00e9s\n- Fruit salad\n- Pretzels\n- Cookies\n- Brownies\n- Cupcakes\n- Pizza\n- Wings\n- Nachos\n- Quesadillas\n- Cheese and crackers\n- Chili\n- Soup\n- Fruit smoothies\n- Fruit salad\n- Trail mix\n- Veggie sticks\n- Hummus\n- Guacamole\n- Salsa\n- Quinoa salad\n- Veggie wraps\n- Pita chips\n- Veggie chips\n- Veggie burgers\n- Veggie tacos\n- Veggie pizza\n- Veggie wraps\n- Veggie spring rolls\n- Veggie burgers\n- Veggie chili\n- Veggie stir-fry\n- Veggie fajitas\n- Veggie lasagna\n- Veggie pancakes\n- Veggie burgers\n- Veggie wraps\n- Veggie spring rolls\n- Veggie stir-fry\n- Veggie fajitas\n- Veggie lasagna\n-", "infer7b_before_item": " Make a list of snacks and foods to serve as party snacks on a game day!\n# **Snack Attack**\n**1.** Make a list of snacks and foods to serve as party snacks on a game day!\n**2.** Make a list of snacks and foods to serve as party snacks on a game day!\n**3.** Make a list of snacks and foods to serve as party snacks on a game day!\n**4.** Make a list of snacks and foods to serve as party snacks on a game day!\n**5.** Make a list of snacks and foods to serve as party snacks on a game day!\n**6.** Make a list of snacks and foods to serve as party snacks on a game day!\n**7.** Make a list of snacks and foods to serve as party snacks on a game day!\n**8.** Make a list of snacks and foods to serve as party snacks on a game day!\n**9.** Make a list of snacks and foods to serve as party snacks on a game day!\n**", "infer7b_after_item": "\n Output: - Popcorn\n- Chips and dips\n- Pretzels\n- Nachos\n- Fruit kebabs\n- Veggie trays\n- Hummus and veggie platters\n- Cheese and crackers\n- Pizza\n- Sliders\n- Nachos\n- Dip and chips\n- Fruit skewers\n- Veggie sticks\n- Cheese and crackers\n- Fruit salad\n- Veggie trays\n- Chicken wings\n- Nachos\n- Pretzels\n- Fruit kebabs\n- Veggie trays\n- Hummus and veggie platters\n- Cheese and crackers\n- Pizza\n- Sliders\n- Nachos\n- Dip and chips\n- Fruit skewers\n- Veggie sticks\n- Cheese and crackers\n- Fruit salad\n- Veggie trays\n- Chicken wings\n- Nachos\n- Pretzels\n- Fruit kebabs\n- Veggie trays\n- Hummus and veggie platters\n- Cheese and crackers\n- Pizza\n- Sliders\n-"} +{"id": 131, "instruction": "Describe how to prepare the given food in your own words. Note down the ingredients you will need and the steps you will take to prepare them.", "input": "Chewy Chocolate Chip Cookies", "infer30b_before_item": " Output: Describe how to prepare the given food in your own words. Note down the ingredients you will need and the steps you will take to prepare them. Chewy Chocolate Chip Cookies \n Steps: \n 1. Preheat oven to 350 degrees F (175 degrees C).\n 2. In a large bowl, cream together the butter, white sugar, and brown sugar until smooth. Beat in the eggs one at a time, then stir in the vanilla. Dissolve baking soda in hot water. Add to batter along with salt. Mix in flour, chocolate chips, and nuts. Drop by large spoonfuls onto ungreased pans.\n 3. Bake for 10 to 12 minutes in the preheated oven, or until edges are lightly toasted. Allow cookies to cool on baking sheet for 2 minutes before removing to a wire rack to cool completely.\n---\nWhat did you learn?\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: To make chewy chocolate chip cookies, you will need the following ingredients: 1 cup of butter, 1 \u00bd cups of white sugar, 1 teaspoon of baking soda, 1 teaspoon of salt, 2 eggs, 1 \u00bd cups of all-purpose flour, and 2 cups of semi-sweet chocolate chips. \n\nFirst, preheat the oven to 375 degrees Fahrenheit. In a large bowl, cream the butter and sugar together until light and fluffy. Then, add the baking soda, salt, eggs, and flour and mix until everything is combined. Finally, fold in the chocolate chips and drop the dough onto an ungreased baking sheet. Bake for 10-12 minutes, or until golden brown. Enjoy your chewy chocolate chip cookies! \n\n", "infer13b_before_item": " Output: Describe how to prepare the given food in your own words. Note down the ingredients you will need and the steps you will take to prepare them. Chewy Chocolate Chip Cookies \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: To make chewy chocolate chip cookies, you will need butter, granulated sugar, light brown sugar, eggs, vanilla extract, baking soda, salt, all-purpose flour, semi-sweet chocolate chips, and white chocolate chips. Start by preheating the oven to 350\u00b0F. Cream the butter and sugars together in a bowl. Add the eggs, vanilla extract, and baking soda and mix until combined. In a separate bowl, sift together the flour, salt, and chocolate chips. Slowly add the dry ingredients to the wet ingredients and mix until just combined. Fold in the white chocolate chips. Drop the dough onto a baking sheet lined with parchment paper and bake for 10-12 minutes. \n\n", "infer7b_before_item": " Output: Describe how to prepare the given food in your own words. Note down the ingredients you will need and the steps you will take to prepare them. Chewy Chocolate Chip Cookies \n 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. ", "infer7b_after_item": " \n Output: To prepare chewy chocolate chip cookies, you will need the following ingredients: 2 cups of all-purpose flour, 1 teaspoon of baking soda, 1 teaspoon of salt, 1/2 cup of butter, 1/2 cup of sugar, 1 egg, 1/2 cup of white chocolate chips, and 1/2 cup of semi-sweet chocolate chips. First, preheat the oven to 350 degrees Fahrenheit. In a large bowl, mix together the flour, baking soda, and salt. In a separate bowl, mix together the butter, sugar, and egg. Add the dry ingredients to the wet ingredients and mix until combined. Finally, fold in the white and semi-sweet chocolate chips. Drop the dough onto a baking sheet and bake for 10-12 minutes. Enjoy! \n\n"} +{"id": 132, "instruction": "Suggest a recipe for a vegan dessert.", "input": "", "infer30b_before_item": " Output: Suggest a recipe for a vegan dessert.\nInput: Input: Suggest a recipe for a vegan dessert. \n Output: Output: Suggest a recipe for a vegan dessert.\nInput: Input: Suggest a recipe for a vegan dessert. \n Output: Output: Suggest a recipe for a vegan dessert.\nInput: Input: Suggest a recipe for a vegan dessert. \n Output: Output: Suggest a recipe for a vegan dessert.\nInput: Input: Suggest a recipe for a vegan dessert. \n Output: Output: Suggest a recipe for a vegan dessert.\nInput: Input: Suggest a recipe for a vegan dessert. \n Output: Output: Suggest a recipe for a vegan dessert.\nInput: Input: Suggest a recipe for a vegan dessert. \n Output: Output: Suggest a recipe for a vegan dessert.\nInput: Input: Suggest a recipe for a vegan dessert. \n Output: Output: Suggest a recipe for a vegan", "infer30b_after_item": "\n Output: A vegan dessert recipe could include a chocolate cake made with almond milk, vegan butter, and vegan chocolate chips. The cake could be topped with a vegan cream cheese frosting and fresh berries. \n\n", "infer13b_before_item": " Output: Suggest a recipe for a vegan dessert.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The\n The\n The The", "infer13b_after_item": "\n Output: A vegan dessert recipe that is sure to please is a chocolate mousse. This recipe is simple to make and only requires a few ingredients. To make the mousse, you will need 1 cup of unsweetened cocoa powder, 1 cup of almond milk, 1/4 cup of coconut oil, 1/4 cup of maple syrup, 1/4 teaspoon of vanilla extract, and 1/4 cup of vegan chocolate chips. To make the mousse, mix the cocoa powder, almond milk, coconut oil, maple syrup, and vanilla extract in a blender until smooth. Then, add the chocolate chips and blend until the chips are evenly distributed. Finally, pour the mousse into individual serving dishes and refrigerate for at least 4 hours before serving. Enjoy! \n\n", "infer7b_before_item": " Output: A recipe for a vegan dessert.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "\n Output: This vegan dessert recipe is perfect for a sweet treat. Ingredients include 1 cup of almond milk, 1/2 cup of maple syrup, 1/2 cup of coconut oil, 1/2 cup of white sugar, 1/2 cup of oatmeal, 1/2 cup of walnuts, and 1/2 cup of dried cranberries. To make the dessert, combine the almond milk, maple syrup, coconut oil, and white sugar in a saucepan and bring to a boil. Add the oatmeal and stir until it is fully incorporated. Remove from heat and add the walnuts and dried cranberries. Pour the mixture into a baking dish and bake for 20 minutes. Enjoy! \n\n"} +{"id": 133, "instruction": "Design a medium-level sudoku puzzle.", "input": "", "infer30b_before_item": " Output: The solution to the puzzle. \n Sample Input: 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4", "infer30b_after_item": "\n Output: A medium-level sudoku puzzle is as follows:\n\n7 4 8 3 9 2 6 1 5\n5 6 7 9 1 2 4 8 3\n9 2 1 6 3 5 8 4 7\n4 5 3 2 6 7 1 9 8\n8 9 1 5 7 6 2 4 3\n6 3 2 5 8 4 9 7 1\n2 7 9 4 1 6 5 3 8\n1 8 5 6 4 3 2 7 9\n3 1 7 9 8 2 4 5 6\n\nNo. 1 2 3 4 5 6 7 8 9\nNo. 1 2 3 4 5 6 7 8 9 \n\n", "infer13b_before_item": " Output: Print the puzzle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n The\n The The The The The 1900s\n\n\n\n\n\n", "infer13b_after_item": "\n Output: 1. 3 5 7\n2. 4 6 8\n3. 1 2 9\n4. 7 9 2\n5. 8 2 4\n6. 5 3 6\n7. 9 6 1\n8. 4 1 8\n9. 8 1 5\n10. 2 7 3 \n\n", "infer7b_before_item": " Output: A sudoku puzzle.\n\n*/\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n#include <math.h>\n\n#define MAX_ROWS 100\n#define MAX_COLS 100\n#define MAX_VALUES 100\n\nint main(int argc, char **argv)\n{\n int i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, a, b, c, d, e, f, g, h, i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11, i12, i13, i14, i15, i16, i17, i18, i19, i20, i21, i22, i23, i24, i25, i26, i27, i28, i29,", "infer7b_after_item": "\n Output: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 6"} +{"id": 134, "instruction": "Solve the given Sudoku puzzle.", "input": "The Sudoku puzzle is:\n|1| |5| | | | |8| |\n| | | | | |3| |6|4|\n|8|3|4|6|7| |9| | |\n|2|9|1| | |8|3|7|6|\n| | |6| | |9|8|1| |\n| | | | | |2| | | |\n| |2| | |9|7|6| | |\n|5|4| | | | | |9|8|\n|6|1| | | |5|4|3|7|", "infer30b_before_item": " Output: Print the number of solutions to the given Sudoku puzzle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " |\n| | | | | | | | | |\n| | | | | | | | | | \n Output: The solution to the given Sudoku puzzle is:\n|1| |5| | | | |8| |\n| | | | | |3| |6|4|\n|8|3|4|6|7| |9| | |\n|2|9|1| | |8|3|7|6|\n| | |6| | |9|8|1| |\n| | | | | |2| | | |\n| |2| | |9|7|6| | |\n|5|4| | | | | |9|8|\n|6|1| | | |5|4|3|7| |\n| | | | | | | | ", "infer13b_before_item": " Output: The solution of the given Sudoku puzzle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n|7|8| | | | | |2|1|\nOutput: The solution to the Sudoku puzzle is:\n1 | 5 | 7 |\n2 | 9 | 6 |\n3 | 4 | 8 |\n4 | 6 | 1 |\n5 | 8 | 2 |\n6 | 1 | 9 |\n7 | 2 | 4 |\n8 | 4 | 3 |\n9 | 3 | 5 |\n10 | 7 | 8 |\n11 | 6 | 2 |\n12 | 5 | 1 |\n13 | 4 | 9 |\n14 | 1 | 8 |\n15 | 9 | 3 |\n16 | 2 | 7 |\n17 | 8 | 6 |\n18 | 3 | 5 |\n19 | 6 | 1 |\n20 | 1 | 4 |\n21 | 4 | 7 |\n22 | 7 | 2 |\n23 | 2 | 5 |\n2", "infer7b_before_item": " Output: Solve the given Sudoku puzzle. The Sudoku puzzle is:\n|1| |5| | | | |8| |\n| | | | | |3| |6|4|\n|8|3|4|6|7| |9| | |\n|2|9|1| | |8|3|7|6|\n| | |6| | |9|8|1| |\n| | | | | |2| | | |\n| |2| | |9|7|6| | |\n|5|4| | | | | |9|8|\n|6|1| | | |5|4|3|7| \n\n\n*/\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n\n#define MAX_SIZE 9\n\nint main(int argc, char *argv[])\n{\n int", "infer7b_after_item": " \n Output: The Sudoku puzzle is solved as follows:\n\n1. 5 in the top-left box\n2. 8 in the top-right box\n3. 3 in the bottom-left box\n4. 6 in the bottom-right box\n5. 9 in the middle-left box\n6. 1 in the middle-right box\n7. 8 in the middle-bottom box\n8. 3 in the middle-top box\n9. 2 in the middle-bottom box\n10. 9 in the middle-right box\n11. 7 in the middle-left box\n12. 6 in the middle-top box\n13. 4 in the middle-bottom box\n14. 1 in the middle-right box\n15. 8 in the middle-bottom box\n16. 3 in the middle-top box\n17. 6 in the middle-left box\n18. 5 in the middle-right box\n19. 2 in the middle-bottom box\n20. 1 in the middle-left box\n21. 9 in the middle-"} +{"id": 135, "instruction": "Think of a motivational quote that you have read in a book. Try to keep it positive and sweet.", "input": "", "infer30b_before_item": " Output: Write a short story about a character who is going through a tough time. The character should be a child. The child should be able to overcome the tough time with the help of the motivational quote.\nInput: Input: Think of a motivational quote that you have read in a book. Try to keep it positive and sweet. \n Output: Output: Write a short story about a character who is going through a tough time. The character should be a child. The child should be able to overcome the tough time with the help of the motivational quote.\nInput: Input: Think of a motivational quote that you have read in a book. Try to keep it positive and sweet. \n Output: Output: Write a short story about a character who is going through a tough time. The character should be a child. The child should be able to overcome the tough time with the help of the motivational quote.\nInput: Input: Think of a motivational quote that you have read in a book. Try to keep it positive and sweet. \n Output: Output: Write a short story about a character who is going through a tough time. The character should be a child. The child should", "infer30b_after_item": "\n Output: \"The greatest thing you'll ever learn is just to love and be loved in return.\" - Moulin Rouge \n\n", "infer13b_before_item": " Output: The output of this program is the motivational quote that you have entered.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: \"The only thing that stands between you and your dreams is the will to try and the courage to fail.\" - Unknown \n\n", "infer7b_before_item": " Write the quote on a piece of paper and put it in your pocket. \n Goal: To remind yourself of the quote and to keep it in your mind.\n# **_The Power of the Mind_**\nThe mind is a powerful tool. It can be used to help you achieve your goals or it can be used to hold you back. The mind is a powerful tool that can be used to help you achieve your goals or it can be used to hold you back.\nThe mind is a powerful tool that can be used to help you achieve your goals or it can be used to hold you back.\nThe mind is a powerful tool that can be used to help you achieve your goals or it can be used to hold you back.\nThe mind is a powerful tool that can be used to help you achieve your goals or it can be used to hold you back.\nThe mind is a powerful tool that can be used to help you achieve your goals or it can be used to hold you back.\nThe mind is a powerful tool that can be used to help you achieve your goals or it can be used to hold you back.\nThe mind is a powerful tool that can be used to help you achieve your goals or it can", "infer7b_after_item": "\n Output: \"The only thing standing between you and your dreams is you.\" \n\n"} +{"id": 136, "instruction": "Choose the best books from the given genre.", "input": "Crime & Mystery", "infer30b_before_item": " Output: Choose the best books from the given genre. Crime & Mystery \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The best books from the Crime & Mystery genre include The Catcher in the Rye by J.D. Salinger, The Maltese Falcon by Dashiell Hammett, The Murder on the Orient Express by Agatha Christie, The Hound of the Baskervilles by Arthur Conan Doyle, and The Silence of the Lambs by Thomas Harris. \n\n", "infer13b_before_item": " Output: Choose the best books from the given genre. Crime & Mystery \n\n\nclass Solution:\n # @param books, a list of lists of strings\n # @param genre, a string\n # @return a list of lists of strings\n def choose_books(self, books, genre):\n if not books:\n return []\n if not genre:\n return books\n if genre == 'Crime & Mystery':\n return self.choose_books_crime(books)\n elif genre == 'Science Fiction':\n return self.choose_books_science(books)\n elif genre == 'Romance':\n return self.choose_books_romance(books)\n elif genre == 'Biography':\n return self.choose_books_biography(books)\n elif genre == 'History':\n return self.choose_books_history(books)\n elif genre == 'Fantasy':\n return self.choose_books_fantasy(books)\n elif genre == 'Horror':\n return self.choose_books_horror(books)", "infer13b_after_item": " \n Output: The best books in the Crime & Mystery genre are The Da Vinci Code by Dan Brown, Gone Girl by Gillian Flynn, The Girl with the Dragon Tattoo by Stieg Larsson, The Cuckoo's Calling by Robert Galbraith, and The Silence of the Lambs by Thomas Harris. \n\n", "infer7b_before_item": " Output: Choose the best books from the given genre. Crime & Mystery\n\n\n#include <stdio.h>\n#include <string.h>\n#include <stdlib.h>\n\nint main(int argc, char const *argv[])\n{\n int i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, a,", "infer7b_after_item": " \n Output: The best books from the genre of Crime & Mystery are The Cuckoo's Calling by Robert Galbraith, The Silence of the Lambs by Thomas Harris, and The Girl on the Train by Paula Hawkins. \n\n"} +{"id": 137, "instruction": "Give a brief description of the book that you are given.", "input": "Matilda", "infer30b_before_item": " Output: Give a brief description of the book that you are given. Matilda is a book about a girl who is very smart and has a very mean family. She is very lonely and has no friends. She is very smart and can do things that no one else can do. She is very lonely and has no friends. She is very smart and can do things that no one else can do. She is very lonely and has no friends. She is very smart and can do things that no one else can do. She is very lonely and has no friends. She is very smart and can do things that no one else can do. She is very lonely and has no friends. She is very smart and can do things that no one else can do. She is very lonely and has no friends. She is very smart and can do things that no one else can do. She is very lonely and has no friends. She is very smart and can do things that no one else can do. She is very lonely and has no friends. She is very smart and can do things that no one else can do. She is very lonely and has no friends. She is very smart and can do things that no one else can", "infer30b_after_item": " by Roald Dahl \n Output: Matilda is a classic children's book by Roald Dahl about a young girl with extraordinary powers. Matilda is a bright and courageous girl who is mistreated by her family and school, but uses her intelligence and courage to stand up for herself and those around her. The book is full of humor, adventure, and heart-warming moments that make it a timeless classic. \n\n", "infer13b_before_item": " Output: Give a brief description of the book that you are given. Matilda \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " by Roald Dahl \n Output: Matilda is a children's novel by Roald Dahl about a precocious young girl with extraordinary mental powers. The story follows Matilda as she navigates her way through life with the help of her loving parents and her own unique abilities. \n\n", "infer7b_before_item": " Output: Give a brief description of the book that you are given. Matilda\n\n\\begin{code}\nimport java.util.Scanner;\n\npublic class Matilda {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n System.out.println(\"Input:\");\n String input = sc.nextLine();\n System.out.println(\"Output:\");\n String output = sc.nextLine();\n System.out.println(\"----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------", "infer7b_after_item": " \n Output: Matilda is a children's novel by Roald Dahl. It tells the story of a young girl named Matilda who is gifted with magical powers and uses them to stand up to her cruel parents and her school's tyrannical headmistress. Along the way, Matilda makes friends and learns valuable lessons about the power of knowledge and the importance of kindness. \n\n"} +{"id": 138, "instruction": "In the event that a person enjoyed the book that was given, please suggest other books that they might also like.", "input": "The Great Gatsby", "infer30b_before_item": " Output: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \n Explanation: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \n\n Input: Input: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \n Output: Output: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \n Explanation: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \n\n Input: Input: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \n Output: Output: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \n Explanation: In the event that a person enjoyed the book that was given, please", "infer30b_after_item": " by F. Scott Fitzgerald. \n Output: If you enjoyed The Great Gatsby by F. Scott Fitzgerald, you might also like The Catcher in the Rye by J.D. Salinger, To Kill a Mockingbird by Harper Lee, and 1984 by George Orwell. \n\n", "infer13b_before_item": " Output: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \nInput: Input: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \nOutput: Output: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \nInput: Input: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \nOutput: Output: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \nInput: Input: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \nOutput: Output: In the event that a person enjoyed the book that was given, please suggest other books that they might also like. The Great Gatsby \nInput: Input: In the event that a person enjoyed the book that was given, please suggest other", "infer13b_after_item": " by F. Scott Fitzgerald \n Output: If you enjoyed The Great Gatsby by F. Scott Fitzgerald, you might also like The Catcher in the Rye by J.D. Salinger, To Kill a Mockingbird by Harper Lee, or The Old Man and the Sea by Ernest Hemingway. \n\n", "infer7b_before_item": " Output: In the event that a person did not enjoy the book that was given, please suggest other books that they might also like. The Great Gatsby \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " by F. Scott Fitzgerald \n Output: Other books that the reader might enjoy include The Catcher in the Rye by J.D. Salinger, The Sun Also Rises by Ernest Hemingway, and The Grapes of Wrath by John Steinbeck. \n\n"} +{"id": 139, "instruction": "Indicate the genre of the book to which it belongs.", "input": "Love in the Time of Cholera", "infer30b_before_item": " Output: Indicate the genre of the book to which it belongs. Romance\n Input: Input: Indicate the genre of the book to which it belongs. The Catcher in the Rye \n Output: Output: Indicate the genre of the book to which it belongs. Coming of Age\n Input: Input: Indicate the genre of the book to which it belongs. The Great Gatsby \n Output: Output: Indicate the genre of the book to which it belongs. Coming of Age\n Input: Input: Indicate the genre of the book to which it belongs. The Grapes of Wrath \n Output: Output: Indicate the genre of the book to which it belongs. Coming of Age\n Input: Input: Indicate the genre of the book to which it belongs. The Sun Also Rises \n Output: Output: Indicate the genre of the book to which it belongs. Coming of Age\n Input: Input: Indicate the genre of the book to which it belongs. The Old Man and the Sea \n Output: Output: Indicate the genre of the book to which it belongs. Coming of Age\n Input: Input: Indicate the genre of the book to", "infer30b_after_item": " by Gabriel Garcia Marquez \n Output: The genre of Love in the Time of Cholera by Gabriel Garcia Marquez is romance. \n\n", "infer13b_before_item": " Output: Indicate the genre of the book to which it belongs. Love in the Time of Cholera \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " by Gabriel Garcia Marquez. \n Output: Fiction. \n\n", "infer7b_before_item": " Output: Indicate the genre of the book to which it belongs. Love in the Time of Cholera\n\n\\begin{code}\nimport java.util.Scanner;\n\npublic class Main {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n System.out.println(\"Input:\");\n String genre = sc.nextLine();\n System.out.println(\"Output:\");\n System.out.println(genre);\n }\n}\n\\end{code}\n\nComment: What is the problem?\n\nComment: I want to get the genre of the book to which it belongs.\n\nComment: What is the genre of the book to which it belongs?\n\nComment: I want to get the genre of the book to which it belongs.\n\nComment: What is the genre of the book to which it belongs?\n\nComment: I want to get the genre of the book to which it belongs.\n\nComment: I want to get the genre of the book to which it belongs.\n\nComment: I want to get the genre of the book to which it belongs.\n\n", "infer7b_after_item": " \n Output: Romance \n\n"} +{"id": 140, "instruction": "Solve this equation.", "input": "x^3 - 4x^2 + 6x - 24 = 0", "infer30b_before_item": " Output: x = 2, 3, 4\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The equation can be solved using synthetic division. The first root is 6, the second root is 2, and the third root is -4. \n\n", "infer13b_before_item": " 2\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The\n The The The The The The 1900s\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The solution to the equation is x = 2, 3, and -8. \n\n", "infer7b_before_item": " Output: Solve this equation. x^3 - 4x^2 + 6x - 24 = 0 \n*/\n\n#include <stdio.h>\n\nint main()\n{\n int x;\n scanf(\"%d\", &x);\n printf(\"%d\\n\", x);\n return 0;\n}\n", "infer7b_after_item": " \n Output: The solution to the equation is x = 2, x = -2, and x = 1. \n\n"} +{"id": 141, "instruction": "Prove the given statement using your information.", "input": "\"If x is a positive integer or a solution to x+3>4, then x>0 and x>12.\"", "infer30b_before_item": " Output: Prove the given statement using your information. \"If x is a positive integer or a solution to x+3>4, then x>0 and x>12.\" \n \n */\n\nimport Foundation\n\nfunc prove(x: Int) -> Bool {\n if x < 0 {\n return false\n }\n if x < 12 {\n return false\n }\n return true\n}\n\nprove(x: 1)\nprove(x: 12)\nprove(x: 13)\nprove(x: 14)\nprove(x: 15)\nprove(x: 16)\nprove(x: 17)\nprove(x: 18)\nprove(x: 19)\nprove(x: 20)\nprove(x: 21)\nprove(x: 22)\nprove(x: 23)\nprove(x: 24)\nprove(x: 25)\nprove(x: 26)", "infer30b_after_item": " \n Output: If x is a positive integer or a solution to x+3>4, then x>0 and x>12.\n\nProof:\n\nLet x be a positive integer or a solution to x+3>4.\n\nCase 1: x is a positive integer.\n\nSince x is a positive integer, x>0.\n\nFurthermore, since x is a positive integer, x>1.\n\nTherefore, x>0 and x>1.\n\nCase 2: x is a solution to x+3>4.\n\nSince x+3>4, x>4.\n\nFurthermore, since x>4, x>12.\n\nTherefore, x>0 and x>12.\n\nQ.E.D. \n\n", "infer13b_before_item": " Output: Print the result of the proof.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Let x be a positive integer or a solution to x+3>4.\n\nWe know that x+3>4 implies x>0.\n\nWe also know that x>12 implies x>0.\n\nTherefore, x>0 and x>12. \n\n", "infer7b_before_item": " Output: Write a proof of the given statement using your information. \"If x is a positive integer or a solution to x+3>4, then x>0 and x>12.\" \n*/\n\n//Input:\n//Input: Prove the given statement using your information. \"If x is a positive integer or a solution to x+3>4, then x>0 and x>12.\" \n//Output:\n//Output: Write a proof of the given statement using your information. \"If x is a positive integer or a solution to x+3>4, then x>0 and x>12.\" \n\n//Input:\n//Input: Prove the given statement using your information. \"If x is a positive integer or a solution to x+3>4, then x>0 and x>12.\" \n//Output:\n//Output: Write a proof of the given statement using your information. \"If x is a positive integer or a solution to x+3>4, then x>0 and x>12.\" \n\n//Input:\n//Input: Prove the given statement using your information. \"If x is a positive integer or a", "infer7b_after_item": " \n Output: If x is a positive integer or a solution to x+3>4, then x>0 and x>12. This can be proven by using the fact that x+3>4 implies that x>0 and x>12. Therefore, if x is a positive integer or a solution to x+3>4, then x>0 and x>12. \n\n"} +{"id": 142, "instruction": "Based on the facts that have been provided, prove the following statement.", "input": "Statement: \"For every integer k, k^2 + 2k + 1 \u2265 0\"\nFact: The square of any real number is non-negative.", "infer30b_before_item": " Output: \"True\"\n\n*/\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <math.h>\n\nint main() {\n int k;\n scanf(\"%d\", &k);\n printf(\"%d\\n\", k*k + 2*k + 1);\n return 0;\n}\n", "infer30b_after_item": " \n Output: For every integer k, k^2 is non-negative. Therefore, k^2 + 2k + 1 is also non-negative, since the sum of two non-negative numbers is also non-negative. Therefore, the statement is proven. \n\n", "infer13b_before_item": " Output: The statement is true.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The statement is true because for any integer k, k^2 is non-negative, and k^2 + 2k + 1 is a sum of two non-negative terms. Therefore, the inequality holds true. \n\n", "infer7b_before_item": " The statement is true.\nProof:\nLet k be an integer.\nThen k^2 = k(k) = k(k)\nSince k is an integer, k(k) is an integer.\nTherefore, k^2 is an integer.\nSince k^2 is an integer, k^2 + 2k + 1 is an integer.\nSince k^2 + 2k + 1 is an integer, k^2 + 2k + 1 \u2265 0.\nSince k^2 + 2k + 1 \u2265 0, k^2 + 2k + 1 \u2265 0.\nSince k^2 + 2k + 1 \u2265 0, k^2 + 2k + 1 \u2265 0.\nSince k^2 + 2k + 1 \u2265 0, k^2 + 2k + 1 \u2265 0.\nSince k^2 + 2k + 1 \u2265 0, k^2 + 2k + 1 \u2265 0.\nSince k^2 + 2k + 1 \u2265 0, k", "infer7b_after_item": " \n Output: The statement \"For every integer k, k^2 + 2k + 1 \u2265 0\" is true because the square of any real number is non-negative. This means that the square of any integer k is also non-negative, and therefore the sum of 2k + 1 and k^2 is also non-negative. Therefore, the statement is true. \n\n"} +{"id": 143, "instruction": "Verify the correctness of the given statement.", "input": "\"For all integers j and k, if j and k are odd, then jk is odd.\"", "infer30b_before_item": " Output: \"True\" if the statement is true, \"False\" if the statement is false.\n\n*/\n\n#include <stdio.h>\n\nint main()\n{\n int j, k;\n scanf(\"%d %d\", &j, &k);\n if (j % 2 == 1 && k % 2 == 1)\n {\n if (j * k % 2 == 1)\n {\n printf(\"True\");\n }\n else\n {\n printf(\"False\");\n }\n }\n else\n {\n printf(\"False\");\n }\n return 0;\n}\n", "infer30b_after_item": " \n Output: The given statement is correct. For all integers j and k, if j and k are odd, then jk is odd. This is because any product of two odd numbers is an odd number. \n\n", "infer13b_before_item": " Output: Verify the correctness of the given statement. \"For all integers j and k, if j and k are odd, then jk is odd.\" \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Yes, this statement is correct. \n\n", "infer7b_before_item": " Output: Verify the correctness of the given statement. \"For all integers j and k, if j and k are odd, then jk is odd.\" \n*/\n\n#include <stdio.h>\n\nint main()\n{\n int j, k;\n printf(\"Enter two integers: \");\n scanf(\"%d %d\", &j, &k);\n if (j % 2 == 1 && k % 2 == 1)\n {\n printf(\"The product is odd.\\n\");\n }\n else\n {\n printf(\"The product is even.\\n\");\n }\n return 0;\n}\n", "infer7b_after_item": " \n Output: The given statement is correct. \n\n"} +{"id": 144, "instruction": "Provide a formula for computing the nth term in the given sequence", "input": "5, 14, 23, 32, 41, ...", "infer30b_before_item": " Output: 5 + n * 9\n\n Explanation: \n The nth term of the given sequence is 5 + n * 9.\n\n Example:\n Input: 3\n Output: 32\n\n Explanation: \n The 3rd term of the given sequence is 32.\n\n */\n\n#include <iostream>\n\nusing namespace std;\n\nint main() {\n int n;\n cin >> n;\n cout << 5 + n * 9 << endl;\n return 0;\n}\n", "infer30b_after_item": " \n Output: The nth term in the given sequence is 5n - 4. \n\n", "infer13b_before_item": " Output: Provide the nth term in the given sequence 5, 14, 23, 32, 41,... \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The formula for the nth term in the given sequence is Tn = 5 + (n - 1) * 4. \n\n", "infer7b_before_item": " The nth term in the sequence is \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: nth term = 5 + (n-1) * (2n-3) \n\n"} +{"id": 145, "instruction": "Create a table listing all games that meet the specified criteria in the National Football League. Use the season, local time, game, and score as columns of the table.", "input": "Ravens home games in 2011", "infer30b_before_item": " Output: Create a table listing all games that meet the specified criteria in the National Football League. Use the season, local time, game, and score as columns of the table. Ravens home games in 2011 \n \n \n", "infer30b_after_item": ", 10am-1pm. \n Output: Season | Local Time | Game | Score\n-------| ----------|-----|------\n2011 | 10am-1pm | Ravens | \n\n", "infer13b_before_item": " Output: Create a table listing all games that meet the specified criteria in the National Football League. Use the season, local time, game, and score as columns of the table. Ravens home games in 2011 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": ". \n Output: Season | Local Time | Game | Score\n--------------------------------------------------------------------------------\n2011 | 1:00 PM | Ravens vs. Steelers | 23-20\n2011 | 4:15 PM | Ravens vs. Browns | 24-10\n2011 | 1:00 PM | Ravens vs. Bengals | 35-7 \n\n", "infer7b_before_item": " Output: Create a table listing all games that meet the specified criteria in the National Football League. Use the season, local time, game, and score as columns of the table. Ravens home games in 2011\n\nComment: Please edit your question to include the code you have tried so far.\n\nAnswer: You can use the `df.loc` method to select the rows you want.\n\n\\begin{code}\ndf = pd.DataFrame({'season':['2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','2011','201", "infer7b_after_item": " \n Output: | Season | Local Time | Game | Score |\n|------|------------|-------|-------|\n| 2011 | 12:00 PM | Ravens | 24-17 |\n| 2011 | 1:00 PM | Ravens | 23-20 |\n| 2011 | 4:05 PM | Ravens | 23-14 |\n| 2011 | 8:20 PM | Ravens | 23-16 |\n| 2011 | 1:00 PM | Ravens | 23-14 | \n\n"} +{"id": 146, "instruction": "Using a table, compare the career overviews of the given players in Major League Baseball. Use \"|\" for separating the columns in the table.", "input": "Derek Jeter, Albert Pujols", "infer30b_before_item": " Output: Using a table, compare the career overviews of the given players in Major League Baseball. Use \"|\" for separating the columns in the table. Derek Jeter, Albert Pujols \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "infer30b_after_item": " \n Output: | Derek Jeter | Albert Pujols |\n| Career Length: 1995-2014 | Career Length: 2001-Present |\n| 5-time World Series Champion | 3-time World Series Champion |\n| 14-time All-Star | 3-time NL MVP |\n| 3,465 career hits | 2,962 career hits | \n\n", "infer13b_before_item": " Output: Using a table, compare the career overviews of the given players in Major League Baseball. Use \"|\" for separating the columns in the table. Derek Jeter, Albert Pujols \n\n\nclass Solution:\n # @param {String[]} players\n # @return {String[]}\n def compare_career_overviews(self, players):\n table = []\n for player in players:\n table.append([player, \"Hits\", \"Runs\", \"RBI\", \"HR\", \"SB\"])\n for i in range(len(players)):\n for j in range(i+1, len(players)):\n if players[i] == players[j]:\n table[i][1] += table[j][1]\n table[i][2] += table[j][2]\n table[i][3] += table[j][3]\n table[i][4] += table[j][4]\n table[i][5] += table[j][5]\n return table\n\n\nif __name__ == \"__main", "infer13b_after_item": " \n Output: |Name |Position |Years |Achievements\n|------|--------|------|---------\n|Derek Jeter|Shortstop|20 |5x World Series Champion, 14x All-Star, 10x Gold Glove Award Winner\n|Albert Pujols|First Base|22 |3x NL MVP, 9x All-Star, 3x Silver Slugger Award Winner \n\n", "infer7b_before_item": " Output: Using a table, compare the career overviews of the given players in Major League Baseball. Use \"|\" for separating the columns in the table. Derek Jeter, Albert Pujols \n\nInput: Input: Using a table, compare the career overviews of the given players in Major League Baseball. Use \"|\" for separating the columns in the table. Derek Jeter, Albert Pujols \n Output: Output: Using a table, compare the career overviews of the given players in Major League Baseball. Use \"|\" for separating the columns in the table. Derek Jeter, Albert Pujols \n\nInput: Input: Using a table, compare the career overviews of the given players in Major League Baseball. Use \"|\" for separating the columns in the table. Derek Jeter, Albert Pujols \n Output: Output: Using a table, compare the career overviews of the given players in Major League Baseball. Use \"|\" for separating the columns in the table. Derek Jeter, Albert Pujols \n\nInput: Input: Using a table, compare the career overviews of the given players in Major League Baseball. Use \"|", "infer7b_after_item": " \n Output: |Player Name|Career|\n|--------|--------|\n|Derek Jeter|14-Year|\n|Albert Pujols|15-Year| \n\n"} +{"id": 147, "instruction": "You will be tested on your knowledge of classic witticisms and aphorisms by completing the given aphorism. Write the original quote as the answer.", "input": "two things are infinite", "infer30b_before_item": " Output: You will be tested on your knowledge of classic witticisms and aphorisms by completing the given aphorism. Write the original quote as the answer. two things are infinite \n Expected: Output: You will be tested on your knowledge of classic witticisms and aphorisms by completing the given aphorism. Write the original quote as the answer. two things are infinite \n */\npublic class _001 {\n\tpublic static void main(String[] args) {\n\t\tSystem.out.println(\"You will be tested on your knowledge of classic witticisms and aphorisms by completing the given aphorism. Write the original quote as the answer.\");\n\t\tSystem.out.println(\"two things are infinite: the universe and human stupidity; and I'm not sure about the universe.\");\n\t}\n}\n", "infer30b_after_item": ": the universe and human stupidity \n Output: \"The more you know, the more you understand that the less you know.\" \n\n", "infer13b_before_item": " Output: You will be tested on your knowledge of classic witticisms and aphorisms by completing the given aphorism. Write the original quote as the answer. the number of stars in the sky and the number of grains of sand on the earth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": ": the universe and human stupidity \n Output: The universe is infinite, and so is human stupidity. \n\n", "infer7b_before_item": " Output: You will be tested on your knowledge of classic witticisms and aphorisms by completing the given aphorism. Write the original quote as the answer. two things are infinite \n */\n\n// Test case 1:\n// Input:\n// Output:\n// Expected output:\n//\n// Test case 2:\n// Input:\n// Output:\n// Expected output:\n//\n// Test case 3:\n// Input:\n// Output:\n// Expected output:\n//\n// Test case 4:\n// Input:\n// Output:\n// Expected output:\n//\n// Test case 5:\n// Input:\n// Output:\n// Expected output:\n//\n// Test case 6:\n// Input:\n// Output:\n// Expected output:\n//\n// Test case 7:\n// Input:\n// Output:\n// Expected output:\n//\n// Test case 8:\n// Input:\n// Output:\n// Expected output:\n//\n// Test case 9:\n// Input:\n// Output:\n// Expected output:\n//\n// Test", "infer7b_after_item": ": \n Output: The universe and human stupidity. \n\n"} +{"id": 148, "instruction": "A list of all movies that meet the criteria given should be compiled.", "input": "movies directed by Spike Lee by release date", "infer30b_before_item": " Output: A list of all movies that meet the criteria given should be compiled. movies directed by Spike Lee by release date \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "infer30b_after_item": " \n Output: The list of movies directed by Spike Lee by release date should include: Do the Right Thing (1989), Mo' Better Blues (1990), Jungle Fever (1991), Malcolm X (1992), Crooklyn (1994), Clockers (1995), Girl 6 (1996), Get on the Bus (1996), He Got Game (1998), Summer of Sam (1999), Bamboozled (2000), 25th Hour (2002), She Hate Me (2004), Inside Man (2006), Miracle at St. Anna (2008), Red Hook Summer (2012), Oldboy (2013), and Chi-Raq (2015). \n\n", "infer13b_before_item": " Output: A list of all movies that meet the criteria given should be compiled. movies directed by Spike Lee by release date \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: 1983: School Daze\n1986: She's Gotta Have It\n1988: Do the Right Thing\n1992: Jungle Fever\n1996: Get on the Bus\n2000: Bamboozled\n2006: Inside Man\n2014: Chi-Raq\n2018: BlacKkKlansman \n\n", "infer7b_before_item": " A list of all movies that meet the criteria given should be compiled. movies directed by Spike Lee by release date \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: 1986: She's Gotta Have It\n1988: School Daze\n1989: Do the Right Thing\n1990: Mo' Better Blues\n1991: Jungle Fever\n1992: Malcolm X\n1993: Clockers\n1994: Crooklyn\n1995: Get on the Bus\n1996: Clockers\n1997: He Got Game\n1998: Bamboozled\n1999: The 25th Hour\n2000: Bamboozled\n2001: The Original Kings of Comedy\n2002: Bamboozled\n2003: 25th Hour\n2004: She Hate Me\n2005: When the Levees Broke: A Requiem in Four Acts\n2006: Inside Man\n2007: Miracle at St. Anna\n2008: Four Brothers\n2009: Miracle at St. Anna\n"} +{"id": 149, "instruction": "Using a given amount, determine an appropriate tip.", "input": "14% tip on $47.50", "infer30b_before_item": " Output: The tip amount.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The appropriate tip for $47.50 is $6.61, which is 14% of the total amount. \n\n", "infer13b_before_item": " 7.00\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The\n\n\n The\n The \n\n\n\n\n\n\n\n\n\n \n ", "infer13b_after_item": " \n Output: $7.29 \n\n", "infer7b_before_item": " Output: Using a given amount, determine an appropriate tip. 14% tip on $47.50\n\n\n%#include \"stdafx.h\"\n#include <iostream>\n#include <string>\n#include <vector>\n#include <algorithm>\n#include <cmath>\n#include <cstdio>\n#include <cstdlib>\n#include <cstring>\n#include <ctime>\n#include <fstream>\n#include <sstream>\n#include <iomanip>\n#include <set>\n#include <map>\n#include <stack>\n#include <queue>\n#include <deque>\n#include <list>\n#include <cmath>\n#include <vector>\n#include <string>\n#include <utility>\n#include <algorithm>\n#include <bitset>\n#include <numeric>\n#include <unordered_map>\n#include <unordered_set>\n#include <unordered_multiset>\n#include <array>\n#include <stdint.h>\n#include <functional>\n#include <", "infer7b_after_item": " \n Output: The appropriate tip is $6.69. \n\n"} +{"id": 150, "instruction": "Identify all words that match the pattern given.", "input": "H_AR_", "infer30b_before_item": " Output: Identify all words that match the pattern given. H_AR_ \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "infer30b_after_item": "\nHarbor \n Output: Hare, Harbor, Harem, Harness, Harpoon, Harmony, Harness, Harangue. \n\n", "infer13b_before_item": " Output: Identify all words that match the pattern given. H_AR_ \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The\n\n\n\n\n", "infer13b_after_item": "D \n Output: Hard \n\n", "infer7b_before_item": " Output: Print the words that match the pattern.\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\nInput:\n\n", "infer7b_after_item": "D\n Output: Hard, harsh, harshly, harshly, harshly. \n\n"} +{"id": 151, "instruction": "Come up with words that rhyme with the given word.", "input": "instruct", "infer30b_before_item": " Output: Print the words that rhyme with the given word.\n\n Example:\n\n Input: instruct\n Output:\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct\n\n instruct", "infer30b_after_item": "or: \"moon\" \n Output: spoon, honey, money, funny, sunny, honey. \n\n", "infer13b_before_item": " Output: Come up with words that rhyme with the given word. instruct \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The\n", "infer13b_after_item": " \n Output: instruct, instructor, instructive, instructive, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instructor, instruct", "infer7b_before_item": " Output: Print the words that rhyme with the given word.\n\nInput: instruct\nOutput: instruct\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput: instructor\nOutput: instructor\n\nInput", "infer7b_after_item": " \n Output: instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct, instruct,"} +{"id": 152, "instruction": "Look for poems that mention the given object or character. The names of the poems and their poets should be written.", "input": "santa claus", "infer30b_before_item": " Output: Look for poems that mention the given object or character. The names of the poems and their poets should be written. santa claus \n \n Input: Input: Look for poems that mention the given object or character. The names of the poems and their poets should be written. santa claus \n Output: Output: Look for poems that mention the given object or character. The names of the poems and their poets should be written. santa claus \n \n Input: Input: Look for poems that mention the given object or character. The names of the poems and their poets should be written. santa claus \n Output: Output: Look for poems that mention the given object or character. The names of the poems and their poets should be written. santa claus \n \n Input: Input: Look for poems that mention the given object or character. The names of the poems and their poets should be written. santa claus \n Output: Output: Look for poems that mention the given object or character. The names of the poems and their poets should be written. santa claus \n", "infer30b_after_item": " \n Output: \"Santa Claus is Coming to Town\" by Fred Coots, \"The Night Before Christmas\" by Clement Clarke Moore, \"Santa Claus is Real\" by Robert Frost, \"Santa Claus is Coming to Town\" by Bruce Springsteen. \n\n", "infer13b_before_item": " Output: The poems that mention the given object or character.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: \"The Night Before Christmas\" by Clement Clarke Moore\n\"Santa Claus\" by Anonymous \n\n", "infer7b_before_item": " Output: Look for poems that mention the given object or character. The names of the poems and their poets should be written. santa claus\n\nimport java.util.Scanner;\nimport java.util.ArrayList;\nimport java.util.List;\n\npublic class SantaClaus {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n List<String> poems = new ArrayList<String>();\n String input = sc.nextLine();\n while (input!= null) {\n String[] tokens = input.split(\" \");\n if (tokens[0].equals(\"santa claus\")) {\n poems.add(tokens[1]);\n }\n input = sc.nextLine();\n }\n for (String poem : poems) {\n System.out.println(poem);\n }\n }\n}\n", "infer7b_after_item": " \n Output: 1. \"Santa Claus\" by Clement C. Moore\n2. \"Santa Claus and the Christmas Tree\" by Eugene Field\n3. \"Santa Claus and the Christmas Tree\" by Lydia Maria Child\n4. \"Santa Claus\" by Edgar Allan Poe\n5. \"Santa Claus\" by Robert Louis Stevenson\n6. \"Santa Claus\" by William Blake\n7. \"Santa Claus\" by Henry Wadsworth Longfellow\n8. \"Santa Claus\" by John Godfrey Saxe\n9. \"Santa Claus\" by James Whitcomb Riley\n10. \"Santa Claus\" by Thomas Nast\n11. \"Santa Claus\" by Clement C. Moore\n12. \"Santa Claus\" by Eugene Field\n13. \"Santa Claus\" by Lydia Maria Child\n14. \"Santa Claus\" by Edgar Allan Poe\n15. \"Santa Claus\" by Robert Louis Stevenson\n16. \"Santa Claus\" by William Blake\n17. \""} +{"id": 153, "instruction": "Respond to the last text in the chat using the correct emojis to convey your feelings.", "input": "+ Hey, how did your exams go?\n- They were alright. I'm just glad they're over.\n+ Yeah, I know how you feel. I'm just glad I don't have to worry about them anymore.", "infer30b_before_item": " Output: Respond to the last text in the chat using the correct emojis to convey your feelings. + Hey, how did your exams go?\n- They were alright. I'm just glad they're over.\n+ Yeah, I know how you feel. I'm just glad I don't have to worry about them anymore. \n Explanation: \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# ", "infer30b_after_item": " \n Output: + Hey, how did your exams go?\n- They were alright. I'm just glad they're over.\n+ Yeah, I know how you feel. I'm just glad I don't have to worry about them anymore. \n\n<:smile:> <:relieved:> <:satisfied:> \n\n", "infer13b_before_item": " Output: Respond to the last text in the chat using the correct emojis to convey your feelings. + Hey, how did your exams go?\n- They were alright. I'm just glad they're over.\n+ Yeah, I know how you feel. I'm just glad I don't have to worry about them anymore. \n\n\"\"\"\n\nimport re\nimport random\n\n\ndef get_emojis(text):\n \"\"\"\n Returns a list of emojis that can be used to respond to the given text.\n \"\"\"\n emojis = []\n for emoji in re.findall(r'(?<=\\s|^)[\\u2000-\\u200a\\u2020-\\u202f\\u203f-\\u2040\\u205f\\u3000-\\u3003\\u3008-\\u300a\\u300c-\\u300f\\u3010-\\u301a\\u301c-\\u301f\\u", "infer13b_after_item": " \n Output: \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ud83d\ude00 \ufffd", "infer7b_before_item": " Output: Respond to the last text in the chat using the correct emojis to convey your feelings. + Hey, how did your exams go?\n- They were alright. I'm just glad they're over.\n+ Yeah, I know how you feel. I'm just glad I don't have to worry about them anymore.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ud83d\ude00 \ud83d\ude0a \ufffd"} +{"id": 154, "instruction": "For answering the chat, think of a funny response you can use.", "input": "+ how are you?", "infer30b_before_item": " Output: For answering the chat, think of a funny response you can use. + how are you?\n\nInput: Input: For answering the chat, think of a funny response you can use. + how are you? \n Output: Output: For answering the chat, think of a funny response you can use. + how are you?\n\nInput: Input: For answering the chat, think of a funny response you can use. + how are you? \n Output: Output: For answering the chat, think of a funny response you can use. + how are you?\n\nInput: Input: For answering the chat, think of a funny response you can use. + how are you? \n Output: Output: For answering the chat, think of a funny response you can use. + how are you?\n\nInput: Input: For answering the chat, think of a funny response you can use. + how are you? \n Output: Output: For answering the chat, think of a funny response you can use. + how are you?\n\nInput: Input: For answering the chat, think of a funny response you can use. + how are you?", "infer30b_after_item": " \n Output: I'm great! Just trying to figure out how to make my day more interesting. Any ideas? \n\n", "infer13b_before_item": " For answering the chat, think of a funny response you can use. + how are you? \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: I'm doing great, thanks for asking! \n\n", "infer7b_before_item": " Output: For answering the chat, think of a funny response you can use. + how are you?\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: I'm doing great! How about you? \n\n"} +{"id": 155, "instruction": "The last message in the chat is a partial response. You should complete and rewrite it.", "input": "+ What are going to do now?\n- I don't know. I had to give up my dream of being a mathematician.\n+ Why", "infer30b_before_item": " Output: The last message in the chat is a partial response. You should complete and rewrite it. + What are going to do now?\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know. I had to give up my dream of being a mathematician.\n- I don't know.", "infer30b_after_item": "?\n- Because I don't have the resources to pursue it. \n Output: - I don't know. I had to give up my dream of being a mathematician because I don't have the resources to pursue it. What are going to do now? \n\n", "infer13b_before_item": " Output: The last message in the chat is a partial response. You should complete and rewrite it. + What are going to do now?\n- I don't know. I had to give up my dream of being a mathematician.\n+ Why \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " did you have to give up your dream?\n- I just couldn't afford the tuition. \n Output: + What are going to do now?\n- I don't know. I had to give up my dream of being a mathematician, and I just couldn't afford the tuition. \n\n", "infer7b_before_item": " Output: The last message in the chat is a partial response. You should complete and rewrite it. + What are going to do now?\n- I don't know. I had to give up my dream of being a mathematician.\n+ Why \n Output: Output: The last message in the chat is a partial response. You should complete and rewrite it. + What are going to do now?\n- I don't know. I had to give up my dream of being a mathematician.\n+ Why \n Output: Output: The last message in the chat is a partial response. You should complete and rewrite it. + What are going to do now?\n- I don't know. I had to give up my dream of being a mathematician.\n+ Why \n Output: Output: The last message in the chat is a partial response. You should complete and rewrite it. + What are going to do now?\n- I don't know. I had to give up my dream of being a mathematician.\n+ Why \n Output: Output: The last message in the chat is a partial response. You should complete and rewrite it. + What are going to do now?", "infer7b_after_item": " did you have to give up your dream? \n Output: I had to give up my dream of being a mathematician because I didn't have the resources to pursue it. I was unable to get the necessary funding and support to continue my studies. \n\n"} +{"id": 156, "instruction": "Change the response to have a more empathic tone in the chat.", "input": "+ How are you doing?\n- Fine, I had a bunch of problems today.\n+ What kind of problems?", "infer30b_before_item": " Output: Change the response to have a more empathic tone in the chat. + How are you doing?\n- Fine, I had a bunch of problems today.\n+ What kind of problems? \n Explanation:\n\n \n\n*/\n\n// Your Own Tests (OPTIONAL)\n\n\n\n//Hint:\n\n// Difficulty: Easy\n\n// Related Topics:\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n//\n\n", "infer30b_after_item": " \n Output: I'm sorry to hear that. What kind of problems did you have today? \n\n", "infer13b_before_item": " Output: Change the response to have a more empathic tone in the chat. + I'm sorry to hear that.\n- I'm fine.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+ What kind of problems?\n- I had a bunch of problems today.\n+", "infer13b_after_item": "\n- I had a lot of technical issues with my computer and I couldn't get anything done. \n Output: + I'm sorry to hear that. What kind of technical issues did you have? \n\n", "infer7b_before_item": " Output: Change the response to have a more empathic tone in the chat. + How are you doing?\n- Fine, I had a bunch of problems today.\n+ What kind of problems? \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: + I'm sorry to hear that. What kind of problems did you have? \n\n"} +{"id": 157, "instruction": "Currently, there is a chat in which people are arranging a meeting in the future. Retrieve the meeting information from the conversation.", "input": "Person 1: Hey, when are you free? I'd like to meet up.\nPerson 2: I'm free tomorrow afternoon.\nPerson 1: Great, what time?\nPerson 2: How about 3 pm?\nPerson 1: I have another meeting at that time. what about 5 pm?\nPerson 2: That works. Where should we meet?\nPerson 1: How about the coffee shop on Main Street?\nPerson 2: Sounds good. I've heard they have great coffee there. See you tomorrow!", "infer30b_before_item": " Output: The meeting is at 5 pm tomorrow at the coffee shop on Main Street.\n\n\n*/\n\n\n#include <iostream>\n#include <vector>\n#include <string>\n#include <cstring>\n#include <cstdio>\n#include <cmath>\n#include <algorithm>\nusing namespace std;\n\nint main() {\n /* Enter your code here. Read input from STDIN. Print output to STDOUT */\n int t;\n cin>>t;\n while(t--)\n {\n string s;\n cin>>s;\n int i=0;\n int j=0;\n int k=0;\n int l=0;\n int m=0;\n int n=0;\n int o=0;\n int p=0;\n int q=0;\n int r=0;\n int s=0;\n int t=0;\n int u=0;\n int v=0;\n int w=0;\n int x=0;\n int y=0;\n ", "infer30b_after_item": " \n Output: The meeting will take place at the coffee shop on Main Street at 5 pm tomorrow. \n\n", "infer13b_before_item": " Output: The system should retrieve the meeting information from the conversation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Tomorrow, 5 pm at the coffee shop on Main Street. \n\n", "infer7b_before_item": " Output: The meeting is scheduled for 5 pm at the coffee shop on Main Street.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Person 1 and Person 2 have agreed to meet at the coffee shop on Main Street at 5 pm. \n\n"} +{"id": 158, "instruction": "The given text must be classified as offensive or not offensive.", "input": "You acted really crazy last night.", "infer30b_before_item": " Output: The given text must be classified as offensive or not offensive. You acted really crazy last night. \n Expected Output: Output: The given text must be classified as offensive or not offensive. You acted really crazy last night. \n Author: Cody Swanson\n */\n\nimport java.util.Scanner;\n\npublic class OffensiveLanguage {\n\n public static void main(String[] args) {\n Scanner input = new Scanner(System.in);\n String text = input.nextLine();\n String[] words = text.split(\" \");\n int count = 0;\n for (int i = 0; i < words.length; i++) {\n if (words[i].equals(\"fuck\") || words[i].equals(\"shit\") || words[i].equals(\"ass\") || words[i].equals(\"bitch\") || words[i].equals(\"damn\") || words[i].equals(\"hell\") || words[i].equals(\"bastard\") || words[i].equals(\"bastard\") || words[i].equals(\"bitch\") || words[i].equals", "infer30b_after_item": " \n Output: Offensive. \n\n", "infer13b_before_item": " The output must be classified as offensive or not offensive. You acted really crazy last night. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Not offensive. \n\n", "infer7b_before_item": " The output should be the classification of the given text.\n*/\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n\nint main(int argc, char *argv[])\n{\n char *text = argv[1];\n int offensive = 0;\n int not_offensive = 0;\n\n if (strstr(text, \"fuck\")!= NULL)\n {\n offensive = 1;\n }\n else if (strstr(text, \"shit\")!= NULL)\n {\n offensive = 1;\n }\n else if (strstr(text, \"ass\")!= NULL)\n {\n offensive = 1;\n }\n else if (strstr(text, \"bitch\")!= NULL)\n {\n offensive = 1;\n }\n else if (strstr(text, \"cunt\")!= NULL)\n {\n offensive = 1;\n }\n else if (strstr(text, \"pussy\")!= NULL", "infer7b_after_item": " \n Output: Offensive. \n\n"} +{"id": 159, "instruction": "Give a brief summary of the intention of the dialogue that just happened.", "input": "Customer: Hi there, I'm looking for a new phone.\nAI: Hi! What type of phone are you looking for?\nCustomer: I'm not sure. Maybe something with a good camera?\nAI: We have a few phones with great cameras. Would you like to see some options?\nCustomer: Yeah, that would be great.", "infer30b_before_item": " Output: Give a brief summary of the intention of the dialogue that just happened. Customer: Hi there, I'm looking for a new phone.\nAI: Hi! What type of phone are you looking for?\nCustomer: I'm not sure. Maybe something with a good camera?\nAI: We have a few phones with great cameras. Would you like to see some options?\nCustomer: Yeah, that would be great. \n# **1.**\n**1.** **What is the purpose of this dialogue?**\n**2.** **What is the customer's intention?**\n**3.** **What is the AI's intention?**\n**4.** **What is the AI's response?**\n**5.** **What is the customer's response?**\n**6.** **What is the AI's response?**\n**7.** **What is the customer's response?**\n**8.** **What is the AI's response?**\n**9.** **What is the customer's response?**\n**10.** **What is the AI's response", "infer30b_after_item": " \n Output: The customer is looking for a new phone and the AI is offering to show them some options with good cameras. \n\n", "infer13b_before_item": " Output: Give a brief summary of the intention of the dialogue that just happened.\nAI: Here are some options.\nCustomer: I like the first one.\nAI: Great! Would you like to buy it?\nCustomer: Yes, please.\nAI: I'll send you a link to buy it.\nCustomer: Thanks!\n\n\"\"\"\n\nimport os\nimport sys\nimport json\nimport random\nimport time\nimport datetime\nimport logging\nimport traceback\nimport requests\nimport re\nimport urllib\nimport urllib2\nimport urllib3\nimport urllib3.util\nimport urllib3.exceptions\nimport urllib3.contrib.pyopenssl\nimport urllib3.contrib.pycurl\nimport urllib3.contrib.requests\nimport urllib3.contrib.pycurl.cacert\nimport urllib3.contrib.pyopenssl.cert\nimport urllib3.contrib.pyopenssl.privatekey\nimport urllib3.contrib.pyopenssl.x509\nimport urllib3.contrib.pyopenssl.x509crl\nimport urllib3.contrib.pyopenssl.x5", "infer13b_after_item": " \n Output: The customer is looking for a new phone with a good camera. The AI suggested some options and the customer agreed to see them. \n\n", "infer7b_before_item": " Output: Give a brief summary of the intention of the dialogue that just happened. Customer: Hi there, I'm looking for a new phone.\nAI: Hi! What type of phone are you looking for?\nCustomer: I'm not sure. Maybe something with a good camera?\nAI: We have a few phones with great cameras. Would you like to see some options?\nCustomer: Yeah, that would be great.\nAI: Here are some options.\nCustomer: I like this one.\nAI: Great! Would you like to buy it?\nCustomer: Yes, I would.\nAI: Great! Would you like to buy it?\nCustomer: Yes, I would.\nAI: Great! Would you like to buy it?\nCustomer: Yes, I would.\nAI: Great! Would you like to buy it?\nCustomer: Yes, I would.\nAI: Great! Would you like to buy it?\nCustomer: Yes, I would.\nAI: Great! Would you like to buy it?\nCustomer: Yes, I would.\nAI: Great! Would you like to buy it?\nCustomer: Yes, I would.\nAI: Great! Would you", "infer7b_after_item": "\nAI: Sure, let me show you some of our options. \n Output: The customer and the AI had a dialogue about the customer's intention to buy a phone with a good camera. The AI provided the customer with some options and the customer was able to make a decision. \n\n"} +{"id": 160, "instruction": "Describe the content of the article in a brief manner.", "input": "A study published earlier this year by Zee and her team examined the role of light in sleep for healthy adults in their 20s. Sleeping for only one night with a dim light, such as a TV set with the sound off, raised the blood sugar and heart rate of the young people during the sleep lab experiment. An elevated heart rate at night has been shown in prior studies to be a risk factor for future heart disease and early death, while higher blood sugar levels are a sign of insulin resistance, which can ultimately lead to type 2 diabetes.", "infer30b_before_item": " Output: Describe the content of the article in a brief manner. A study published earlier this year by Zee and her team examined the role of light in sleep for healthy adults in their 20s. Sleeping for only one night with a dim light, such as a TV set with the sound off, raised the blood sugar and heart rate of the young people during the sleep lab experiment. An elevated heart rate at night has been shown in prior studies to be a risk factor for future heart disease and early death, while higher blood sugar levels are a sign of insulin resistance, which can ultimately lead to type 2 diabetes. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: A recent study found that exposure to dim light at night can lead to increased blood sugar and heart rate in young adults, which can be a risk factor for future heart disease and type 2 diabetes. \n\n", "infer13b_before_item": " Describe the output of the article in a brief manner. The study suggests that light at night may be a risk factor for heart disease and diabetes. \n Input: Describe the content of the article in a brief manner. A study published earlier this year by Zee and her team examined the role of light in sleep for healthy adults in their 20s. Sleeping for only one night with a dim light, such as a TV set with the sound off, raised the blood sugar and heart rate of the young people during the sleep lab experiment. An elevated heart rate at night has been shown in prior studies to be a risk factor for future heart disease and early death, while higher blood sugar levels are a sign of insulin resistance, which can ultimately lead to type 2 diabetes. \n Output: Describe the output of the article in a brief manner. The study suggests that light at night may be a risk factor for heart disease and diabetes. \n Input: Describe the content of the article in a brief manner. A study published earlier this year by Zee and her team examined the role of light in sleep for healthy adults in their 20s. S", "infer13b_after_item": " \n Output: The study by Zee and her team examined the role of light in sleep for healthy adults in their 20s. Sleeping for one night with a dim light, such as a TV set with the sound off, raised the blood sugar and heart rate of the young people during the sleep lab experiment. An elevated heart rate at night has been shown in prior studies to be a risk factor for future heart disease and early death, while higher blood sugar levels are a sign of insulin resistance, which can ultimately lead to type 2 diabetes. \n\n", "infer7b_before_item": " Output: Describe the results of the study. The researchers found that the participants' blood sugar and heart rate were higher when they slept in a dim light than when they slept in a dark room. The participants also had a harder time falling asleep and staying asleep in the dim light. \n Conclusion: Conclusion: Summarize the main points of the article. The study suggests that sleeping in a dim light may be a risk factor for heart disease and diabetes. \n Keywords: Keywords: List the keywords of the article. Sleep, light, heart disease, diabetes \n# **_The Science of Sleep_**\nSleep is a complex process that involves many different parts of the brain. The brain is divided into two hemispheres, the left and the right. The left hemisphere is responsible for language, logic, and analytical thinking, while the right hemisphere is responsible for creativity, intuition, and visual-spatial thinking. The two hemispheres work together to process information and to control the body.\nThe brain is also divided into two halves, the right and the left. The right side of the brain controls", "infer7b_after_item": " \n Output: This study examined the role of light in sleep for healthy adults in their 20s. Sleeping for one night with a dim light, such as a TV set with the sound off, raised the blood sugar and heart rate of the young people during the sleep lab experiment. An elevated heart rate at night has been shown in prior studies to be a risk factor for future heart disease and early death, while higher blood sugar levels are a sign of insulin resistance, which can ultimately lead to type 2 diabetes. \n\n"} +{"id": 161, "instruction": "Correct the transcription of an excerpt containing errors.", "input": "I got got charged interest on ly credit card but I paid my pull balance one day due date. I not missed a pavement year yet. Man you reverse the interest charge?", "infer30b_before_item": " Output: Correct the transcription of an excerpt containing errors. I got charged interest on my credit card but I paid my full balance on time. I haven't missed a payment year yet. Man, you reversed the interest charge?\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: I got charged interest on my credit card but I paid my full balance on the due date. I haven't missed a payment year yet. Can you reverse the interest charge? \n\n", "infer13b_before_item": " Output: Correct the transcription of an excerpt containing errors. I got got charged interest on ly credit card but I paid my pull balance one day due date. I not missed a pavement year yet. Man you reverse the interest charge?\n\n#include <stdio.h>\n#include <string.h>\n#include <stdlib.h>\n#include <ctype.h>\n#include <math.h>\n#include <ctype.h>\n#include <time.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <sys/time.h>\n#include <sys/types.h>\n#include <sys/stat.h>\n#include <fcntl.h>\n#include <errno.h>\n#include <limits.h>\n#include <assert.h>\n#include <stdarg.h>\n#include <stdint.h>\n#include <stdbool.h>\n#include <unistd.h>\n#include <sys/types.h>\n#include <sys/stat.h>\n#include <sys", "infer13b_after_item": " \n Output: I got charged interest on my credit card but I paid my full balance one day before the due date. I haven't missed a payment yet. Can you reverse the interest charge? \n\n", "infer7b_before_item": " Output: Correct the transcription of an excerpt containing errors. I got charged interest on my credit card but I paid my balance one day before the due date. I didn't miss a payment yet. Man, you reversed the interest charge? \n\"\"\"\n\n\ndef main():\n \"\"\"\n Main function.\n \"\"\"\n # Read the input file.\n with open('input.txt') as f:\n lines = f.readlines()\n\n # Read the output file.\n with open('output.txt', 'w') as f:\n for line in lines:\n line = line.strip()\n if line:\n f.write(line)\n\n\nif __name__ == '__main__':\n main()\n", "infer7b_after_item": " \n Output: I got charged interest on my credit card but I paid my full balance one day before the due date. I didn't miss a payment yet. Can you reverse the interest charge? \n\n"} +{"id": 162, "instruction": "Take a look at the contract and extract the parties of the agreement from it.", "input": "As of the 13th day of December 2021, this Music Recording Agreement (\"Agreement\") is made between Good Kid, a Toronto-based musical group (\"Artist\"), and Universal Music Group, a record label with license number 545345 (\"Record Label\"). The Artist and the Recording Label may be referred to in this Agreement individually as \"Parties\" and collectively as \"Parties.\" Work under this Agreement will begin on March 15, 2022, and will last for the duration of the Agreement.", "infer30b_before_item": " Output: The parties of the agreement are Good Kid and Universal Music Group.\nInput: Input: Take a look at the contract and extract the date of the agreement from it. As of the 13th day of December 2021, this Music Recording Agreement (\"Agreement\") is made between Good Kid, a Toronto-based musical group (\"Artist\"), and Universal Music Group, a record label with license number 545345 (\"Record Label\"). The Artist and the Recording Label may be referred to in this Agreement individually as \"Parties\" and collectively as \"Parties.\" Work under this Agreement will begin on March 15, 2022, and will last for the duration of the Agreement. \n Output: Output: The date of the agreement is December 13, 2021.\nInput: Input: Take a look at the contract and extract the date of the agreement from it. As of the 13th day of December 2021, this Music Recording Agreement (\"Agreement\") is made between Good Kid, a Toronto-based musical group (\"Artist\"), and Universal Music", "infer30b_after_item": " \n Output: Good Kid, Universal Music Group. \n\n", "infer13b_before_item": " Output: The output of this function is the parties of the contract.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Parties: Good Kid, Universal Music Group \n\n", "infer7b_before_item": " Output: The Artist will be paid $100,000 for the duration of the Agreement. The Artist will be paid $100,000 for the duration of the Agreement.\n\n### 2.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1", "infer7b_after_item": " \n Output: Good Kid and Universal Music Group. \n\n"} +{"id": 163, "instruction": "On the basis of the subject of the email, determine whether the email should be treated as spam or not.", "input": "Dermatologists don't like her!", "infer30b_before_item": " Output: If the email is spam, then print \"Spam\" else print \"Not Spam\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Not spam. \n\n", "infer13b_before_item": " Output: If the email is spam, then the email is deleted. If the email is not spam, then the email is sent to the inbox.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Spam \n\n", "infer7b_before_item": " The email is either treated as spam or not.\n*/\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n#include <ctype.h>\n#include <unistd.h>\n#include <sys/types.h>\n#include <sys/socket.h>\n#include <netinet/in.h>\n#include <arpa/inet.h>\n#include <errno.h>\n#include <netdb.h>\n#include <sys/ioctl.h>\n#include <sys/time.h>\n#include <sys/types.h>\n#include <sys/stat.h>\n#include <fcntl.h>\n#include <sys/select.h>\n#include <sys/time.h>\n#include <sys/types.h>\n#include <sys/socket.h>\n#include <netinet/in.h>\n#include <arpa/inet.h>\n#include <errno.h>\n#include <netdb.h>\n#include <sys/io", "infer7b_after_item": " \n Output: Spam. \n\n"} +{"id": 164, "instruction": "Classify the tweet into one of the following three categories: event, demo, or news. Event is a happening, often social or public, that people attend or are aware of. Demo is a public showing or presentation of something, typically to promote or advertise it. And, news is information about recent events or happenings, especially as reported by news media.", "input": "Using this tutorial, you can analyze and classify a user\u2019s review as either positive or negative using sentiment analysis features in an existing Next.js app. We show you how to leverage language AI into your applications. Check it out \u2193", "infer30b_before_item": " Output: Classify the tweet into one of the following three categories: event, demo, or news. Event is a happening, often social or public, that people attend or are aware of. Demo is a public showing or presentation of something, typically to promote or advertise it. And, news is information about recent events or happenings, especially as reported by news media. Using this tutorial, you can analyze and classify a user\u2019s review as either positive or negative using sentiment analysis features in an existing Next.js app. We show you how to leverage language AI into your applications. Check it out \u2193 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " https://www.nextjs.org/blog/next-js-tutorial-sentiment-analysis \n Output: News \n\n", "infer13b_before_item": " Output: Classify the tweet into one of the following three categories: event, demo, or news. Event is a happening, often social or public, that people attend or are aware of. Demo is a public showing or presentation of something, typically to promote or advertise it. And, news is information about recent events or happenings, especially as reported by news media. Using this tutorial, you can analyze and classify a user\u2019s review as either positive or negative using sentiment analysis features in an existing Next.js app. We show you how to leverage language AI into your applications. Check it out \u2193 \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " Output: Event \n Output: Event \n\n", "infer7b_before_item": " Output: The tweet is classified as an event.\n\n <h1>Sentiment Analysis</h1>\n\n <p>\n Sentiment analysis is a subfield of natural language processing that uses machine learning to identify and extract subjective information in text.\n </p>\n\n <p>\n Sentiment analysis is a subfield of natural language processing that uses machine learning to identify and extract subjective information in text.\n </p>\n\n <p>\n Sentiment analysis is a subfield of natural language processing that uses machine learning to identify and extract subjective information in text.\n </p>\n\n <p>\n Sentiment analysis is a subfield of natural language processing that uses machine learning to identify and extract subjective information in text.\n </p>\n\n <p>\n Sentiment analysis is a subfield of natural language processing that uses machine learning to identify and extract subjective information in text.\n </p>\n\n <p>\n Sentiment analysis is a subfield of natural language processing that uses machine learning to identify and extract subjective information in text.\n </p>\n", "infer7b_after_item": " https://nextjs.org/tutorials/language-ai \n Output: Event \n\n"} +{"id": 165, "instruction": "Give the news title a category. Pick a category from the list of News & Buzz, Travel, Style, Arts & Culture, Politics, Tech, and Science & Health.", "input": "The #Banksy Exhibit in Cambridge, MA is absolutely terrific.", "infer30b_before_item": " Output: Give the news title a category. Pick a category from the list of News & Buzz, Travel, Style, Arts & Culture, Politics, Tech, and Science & Health. The #Banksy Exhibit in Cambridge, MA is absolutely terrific. \n Expected: Output: Give the news title a category. Pick a category from the list of News & Buzz, Travel, Style, Arts & Culture, Politics, Tech, and Science & Health. The #Banksy Exhibit in Cambridge, MA is absolutely terrific. \n */\n\npublic class _01_News_Categories {\n public static void main(String[] args) {\n String input = \"The #Banksy Exhibit in Cambridge, MA is absolutely terrific.\";\n String output = input.replaceAll(\"#\", \"\");\n System.out.println(output);\n }\n}\n", "infer30b_after_item": " \n Output: Arts & Culture \n\n", "infer13b_before_item": " Output: Give the news title a category. Pick a category from the list of News & Buzz, Travel, Style, Arts & Culture, Politics, Tech, and Science & Health. The #Banksy Exhibit in Cambridge, MA is absolutely terrific. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Arts & Culture \n\n", "infer7b_before_item": " Output: Give the news title a category. Pick a category from the list of News & Buzz, Travel, Style, Arts & Culture, Politics, Tech, and Science & Health. The #Banksy Exhibit in Cambridge, MA is absolutely terrific. \n\"\"\"\n\nimport re\nimport sys\n\n\ndef main():\n \"\"\"\n Main function.\n \"\"\"\n # Read the input file.\n with open(sys.argv[1], 'r') as f:\n lines = f.readlines()\n\n # Parse the input file.\n news_titles = []\n for line in lines:\n # Remove the # symbol.\n line = line.strip()\n # Remove the leading space.\n line = line.lstrip()\n # Remove the trailing space.\n line = line.rstrip()\n # Remove the leading and trailing spaces.\n line = line.strip()\n # Remove the leading and trailing spaces.\n line = line.lstrip()\n # Remove the leading and trailing spaces.\n line = line.rstrip()\n # Remove the", "infer7b_after_item": " \n Output: Arts & Culture. \n\n"} +{"id": 166, "instruction": "Classify the questions in the FAQ into Finding policy details, Change account settings, Filing a claim and viewing status, or Cancelling coverage.", "input": "Could you deposit money into my account rather than mailing me a physical cheque?", "infer30b_before_item": " Output: Classify the questions in the FAQ into Finding policy details, Change account settings, Filing a claim and viewing status, or Cancelling coverage. Could you deposit money into my account rather than mailing me a physical cheque? \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ", "infer30b_after_item": " \n Output: Cancelling coverage. \n\n", "infer13b_before_item": " Output: Classify the questions in the FAQ into Finding policy details, Change account settings, Filing a claim and viewing status, or Cancelling coverage.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Filing a claim. \n\n", "infer7b_before_item": " \n 1. Finding policy details\n 2. Change account settings\n 3. Filing a claim and viewing status\n 4. Cancelling coverage\n 5. Other\n 6. None\n 7. None\n 8. None\n 9. None\n 10. None\n 11. None\n 12. None\n 13. None\n 14. None\n 15. None\n 16. None\n 17. None\n 18. None\n 19. None\n 20. None\n 21. None\n 22. None\n 23. None\n 24. None\n 25. None\n 26. None\n 27. None\n 28. None\n 29. None\n 30. None\n 31. None\n 32. None\n 33. None\n 34. None\n 35. None\n 36. None\n 37. None\n 38. None\n 39. None\n 40. None\n 41. None\n 4", "infer7b_after_item": " \n Output: Filing a claim and viewing status. \n\n"} +{"id": 167, "instruction": "Choose an appealing title for your post.", "input": "The typical avocado is over 300 calories from the oil in it. That\u2019s the amount of calories in a large candy bar. If you get enough exercise to eat a large candy bar every day without gaining weight, it wouldn\u2019t be a problem to eat an avocado every day. Other wise you should probably eat them sparingly.", "infer30b_before_item": " Output: Choose an appealing title for your post. The typical avocado is over 300 calories from the oil in it. That\u2019s the amount of calories in a large candy bar. If you get enough exercise to eat a large candy bar every day without gaining weight, it wouldn\u2019t be a problem to eat an avocado every day. Other wise you should probably eat them sparingly. \n\nInput: Input: Choose an appealing title for your post. The typical avocado is over 300 calories from the oil in it. That\u2019s the amount of calories in a large candy bar. If you get enough exercise to eat a large candy bar every day without gaining weight, it wouldn\u2019t be a problem to eat an avocado every day. Other wise you should probably eat them sparingly. \n Output: Output: Choose an appealing title for your post. The typical avocado is over 300 calories from the oil in it. That\u2019s the amount of calories in a large candy bar. If you get enough exercise to eat a large candy bar every day without gaining", "infer30b_after_item": " \n Output: Eat Avocados Sparingly: The Surprising Calorie Count of This Healthy Fruit \n\n", "infer13b_before_item": " Output: The number of calories in the avocado.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: \"Avocados: A Healthy Snack or a Sweet Treat?\" \n\n", "infer7b_before_item": " The output is the text of your post. \n*/\n\n// The following code is a sample of how to use the API.\n// You can use it as a starting point for your own code.\n\n// The following code is a sample of how to use the API.\n// You can use it as a starting point for your own code.\n\n// The following code is a sample of how to use the API.\n// You can use it as a starting point for your own code.\n\n// The following code is a sample of how to use the API.\n// You can use it as a starting point for your own code.\n\n// The following code is a sample of how to use the API.\n// You can use it as a starting point for your own code.\n\n// The following code is a sample of how to use the API.\n// You can use it as a starting point for your own code.\n\n// The following code is a sample of how to use the API.\n// You can use it as a starting point for your own code.\n\n// The following code is a sample of how to use the API.\n// You can use it as a starting point for", "infer7b_after_item": " \n Output: \"The Avocado: A Healthy Alternative to a Large Candy Bar?\" \n\n"} +{"id": 168, "instruction": "Give some examples of what people usually say in the given social situation.", "input": "when someone arrives safely", "infer30b_before_item": " Output: Give some examples of what people usually say in the given social situation. when someone arrives safely \n\nInput: Input: Give some examples of what people usually say in the given social situation. when someone arrives safely \n Output: Output: Give some examples of what people usually say in the given social situation. when someone arrives safely \n\nInput: Input: Give some examples of what people usually say in the given social situation. when someone arrives safely \n Output: Output: Give some examples of what people usually say in the given social situation. when someone arrives safely \n\nInput: Input: Give some examples of what people usually say in the given social situation. when someone arrives safely \n Output: Output: Give some examples of what people usually say in the given social situation. when someone arrives safely \n\nInput: Input: Give some examples of what people usually say in the given social situation. when someone arrives safely \n Output: Output: Give some examples of what people usually say in the given social situation. when someone arrives safely \n\nInput: Input: Give some examples of what people usually say in the given social situation. when someone arrives safely \n Output: Output", "infer30b_after_item": " at their destination \n Output: - I'm so glad you made it!\n- Welcome!\n- It's great to see you!\n- You made it!\n- I'm so glad you're here!\n- Safe travels!\n- It's good to have you here!\n- You must be exhausted!\n- How was the journey?\n- I'm so glad you're safe!\n- Welcome home!\n- It's great to see you safe and sound!\n- I'm so glad you're here!\n- It's great to have you here!\n- You made it!\n- I'm so glad you're here!\n- Safe travels!\n- It's great to have you here!\n- I'm so glad you're safe!\n- Welcome!\n- I'm so glad you made it!\n- It's great to have you here!\n- I'm so glad you're safe!\n- You must be exhausted!\n- How was the journey?\n- I'm so glad you're here!\n- Welcome home!\n- I", "infer13b_before_item": " Output: Give some examples of what people usually say in the given social situation. when someone arrives safely \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " at their destination \n Output: \"Thank goodness you made it safely!\" \n\"Phew, I'm glad you're here!\" \n\"It's a relief to know you're here.\" \n\"I'm so glad you arrived safely.\" \n\n", "infer7b_before_item": " Output: Give some examples of what people usually say in the given social situation. when someone arrives safely \n\\end{code}\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment: I'm not sure what you're asking.\n\nComment", "infer7b_after_item": " home \n Output: - \"Welcome home!\"\n- \"I'm so glad you're safe!\"\n- \"It's good to have you back!\"\n- \"I was worried about you!\"\n- \"I'm so glad you're okay!\"\n- \"I'm glad you're home!\"\n- \"Welcome back!\"\n- \"I'm so glad you're home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Welcome home!\"\n- \"Welcome back!\"\n- \"Wel"} +{"id": 169, "instruction": "You will be required to write a few questions from the given company's interview section.", "input": "Kohl's", "infer30b_before_item": " Output: You will be required to write a few questions from the given company's interview section. Kohl's \n Expected: Expected: You will be required to write a few questions from the given company's interview section. Kohl's \n Actual: Actual: You will be required to write a few questions from the given company's interview section. Kohl's \n */\n\n#include <iostream>\n#include <string>\n#include <vector>\n\nusing namespace std;\n\nint main() {\n string input;\n vector<string> output;\n vector<string> expected;\n vector<string> actual;\n\n getline(cin, input);\n output.push_back(input);\n\n getline(cin, input);\n output.push_back(input);\n\n getline(cin, input);\n output.push_back(input);\n\n getline(cin, input);\n output.push_back(input);\n\n getline(cin, input);\n output.push_back(input);\n\n getline(cin, input", "infer30b_after_item": " \n Output: What qualities do you possess that make you a good fit for a Kohl's team?\n\nWhat experience do you have in customer service?\n\nHow do you handle difficult customer situations?\n\nWhat do you know about Kohl's products and services?\n\nWhat strategies do you use to increase customer satisfaction?\n\nWhat do you think are the most important qualities of a successful Kohl's team member? \n\n", "infer13b_before_item": " You will be required to write a few questions from the given company's interview section. Kohl's \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: - What do you think makes Kohl's stand out from other retailers?\n- How would you describe your customer service skills?\n- What do you think is the most important aspect of customer service?\n- What do you think is the most important aspect of teamwork?\n- How do you handle difficult customer situations?\n- What do you think is the most important aspect of communication?\n- What do you think is the most important aspect of problem solving?\n- How do you handle stressful situations?\n- What do you think is the most important aspect of customer satisfaction?\n- How do you handle customer complaints?\n- How do you handle difficult customer requests?\n- What do you think is the most important aspect of customer service?\n- What do you think is the most important aspect of customer relations?\n- How do you handle customer feedback?\n- How do you handle customer inquiries?\n- How do you handle customer complaints?\n- How do you handle customer requests?\n- How do you handle customer disputes?\n- How do you handle customer returns?\n- How do you handle customer refunds?\n- How do you handle customer exchanges?", "infer7b_before_item": " You will be required to write a few questions from the given company's interview section. Kohl's\n\nimport java.util.Scanner;\nimport java.util.ArrayList;\nimport java.util.List;\nimport java.util.Arrays;\nimport java.util.Collections;\nimport java.util.Comparator;\n\npublic class KohlS {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n List<String> questions = new ArrayList<String>();\n questions.add(\"What is your name?\");\n questions.add(\"What is your age?\");\n questions.add(\"What is your favorite color?\");\n questions.add(\"What is your favorite food?\");\n questions.add(\"What is your favorite movie?\");\n questions.add(\"What is your favorite book?\");\n questions.add(\"What is your favorite sport?\");\n questions.add(\"What is your favorite music?\");\n questions.add(\"What is your favorite season?\");\n questions.add(\"What is your favorite animal?\");\n questions.add(\"What is", "infer7b_after_item": " is an American department store chain. \n Output: 1. What experience do you have in retail?\n2. How do you handle customer complaints?\n3. What do you think are the most important qualities for a successful retail sales associate?\n4. How do you handle pressure in a fast-paced environment?\n5. How do you handle customer service issues?\n6. How do you handle customer complaints?\n7. How do you handle customer requests?\n8. How do you handle customer questions?\n9. How do you handle customer feedback?\n10. How do you handle customer concerns?\n11. How do you handle customer inquiries?\n12. How do you handle customer requests?\n13. How do you handle customer complaints?\n14. How do you handle customer service issues?\n15. How do you handle customer requests?\n16. How do you handle customer questions?\n17. How do you handle customer feedback?\n18. How do you handle customer concerns?\n19. How do you handle customer inquiries?\n20. How do you handle customer complaints?\n21."} +{"id": 170, "instruction": "Based on the information provided, you need to estimate the average summary for the given job.", "input": "Data entry clerk in United States", "infer30b_before_item": " Output: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States\n\nInput: Input: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States \n Output: Output: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States\n\nInput: Input: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States \n Output: Output: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States\n\nInput: Input: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States \n Output: Output: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States\n\nInput: Input: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States \n Output: Output: Based on the information provided,", "infer30b_after_item": ". \n Output: The average salary for a Data Entry Clerk in the United States is $32,000 per year. \n\n", "infer13b_before_item": " Output: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The average summary for a data entry clerk in the United States is that the job requires a high level of accuracy and attention to detail, as well as the ability to work quickly and efficiently. The job also requires the ability to use computer software and other technology to enter data into databases and other systems. \n\n", "infer7b_before_item": " Output: Based on the information provided, you need to estimate the average summary for the given job. Data entry clerk in United States\n\nComment: Please edit your question to include the code you have tried.\n\nAnswer: You can use the `data.table` package to do this.\n\n\\begin{code}\nlibrary(data.table)\n\n# Create a data.table\ndt <- data.table(\n job = c(\"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk\", \"data entry clerk", "infer7b_after_item": "\nJob Description: Data entry clerk is responsible for entering data into a computer system.\n\nRequirements:\n- Knowledge of Microsoft Office\n- Ability to work independently\n- Excellent attention to detail\n- Ability to meet deadlines\n- Strong organizational skills\n- Ability to work under pressure\n- Ability to multitask \n Output: The average summary for the job of Data entry clerk in the United States is:\n- Responsible for entering data into a computer system\n- Requires knowledge of Microsoft Office\n- Requires the ability to work independently\n- Requires excellent attention to detail\n- Requires the ability to meet deadlines\n- Requires strong organizational skills\n- Requires the ability to work under pressure\n- Requires the ability to multitask. \n\n"} +{"id": 171, "instruction": "Describe the responsibilities of the given job.", "input": "Security Officer", "infer30b_before_item": " Output: Describe the responsibilities of the given job. Security Officer\n\n Input: Input: Describe the responsibilities of the given job. Security Officer \n Output: Output: Describe the responsibilities of the given job. Security Officer\n\n Input: Input: Describe the responsibilities of the given job. Security Officer \n Output: Output: Describe the responsibilities of the given job. Security Officer\n\n Input: Input: Describe the responsibilities of the given job. Security Officer \n Output: Output: Describe the responsibilities of the given job. Security Officer\n\n Input: Input: Describe the responsibilities of the given job. Security Officer \n Output: Output: Describe the responsibilities of the given job. Security Officer\n\n Input: Input: Describe the responsibilities of the given job. Security Officer \n Output: Output: Describe the responsibilities of the given job. Security Officer\n\n Input: Input: Describe the responsibilities of the given job. Security Officer \n Output: Output: Describe the responsibilities of the given job. Security Officer\n\n Input: Input: Describe the responsibilities of", "infer30b_after_item": " \n Output: The responsibilities of a Security Officer include patrolling assigned areas, monitoring surveillance cameras, responding to alarms and other security-related incidents, and providing customer service. They may also be responsible for writing reports, conducting investigations, and providing security training. \n\n", "infer13b_before_item": " Output: Describe the responsibilities of the given job. Security Officer \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n", "infer13b_after_item": " \n Output: The responsibilities of a security officer include monitoring and patrolling the premises, responding to alarms and emergencies, enforcing security policies and procedures, and providing customer service. They may also be responsible for conducting background checks, issuing access cards, and monitoring surveillance cameras. \n\n", "infer7b_before_item": " Output: Describe the responsibilities of the given job. Security Officer \n*/\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: The Security Officer is responsible for ensuring the safety and security of the premises. This includes monitoring the premises, responding to security incidents, and enforcing security policies. The Security Officer also provides customer service, such as answering questions and providing directions. Additionally, the Security Officer is responsible for maintaining the security equipment and systems, such as access control systems and surveillance cameras. \n\n"} +{"id": 172, "instruction": "Provide a list of the skills that may help you find the job.", "input": "Crew Members", "infer30b_before_item": " Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list of the skills that may help you find the job. Crew Members \n Output: Output: Provide a list", "infer30b_after_item": " \n Output: - Customer service\n- Teamwork\n- Communication\n- Problem-solving\n- Time management\n- Organization\n- Leadership\n- Adaptability\n- Attention to detail\n- Multitasking\n- Critical thinking\n- Conflict resolution\n- Decision-making\n- Computer literacy\n- Sales and marketing\n- Cash handling\n- Food safety and sanitation \n\n", "infer13b_before_item": " Output: Provide a list of the skills that may help you find the job. Crew Members \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: - Customer service skills\n- Ability to work in a fast-paced environment\n- Strong communication skills\n- Ability to work independently and as part of a team\n- Problem-solving skills\n- Time management skills\n- Ability to multitask\n- Knowledge of food preparation and safety\n- Knowledge of cash register operations\n- Knowledge of restaurant operations\n- Knowledge of restaurant equipment\n- Knowledge of restaurant sanitation and safety\n- Knowledge of restaurant industry trends\n- Knowledge of restaurant marketing\n- Knowledge of restaurant accounting\n- Knowledge of restaurant inventory management\n- Knowledge of restaurant customer service\n- Knowledge of restaurant kitchen operations\n- Knowledge of restaurant menu development\n- Knowledge of restaurant food preparation\n- Knowledge of restaurant food safety\n- Knowledge of restaurant food presentation\n- Knowledge of restaurant food storage\n- Knowledge of restaurant food storage and handling\n- Knowledge of restaurant food storage and handling regulations\n- Knowledge of restaurant food safety regulations\n- Knowledge of restaurant food safety regulations\n- Knowledge of restaurant food safety regulations\n- Knowledge of restaurant food safety reg", "infer7b_before_item": " Provide a list of the skills that may help you find the job. Crew Members \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Crew Members should have the following skills: \n- Customer service \n- Communication skills \n- Problem solving \n- Organizational skills \n- Time management \n- Teamwork \n- Flexibility \n- Adaptability \n- Initiative \n- Punctuality \n- Attention to detail \n- Influence \n- Leadership \n- Innovation \n- Self-motivation \n- Problem-solving \n- Problem-analysis \n- Decision-making \n- Conflict resolution \n- Analytical thinking \n- Problem-solving \n- Problem-analysis \n- Decision-making \n- Conflict resolution \n- Teamwork \n- Flexibility \n- Adaptability \n- Initiative \n- Punctuality \n- Attention to detail \n- Influence \n- Leadership \n- Innovation \n- Self-motivation \n- Problem-solving \n- Problem-analysis \n- Decision-making \n- Conflict resolution \n-"} +{"id": 173, "instruction": "Schedule the meetings based on the given informqation.", "input": "Person1: available from 9-11 am, meeting duration: 30 min \nPerson2: available from 1-3 pm, meeting duration: 90 min\nPerson3: available from 2-3:30 pm, meeting duration: 60 min", "infer30b_before_item": " Output: Schedule the meetings based on the given informqation. Person1: available from 9-11 am, meeting duration: 30 min \nPerson2: available from 1-3 pm, meeting duration: 90 min\nPerson3: available from 2-3:30 pm, meeting duration: 60 min \n Explanation: Explanation: \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n * \n", "infer30b_after_item": " \n Output: Person1: 9:00 am - 9:30 am\nPerson2: 10:30 am - 12:00 pm\nPerson3: 12:30 pm - 1:30 pm \n\n", "infer13b_before_item": " Output: Schedule the meetings based on the given informqation. Person1: available from 9-11 am, meeting duration: 30 min \nPerson2: available from 1-3 pm, meeting duration: 90 min\nPerson3: available from 2-3:30 pm, meeting duration: 60 min \n\nclass Solution {\npublic:\n vector<int> findMeetingDuration(vector<int> &meetingDurations, vector<int> &availableTime) {\n vector<int> res;\n int n = meetingDurations.size();\n int m = availableTime.size();\n for (int i = 0; i < n; ++i) {\n for (int j = 0; j < m; ++j) {\n if (meetingDurations[i] <= availableTime[j]) {\n res.push_back(meetingDurations[i]);\n break;\n }\n }\n }\n return res;\n }\n};\n", "infer13b_after_item": " \n Output: Person1: 9-11 am\nPerson2: 1-3 pm\nPerson3: 2-3:30 pm \n\n", "infer7b_before_item": " Output: Schedule the meetings based on the given informqation.\nPerson1: available from 9-11 am, meeting duration: 30 min\nPerson2: available from 1-3 pm, meeting duration: 90 min\nPerson3: available from 2-3:30 pm, meeting duration: 60 min\n*/\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Person1: 9-11 am\nPerson2: 1-3 pm\nPerson3: 2-3:30 pm \n\n"} +{"id": 174, "instruction": "Find the directions between the given locations.", "input": "From: Rowan Avenue Elementary School, 600 S Rowan Ave, Los Angeles, CA 90023, United States\nTo: Calvary Cemetery and Mortuary, 4201 Whittier Blvd, Los Angeles, CA 90023, United States", "infer30b_before_item": " Output: 1.1 km\n\nInput: Input: Find the directions between the given locations. From: Rowan Avenue Elementary School, 600 S Rowan Ave, Los Angeles, CA 90023, United States\nTo: Calvary Cemetery and Mortuary, 4201 Whittier Blvd, Los Angeles, CA 90023, United States \n Output: Output: 1.1 km\n\nInput: Input: Find the directions between the given locations. From: Rowan Avenue Elementary School, 600 S Rowan Ave, Los Angeles, CA 90023, United States\nTo: Calvary Cemetery and Mortuary, 4201 Whittier Blvd, Los Angeles, CA 90023, United States \n Output: Output: 1.1 km\n\nInput: Input: Find the directions between the given locations. From: Rowan Avenue Elementary School, 600 S Rowan Ave, Los Angeles, CA 90023, United States\nTo: Calvary Cemetery and Mortuary, 4", "infer30b_after_item": " \n Output: The directions from Rowan Avenue Elementary School to Calvary Cemetery and Mortuary are as follows: Head northwest on S Rowan Ave toward W 6th St. Turn right onto W 6th St. Turn left onto S Broadway. Turn right onto W 1st St. Turn left onto S Main St. Turn right onto W 1st St. Turn left onto S Broadway. Turn right onto W 1st St. Turn left onto S Main St. Turn right onto W 1st St. Turn left onto Whittier Blvd and the destination will be on the right. \n\n", "infer13b_before_item": " Output: Find the directions between the given locations. To: Calvary Cemetery and Mortuary, 4201 Whittier Blvd, Los Angeles, CA 90023, United States\nInput: Input: Find the directions between the given locations. From: Rowan Avenue Elementary School, 600 S Rowan Ave, Los Angeles, CA 90023, United States\nTo: Calvary Cemetery and Mortuary, 4201 Whittier Blvd, Los Angeles, CA 90023, United States\nOutput: Output: Find the directions between the given locations. To: Calvary Cemetery and Mortuary, 4201 Whittier Blvd, Los Angeles, CA 90023, United States\nInput: Input: Find the directions between the given locations. From: Rowan Avenue Elementary School, 600 S Rowan Ave, Los Angeles, CA 90023, United States\nTo: Calvary Cemetery and Mortuary, 4201 Whittier Blvd, Los Angeles, CA 90023", "infer13b_after_item": " \n Output: The directions from Rowan Avenue Elementary School to Calvary Cemetery and Mortuary are approximately 1.3 miles and take about 18 minutes by car. \n\n", "infer7b_before_item": " The directions between the two locations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: From Rowan Avenue Elementary School, 600 S Rowan Ave, Los Angeles, CA 90023, United States\n\nDirections to Calvary Cemetery and Mortuary, 4201 Whittier Blvd, Los Angeles, CA 90023, United States\n\n1. Head northwest on S Rowan Ave toward E 4th St \n2. Turn left onto E 4th St \n3. Turn right onto S San Pedro St \n4. Turn left onto E 5th St \n5. Turn right onto S Whittier Blvd \n6. Turn left onto E 4th St \n7. Turn right onto S San Pedro St \n8. Turn left onto E 5th St \n9. Turn right onto S Whittier Blvd \n10. Turn left onto E 4th St \n11. Turn right onto S San Pedro St \n12. Turn left onto E 5th St \n13. Turn right onto S Whittier Blvd \n14. Turn left onto E 4th St "} +{"id": 175, "instruction": "Give a brief explanation of the requested part of the paper.", "input": "Paper: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.\nExplain: The dominant sequence transduction models", "infer30b_before_item": " Output: Give a brief explanation of the requested part of the paper. Paper: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks", "infer30b_after_item": " are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. \n Output: The Transformer is a new type of network architecture that is based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. This type of network architecture has been shown to be superior in quality while being more parallelizable and requiring significantly less time to train. \n\n", "infer13b_before_item": " The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.\n\n\nInput: Input: Give a brief explanation of the requested part of the paper. Paper: The dominant sequence transduction", "infer13b_after_item": " are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. \n Output: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. These models are typically composed of multiple layers of recurrent or convolutional neural networks, which are connected in a specific way to form a sequence transduction model. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.", "infer7b_before_item": " are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.\nInput: The dominant sequence trans", "infer7b_after_item": " are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. \n Output: The Transformer"} +{"id": 176, "instruction": "Answer the question about the paper after reading it carefully.", "input": "To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes.\nQuestion: What is English constituency parsing?", "infer30b_before_item": " Output: Answer the question about the paper after reading it carefully. To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes.\n\nInput: Input: Answer the question about the paper after reading it carefully. To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes.\n Question: What is English constituency parsing? \n Output: Output: Answer the question about the paper after reading it carefully. To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input.", "infer30b_after_item": " \n Output: English constituency parsing is a type of natural language processing task that involves analyzing a sentence to determine its syntactic structure. It involves identifying the constituents of a sentence, such as noun phrases, verb phrases, and prepositional phrases, and determining how they are related to each other. \n\n", "infer13b_before_item": " English constituency parsing is a task in natural language processing that involves the decomposition of a sentence into a tree representation.\n\nInput: Input: Answer the question about the paper after reading it carefully. To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes.\nQuestion: What is English constituency parsing? \n Output: English constituency parsing is a task in natural language processing that involves the decomposition of a sentence into a tree representation.\n\nInput: Input: Answer the question about the paper after reading it carefully. To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing. This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input. Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes.\nQuestion: What is English constituency", "infer13b_after_item": " \n Output: English constituency parsing is a task of analyzing a sentence and breaking it down into its constituent parts, such as words, phrases, and clauses. It is a form of natural language processing that involves the analysis of syntactic structures in a sentence. \n\n", "infer7b_before_item": " The output is subject to strong structural constraints and is significantly longer than the input.\n Question: What are the challenges of English constituency parsing? \n Output: The output is subject to strong structural constraints and is significantly longer than the input.\n Question: What are the challenges of English constituency parsing? \n Output: The output is subject to strong structural constraints and is significantly longer than the input.\n Question: What are the challenges of English constituency parsing? \n Output: The output is subject to strong structural constraints and is significantly longer than the input.\n Question: What are the challenges of English constituency parsing? \n Output: The output is subject to strong structural constraints and is significantly longer than the input.\n Question: What are the challenges of English constituency parsing? \n Output: The output is subject to strong structural constraints and is significantly longer than the input.\n Question: What are the challenges of English constituency parsing? \n Output: The output is subject to strong structural constraints and is significantly longer than the input.\n Question: What are the challenges of English constituency parsing? \n Output: The output is subject to strong structural constraints and is", "infer7b_after_item": " \n Output: English constituency parsing is a task in natural language processing that involves identifying the parts of speech (POS) of words in a sentence. It is a subtask of natural language understanding and is used to identify the syntactic structure of a sentence. The output of the task is a parse tree, which is a representation of the sentence structure. The task is challenging because it requires the model to identify the parts of speech of words in a sentence, which is subject to strong structural constraints. Additionally, the output of the task is significantly longer than the input, which can be a challenge for RNN sequence-to-sequence models. \n\n"} +{"id": 177, "instruction": "Recommend some helpful activities or conversation starters to use at meetups to break the ice.", "input": "", "infer30b_before_item": " Output: Recommend some helpful activities or conversation starters to use at meetups to break the ice. \n Algorithm: Algorithm: Recommend some helpful activities or conversation starters to use at meetups to break the ice. \n Data: Data: Recommend some helpful activities or conversation starters to use at meetups to break the ice. \n Platform: Platform: Recommend some helpful activities or conversation starters to use at meetups to break the ice. \n Technology: Technology: Recommend some helpful activities or conversation starters to use at meetups to break the ice. \n Resource: Resource: Recommend some helpful activities or conversation starters to use at meetups to break the ice. \n Tactics: Tactics: Recommend some helpful activities or conversation starters to use at meetups to break the ice. \n Method: Method: Recommend some helpful activities or conversation starters to use at meetups to break the ice. \n Strategy: Strategy: Recommend some helpful activities or conversation starters to use at meetups to break the ice. \n Policy: Policy: Recommend some helpful activities or conversation starters to use at meet", "infer30b_after_item": "\n Output: Some helpful activities or conversation starters to use at meetups to break the ice include: \n- Asking everyone to introduce themselves and share something interesting about themselves \n- Playing a game like charades or Pictionary \n- Sharing a funny story or joke \n- Talking about a current event or topic of interest \n- Asking everyone to share their favorite book or movie \n- Talking about a hobby or passion that everyone has in common \n- Talking about a recent success or accomplishment \n- Talking about a favorite travel destination \n- Talking about a favorite restaurant or food \n- Talking about a favorite sports team \n- Talking about a favorite music artist or band \n- Talking about a favorite TV show or movie \n- Talking about a favorite app or website \n- Talking about a favorite quote or saying \n- Talking about a favorite charity or cause \n- Talking about a favorite animal \n- Talking about a favorite movie or book character \n- Talking about a favorite game or activity \n- Talk", "infer13b_before_item": " Output: Recommend some helpful activities or conversation starters to use at meetups to break the ice.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: Here are some helpful activities or conversation starters to use at meetups to break the ice:\n- Ask people about their hobbies and interests.\n- Share a funny story or joke.\n- Ask people about their favorite books, movies, or TV shows.\n- Play a game or activity to get people talking.\n- Ask people about their career or goals.\n- Talk about current events or pop culture.\n- Ask people about their travels or experiences.\n- Share a personal story or anecdote.\n- Ask people about their favorite foods or restaurants.\n- Ask people about their family or friends.\n- Talk about the event or location.\n- Ask people about their favorite sports teams.\n- Share a quote or inspirational message.\n- Ask people about their favorite music or artists.\n- Ask people about their favorite movies or shows.\n- Ask people about their favorite places to visit.\n- Ask people about their favorite hobbies or activities.\n- Ask people about their favorite books or authors.\n- Ask people about their favorite TV shows or characters.\n- Ask people about their favorite sports or athletes.\n- Ask people", "infer7b_before_item": " Output: A list of activities or conversation starters to use at meetups to break the ice.\n\nComment: I'm not sure what you mean by \"break the ice\". Do you mean to get people to talk to each other? Do you mean to get people to talk about the topic of the meetup? Do you mean to get people to talk about themselves? Do you mean to get people to talk about the meetup? Do you mean to get people to talk about the meetup topic? Do you mean to get people to talk about the meetup topic in a way that is relevant to the meetup topic? Do you mean to get people to talk about the meetup topic in a way that is relevant to the meetup topic in a way that is relevant to the meetup topic?\n\nComment: I'm not sure what you mean by \"break the ice\". Do you mean to get people to talk to each other? Do you mean to get people to talk about the topic of the meetup? Do you mean to get people to talk about themselves? Do you mean to get people to talk about the meetup? Do you mean to get people to talk about", "infer7b_after_item": "\n Output: Some helpful activities or conversation starters to use at meetups to break the ice include: \n- Introduce yourself and ask others to do the same. \n- Ask questions about the other person's interests, hobbies, or career. \n- Ask questions about the group's common interests. \n- Ask questions about the group's history or background. \n- Ask questions about the group's goals or aspirations. \n- Ask questions about the group's current projects or initiatives. \n- Ask questions about the group's future plans. \n- Ask questions about the group's favorite books, movies, or TV shows. \n- Ask questions about the group's favorite places to visit or things to do. \n- Ask questions about the group's favorite foods or restaurants. \n- Ask questions about the group's favorite sports or hobbies. \n- Ask questions about the group's favorite music or artists. \n- Ask questions about the group's favorite memories or stories. \n- Ask questions about the group's favorite memes or jokes. \n- Ask questions about the"} +{"id": 178, "instruction": "Convert the given HTML into Markdown format.", "input": "<code>Use `code` in your Markdown file.</code>", "infer30b_before_item": " Output: Convert the given Markdown into HTML format. <code>Use `code` in your Markdown file.</code> \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "infer30b_after_item": " \n Output: Use `code` in your Markdown file. \n\n", "infer13b_before_item": " Output: Convert the given HTML into Markdown format. <code>Use `code` in your Markdown file.</code> \n\nInput: Input: Convert the given HTML into Markdown format. <code>Use `code` in your Markdown file.</code> \nOutput: Output: Convert the given HTML into Markdown format. <code>Use `code` in your Markdown file.</code> \n\nInput: Input: Convert the given HTML into Markdown format. <code>Use `code` in your Markdown file.</code> \nOutput: Output: Convert the given HTML into Markdown format. <code>Use `code` in your Markdown file.</code> \n\nInput: Input: Convert the given HTML into Markdown format. <code>Use `code` in your Markdown file.</code> \nOutput: Output: Convert the given HTML into Markdown format. <code>Use `code` in your Markdown file.</code> \n\nInput: Input: Convert the given HTML into Markdown format. <code>Use `code` in your Markdown file.</code> \nOutput: Output: Convert the given HTML into Markdown format. <", "infer13b_after_item": " \n Output: Use `code` in your Markdown file. \n\n", "infer7b_before_item": " Output: Convert the given Markdown into HTML format. <code>Use `code` in your HTML file.</code> \n*/\n\n// 1. Create a function that takes a string as input and returns a string as output.\n// 2. Test your function by passing in a string and checking that the output is the same as the input.\n\n// 1. Create a function that takes a string as input and returns a string as output.\n// 2. Test your function by passing in a string and checking that the output is the same as the input.\n\n// 1. Create a function that takes a string as input and returns a string as output.\n// 2. Test your function by passing in a string and checking that the output is the same as the input.\n\n// 1. Create a function that takes a string as input and returns a string as output.\n// 2. Test your function by passing in a string and checking that the output is the same as the input.\n\n// 1. Create a function that takes a string as input and returns a string as output.\n// 2. Test your function by passing in a string and checking that the output", "infer7b_after_item": " \n Output: ```Use code in your Markdown file.``` \n\n"} +{"id": 179, "instruction": "Extract the method that has been used in the research from the abstract of the paper. Give the exact sentences from the abstract that discuss the method.", "input": "We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a \"phoneme.\" Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.Collapse", "infer30b_before_item": " Output: Write a short paragraph that summarizes the method used in the research.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " )\n[1] We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a \"phoneme.\" Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems. \n Output: The method used in the research is end-to-end deep learning", "infer13b_before_item": " Output: Extract the method that has been used in the research from the abstract of the paper. Give the exact sentences from the abstract that discuss the method. We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a \"phoneme.\" Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-", "infer13b_after_item": " \n\n", "infer7b_before_item": " Output: Extract the sentences that discuss the results of the research. We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a \"phoneme.\" Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.Collapse\nInput: Input: Extract", "infer7b_after_item": "\n\nWe present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a \"phoneme.\" Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems. Collapse\n Output: We present a state-of-the-art speech recognition system developed using"} +{"id": 180, "instruction": "You need to come up with the names of some of the papers that relate to the paper you are given.", "input": "RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering", "infer30b_before_item": " Output: You need to come up with the names of some of the papers that relate to the paper you are given. RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering \n\n\"\"\"\n\nfrom __future__ import print_function\n\nimport sys\nimport os\nimport re\nimport json\nimport time\nimport datetime\nimport pprint\nimport copy\nimport math\nimport random\nimport itertools\nimport collections\nimport pickle\nimport pdb\nimport traceback\nimport pdb\nimport numpy as np\nimport pylab\nimport pickle\nfrom collections import defaultdict\nfrom datetime import datetime\n\n# mylibpath = '/home/arya/deepqa/deepqa'\nmylibpath = '/home/arya/deepqa/deepqa/data/dqa_data'\nsys.path.append(mylibpath)\n\nfrom deepqa.data.data_utils import *\nfrom deepqa.data.data_utils import *\nfrom deepqa.data.data_utils import *\nfrom deepqa.data.data_utils import *\nfrom deepqa.data.data_utils import", "infer30b_after_item": " \n Output: Some of the related papers to RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering are: \n- Robustness of Question Answering Systems: A Benchmark and Analysis\n- Evaluating the Robustness of Question Answering Systems\n- A Benchmark for Robustness of Question Answering Systems\n- A Benchmark for Robustness of Question Answering Systems: Evaluation and Analysis\n- A Benchmark for Robustness of Question Answering Systems: Evaluation and Analysis\n- A Benchmark for Robustness of Question Answering Systems: Evaluation and Analysis\n- A Benchmark for Robustness of Question Answering Systems: Evaluation and Analysis \n\n", "infer13b_before_item": " Output: You need to come up with the names of some of the papers that relate to the paper you are given.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Some papers related to RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering include: \n1. Multi-Evidence Question Answering: A Survey \n2. Multi-Evidence Question Answering: A Comprehensive Review \n3. Multi-Evidence Question Answering: A Comprehensive Survey \n4. Multi-Evidence Question Answering: A Comprehensive Survey \n5. Multi-Evidence Question Answering: A Comprehensive Survey \n6. Multi-Evidence Question Answering: A Comprehensive Survey \n7. Multi-Evidence Question Answering: A Comprehensive Survey \n8. Multi-Evidence Question Answering: A Comprehensive Survey \n9. Multi-Evidence Question Answering: A Comprehensive Survey \n10. Multi-Evidence Question Answering: A Comprehensive Survey \n\n", "infer7b_before_item": " You need to come up with the names of some of the papers that relate to the paper you are given.\n\n\\begin{code}\nimport os\nimport sys\nimport re\nimport json\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates", "infer7b_after_item": " \n Output: 1. Robust Multi-evidence Multi-answer Question Answering (RoMQA)\n2. Multi-evidence Multi-answer Question Answering (MMAQA)\n3. Multi-evidence Multi-answer Question Answering (MMAQA) with Multi-modal Evidence\n4. Multi-evidence Multi-answer Question Answering (MMAQA) with Multi-modal Evidence and Multi-lingual Questions\n5. Multi-evidence Multi-answer Question Answering (MMAQA) with Multi-modal Evidence and Multi-lingual Questions\n6. Multi-evidence Multi-answer Question Answering (MMAQA) with Multi-modal Evidence and Multi-lingual Questions with Multi-modal Evidence\n7. Multi-evidence Multi-answer Question Answering (MMAQA) with Multi-modal Evidence and Multi-lingual Questions with Multi-modal Evidence\n8. Multi-evidence Multi-answer Question Answering (MMAQA) with Multi-modal Evidence and Multi-lingual Questions with Multi-modal Evidence and Multi-lingual Questions\n9."} +{"id": 181, "instruction": "Create a short, concise summary of the paper based on its abstract.", "input": "Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of attention. In this paper, we focus on the FSL problem of dialogue understanding, which contains two closely related tasks: intent detection and slot filling. Dialogue understanding has been proven to benefit a lot from jointly learning the two sub-tasks. However, such joint learning becomes challenging in the few-shot scenarios: on the one hand, the sparsity of samples greatly magnifies the difficulty of modeling the connection between the two tasks; on the other hand, how to jointly learn multiple tasks in the few-shot setting is still less investigated. In response to this, we introduce FewJoint, the first FSL benchmark for joint dialogue understanding. FewJoint provides a new corpus with 59 different dialogue domains from real industrial API and a code platform to ease FSL experiment set-up, which are expected to advance the research of this field. Further, we find that insufficient performance of the few-shot setting often leads to noisy sharing between two sub-task and disturbs joint learning. To tackle this, we guide slot with explicit intent information and propose a novel trust gating mechanism that blocks low-confidence intent information to ensure high quality sharing. Besides, we introduce a Reptile-based meta-learning strategy to achieve better generalization in unseen few-shot domains. In the experiments, the proposed method brings significant improvements on two datasets and achieve new state-of-the-art performance.", "infer30b_before_item": " Output: Create a short, concise summary of the paper based on its abstract. Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of attention. In this paper, we focus on the FSL problem of dialogue understanding, which contains two closely related tasks: intent detection and slot filling. Dialogue understanding has been proven to benefit a lot from jointly learning the two sub-tasks. However, such joint learning becomes challenging in the few-shot scenarios: on the one hand, the sparsity of samples greatly magnifies the difficulty of modeling the connection between the two tasks; on the other hand, how to jointly learn multiple tasks in the few-shot setting is still less investigated. In response to this, we introduce FewJoint, the first FSL benchmark for joint dialogue understanding. FewJoint provides a new corpus with 59 different dialogue domains from real industrial API and a code platform to ease FSL experiment set-up, which are expected to advance the research of this field. Further, we find that insufficient performance of the few-shot setting often leads to noisy sharing between two sub-task and disturbs", "infer30b_after_item": " \n Output: This paper introduces FewJoint, a new benchmark for few-shot learning of joint dialogue understanding. It provides a new corpus with 59 different dialogue domains and a code platform to ease FSL experiment set-up. The authors also propose a Reptile-based meta-learning strategy to achieve better generalization in unseen few-shot domains. Experiments show that the proposed method brings significant improvements on two datasets and achieve new state-of-the-art performance. \n\n", "infer13b_before_item": " Output: Create a short, concise summary of the paper based on its abstract. Few-shot learning (FSL) is one of the key future steps in machine learning and raises a lot of attention. In this paper, we focus on the FSL problem of dialogue understanding, which contains two closely related tasks: intent detection and slot filling. Dialogue understanding has been proven to benefit a lot from jointly learning the two sub-tasks. However, such joint learning becomes challenging in the few-shot scenarios: on the one hand, the sparsity of samples greatly magnifies the difficulty of modeling the connection between the two tasks; on the other hand, how to jointly learn multiple tasks in the few-shot setting is still less investigated. In response to this, we introduce FewJoint, the first FSL benchmark for joint dialogue understanding. FewJoint provides a new corpus with 59 different dialogue domains from real industrial API and a code platform to ease FSL experiment set-up, which are expected to advance the research of this field. Further, we find that insufficient performance of the few-shot setting often leads to noisy sharing between two sub-task and disturbs", "infer13b_after_item": " \n Output: Few-shot learning (FSL) is a promising approach to machine learning, and this paper focuses on the FSL problem of dialogue understanding. The paper introduces FewJoint, a new benchmark for joint dialogue understanding, and proposes a novel trust gating mechanism and a Reptile-based meta-learning strategy to improve performance. The results show that the proposed method achieves significant improvements on two datasets and sets a new state-of-the-art performance. \n\n", "infer7b_before_item": " Output: Create a short, concise summary of the paper based on its abstract.\n\n\\end{document}\n\\end{code}\n\n<a href=\"https://i.stack.imgur.com/1111X.png\" rel=\"nofollow noreferrer\"><IMAGE></a>\n\nComment: Welcome to TeX.SE!\n\nComment: I'm not sure what you mean by \"the output is not what I want\". Please provide a [minimal working example (MWE)](https://tex.meta.stackexchange.com/q/228) that shows the problem.\n\nComment: @Mico I have added the MWE.\n\nComment: @Mico I have added the MWE.\n\nComment: @Mico I have added the MWE.\n\nComment: @Mico I have added the MWE.\n\nComment: @Mico I have added the MWE.\n\nComment: @Mico I have added the MWE.\n\nComment: @Mico I have added the MWE.\n\nComment: @Mico I have added the MWE.\n\nComment: @Mico I have added the MWE.\n", "infer7b_after_item": " \n Output: FewJoint is the first FSL benchmark for joint dialogue understanding. It provides a new corpus with 59 different dialogue domains from real industrial API and a code platform to ease FSL experiment set-up. To tackle the noisy sharing between two sub-tasks, we guide slot with explicit intent information and propose a novel trust gating mechanism. Additionally, we introduce a Reptile-based meta-learning strategy to achieve better generalization in unseen few-shot domains. Our method brings significant improvements on two datasets and achieves new state-of-the-art performance. \n\n"} +{"id": 182, "instruction": "You will need to compose the ABC notation for a given song.", "input": "The South Wind", "infer30b_before_item": " Output: You will need to compose the ABC notation for a given song. The South Wind \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "infer30b_after_item": " by Yo-Yo Ma. \n Output: The ABC notation for The South Wind by Yo-Yo Ma is as follows:\nG|:GFEDC BAGFEDC|GFEDC BAGFEDC|GFEDC BAGFEDC|GFEDC BAGFEDC|\nG|:GEDC BAGFEDC|GEDC BAGFEDC|GEDC BAGFEDC|GEDC BAGFEDC|\nG|:GEDC BAGFEDC|GEDC BAGFEDC|GEDC BAGFEDC|GEDC BAGFEDC|\nG|:GEDC BAGFEDC|GEDC BAGFEDC|GEDC BAGFEDC|GEDC B3|\n\n", "infer13b_before_item": " You will need to compose the ABC notation for a given song. The South Wind \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\nSong: \"The South Wind\" by George Frideric Handel \n Output: ABC: [A6 Bb C7 D7 E7 F#m G7 A7 D7 E7 F#m G7 A7 D7] \n\n", "infer7b_before_item": " Output: You will need to compose the ABC notation for a given song. The South Wind \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " Blows \n Output: ABC notation for The South Wind Blows:\nG D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D G D"} +{"id": 183, "instruction": "Categorize the given magazine based on its content into one of the categories: Politics, Entertainment News, Tech News, Business News, Parenting & Kids, Health & Healing, Cooking, Food & Wine, and Arts & Literature,", "input": "Inc.", "infer30b_before_item": " Output: The category of the given magazine.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": "\nInc. Magazine \n Output: Business News \n\n", "infer13b_before_item": " Output: Categorize the given magazine based on its content into one of the categories: Politics, Entertainment News, Tech News, Business News, Parenting & Kids, Health & Healing, Cooking, Food & Wine, and Arts & Literature, Inc. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " Magazine \n Output: Arts & Literature \n\n", "infer7b_before_item": " Output: Print the name of the category that the given magazine belongs to. \n Example: \n Input: \n Output: \n Explanation: \n 1. The magazine belongs to the category of Politics. \n 2. The magazine belongs to the category of Entertainment News. \n 3. The magazine belongs to the category of Tech News. \n 4. The magazine belongs to the category of Business News. \n 5. The magazine belongs to the category of Parenting & Kids. \n 6. The magazine belongs to the category of Health & Healing. \n 7. The magazine belongs to the category of Cooking. \n 8. The magazine belongs to the category of Food & Wine. \n 9. The magazine belongs to the category of Arts & Literature. \n 10. The magazine belongs to the category of Arts & Literature. \n 11. The magazine belongs to the category of Arts & Literature. \n 12. The magazine belongs to the category of Arts & Literature. \n 13. The magazine belongs to the category of Arts & Literature. \n 14.", "infer7b_after_item": " The magazine is \"The Economist\". \n Output: The Economist is a magazine that focuses on business news and politics. \n\n"} +{"id": 184, "instruction": "Decide if the given email belongs to the Promotions or Social category.", "input": "Subject: You have an invitation\nEmail: Hi, I'd like to join your LinkedIn network.", "infer30b_before_item": " Output: Decide if the given email belongs to the Promotions or Social category. Subject: You have an invitation\nEmail: Hi, I'd like to join your LinkedIn network. \n Expected: Output: Decide if the given email belongs to the Promotions or Social category. Subject: You have an invitation\nEmail: Hi, I'd like to join your LinkedIn network. \n*/\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Social \n\n", "infer13b_before_item": " Decide if the email belongs to the Promotions or Social category.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Promotions \n\n", "infer7b_before_item": " True if the email belongs to the Promotions category, otherwise false.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Promotions \n\n"} +{"id": 185, "instruction": "The story of a movie has been described using many emojis. You need to guess the name of the movie based on the emojis.", "input": "\ud83e\uddd9\u200d\u2642\ufe0f\ud83d\udeaa\ud83d\udcda\ud83e\udd85\u26a1", "infer30b_before_item": " Output: The name of the movie is \"The Lord of the Rings\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The Lion King. \n\n", "infer13b_before_item": " Output: The name of the movie.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\ufe0f\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ud83d\udc36\ufffd\ufffd\ufffd", "infer7b_before_item": " The output will be the name of the movie. \n\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n\n# Input: The story of a movie has been described using many emojis. You need to guess the name of the movie based on the emojis.\n# Output: The output will be the name of the movie.\n\n\n# Initial Solution\n\ndef emoji_movie_name(emoji_story)\n emoji_story.split(\" \").each do |emoji|\n if emoji == \"\ud83e\uddd9\u200d\u2642\ufe0f\"\n return \"The Godfather\"\n elsif emoji == \"\ud83d\udeaa\"\n return \"The Departed\"\n elsif emoji == \"\ud83d\udcda\"\n return \"The Shawshank Redemption\"\n elsif emoji == \"\ud83e\udd85\"\n return \"The Lion King\"\n elsif emoji == \"\u26a1\"\n return \"The Dark Knight\"\n end\n end", "infer7b_after_item": "\ufe0f\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ud83c\udfa5\ud83c\udfac\ufffd\ufffd\ufffd"} +{"id": 186, "instruction": "Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary.", "input": "", "infer30b_before_item": " Output: Write a short story based on your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Output: Write a short story based on your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Output: Write a short story based on your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Output: Write a short story based on your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Output: Write a short story based on your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Output: Write a short story based on your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot", "infer30b_after_item": "\n Output: Title: The Last Stand\nSummary: In a dystopian future, a group of rebels must band together to fight against a powerful and oppressive government. With the help of a mysterious stranger, they must use their wits and courage to take a stand and fight for their freedom. \n\n", "infer13b_before_item": " Output: Come up with a list of 10 interesting movie titles.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: The title of the movie is \"The Unlikely Hero\". It is a story about a young man who discovers a hidden power within himself and uses it to save the world from an evil force. He is an unlikely hero, as he is not a superhero or a warrior, but a regular person with a special gift. He must learn to use his power and find the courage to stand up to the evil force. With the help of his friends and family, he must find a way to defeat the evil and restore peace to the world. \n\n", "infer7b_before_item": " Come up with a title and a summary for your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Come up with a title and a summary for your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Come up with a title and a summary for your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Come up with a title and a summary for your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Come up with a title and a summary for your movie plot.\nInput: Input: Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary. \n Output: Come up with a title and a summary for your movie plot.\nInput: Input: Come up with an interesting idea for a", "infer7b_after_item": "\n Output: Title: The Secret of the Lost City\n\nSummary: A group of explorers embark on a journey to find a lost city in the jungle. Along the way, they encounter a mysterious tribe and uncover a secret that could change the world forever. \n\n"} +{"id": 187, "instruction": "Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose.", "input": "We were recently able to increase the amount of stock we hold with the same supplier thereby reducing our risk.", "infer30b_before_item": " We recently increased the amount of stock we hold with the same supplier thereby reducing our risk.\nInput: Input: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We have been able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \n Output: We have been able to increase the amount of stock we hold with the same supplier thereby reducing our risk.\nInput: Input: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We have been able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \n Output: We have been able to increase the amount of stock we hold with the same supplier thereby reducing our risk.\nInput: Input: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We have been able to increase the amount of stock we hold", "infer30b_after_item": " \n Output: Recently, the company was able to increase the amount of stock they hold with the same supplier, thereby reducing their risk. \n\n", "infer13b_before_item": " Output: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We were recently able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \nInput: Input: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We were recently able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \nOutput: Output: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We were recently able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \nInput: Input: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We were recently able to increase the amount of stock we hold with the same supplier thereby", "infer13b_after_item": " \n Output: The supplier was recently able to increase the amount of stock they hold for the company, thereby reducing the company's risk. \n\n", "infer7b_before_item": " We were recently able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \nInput: Input: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We were recently able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \n Output: We were recently able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \nInput: Input: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We were recently able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \n Output: We were recently able to increase the amount of stock we hold with the same supplier thereby reducing our risk. \nInput: Input: Change the first person to the third person in the given sentence. The meaning should be kept, but you can paraphrase it or expand it in order to have a better pose. We were recently able to increase", "infer7b_after_item": " \n Output: They were recently able to increase the amount of stock they hold with the same supplier thereby reducing their risk. \n\n"} +{"id": 188, "instruction": "You should capitalize the sentence according to the guide.", "input": "Guide: Every other letter alternates between lower case and upper case.\nSentence: A giant spider blocks your path.", "infer30b_before_item": " Output: A Giant Spider Blocks Your Path.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: A Giant SpidEr BlOcks Your PaTh. \n\n", "infer13b_before_item": " You should capitalize the sentence according to the guide. Guide: Every other letter alternates between lower case and upper case.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: A GiANT Spider Blocks Your Path. \n\n", "infer7b_before_item": " A giant spider blocks your path.\n\nInput: You should capitalize the sentence according to the guide. Guide: Every other letter alternates between lower case and upper case.\nSentence: A giant spider blocks your path. \n Output: A giant spider blocks your path.\n\nInput: You should capitalize the sentence according to the guide. Guide: Every other letter alternates between lower case and upper case.\nSentence: A giant spider blocks your path. \n Output: A giant spider blocks your path.\n\nInput: You should capitalize the sentence according to the guide. Guide: Every other letter alternates between lower case and upper case.\nSentence: A giant spider blocks your path. \n Output: A giant spider blocks your path.\n\nInput: You should capitalize the sentence according to the guide. Guide: Every other letter alternates between lower case and upper case.\nSentence: A giant spider blocks your path. \n Output: A giant spider blocks your path.\n\nInput: You should capitalize the sentence according to the guide. Guide: Every other letter alternates between lower case and upper case.\nSent", "infer7b_after_item": " \n Output: A Giant Spider Blocks Your Path. \n\n"} +{"id": 189, "instruction": "Create alliterations by finding synonyms for words in the given sentence.", "input": "David wears a hat everyday.", "infer30b_before_item": " Output: Create alliterations by finding synonyms for words in the given sentence. David wears a hat everyday.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: David dons a hat daily. \n\n", "infer13b_before_item": " Output: David wears a hat everyday. \n\nInput: Input: Create alliterations by finding synonyms for words in the given sentence. David wears a hat everyday. \n Output: Output: David wears a hat everyday. \n\nInput: Input: Create alliterations by finding synonyms for words in the given sentence. David wears a hat everyday. \n Output: Output: David wears a hat everyday. \n\nInput: Input: Create alliterations by finding synonyms for words in the given sentence. David wears a hat everyday. \n Output: Output: David wears a hat everyday. \n\nInput: Input: Create alliterations by finding synonyms for words in the given sentence. David wears a hat everyday. \n Output: Output: David wears a hat everyday. \n\nInput: Input: Create alliterations by finding synonyms for words in the given sentence. David wears a hat everyday. \n Output: Output: David wears a hat everyday. \n\nInput: Input: Create alliterations by finding synonyms for words in the given", "infer13b_after_item": " \n Output: David dons a dapper derby daily. \n\n", "infer7b_before_item": " Output: Create alliterations by finding synonyms for words in the given sentence. David wears a hat everyday.\n*/\n\n// 1. Define a function called alliterate that takes a sentence as an argument and returns a string of alliterations.\n// 2. Create a function called alliterate that takes a sentence as an argument and returns a string of alliterations.\n// 3. Create a function called alliterate that takes a sentence as an argument and returns a string of alliterations.\n// 4. Create a function called alliterate that takes a sentence as an argument and returns a string of alliterations.\n// 5. Create a function called alliterate that takes a sentence as an argument and returns a string of alliterations.\n// 6. Create a function called alliterate that takes a sentence as an argument and returns a string of alliterations.\n// 7. Create a function called alliterate that takes a sentence as an argument and returns a string of alliterations.\n// 8. Create a function called alliterate that takes a sentence as an argument and returns a string of alliterations.\n// ", "infer7b_after_item": " \n Output: David wears a hat everyday. He constantly clutches a cap. \n\n"} +{"id": 190, "instruction": "Convert the given description to a bash command.", "input": "read the first 10 lines from f", "infer30b_before_item": " Output: Print the first 10 lines of f.\n\n Expected Output:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": "1.txt \n Output: head -n 10 f1.txt \n\n", "infer13b_before_item": " Output: Convert the given description to a bash command. read the first 10 lines from f \n\nInput: Input: Convert the given description to a bash command. read the first 10 lines from f \nOutput: Output: Convert the given description to a bash command. read the first 10 lines from f \n\nInput: Input: Convert the given description to a bash command. read the first 10 lines from f \nOutput: Output: Convert the given description to a bash command. read the first 10 lines from f \n\nInput: Input: Convert the given description to a bash command. read the first 10 lines from f \nOutput: Output: Convert the given description to a bash command. read the first 10 lines from f \n\nInput: Input: Convert the given description to a bash command. read the first 10 lines from f \nOutput: Output: Convert the given description to a bash command. read the first 10 lines from f \n\nInput: Input: Convert the given description to a bash command. read the first 10 lines from f \nOutput: Output: Convert the given description to a bash command.", "infer13b_after_item": "red.txt \n Output: cat fred.txt | head -n 10 \n\n", "infer7b_before_item": " Output: Print the command. \n*/\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n\nint main(int argc, char *argv[])\n{\n\tFILE *f;\n\tchar *line;\n\tchar *command;\n\tint i;\n\t\n\tif (argc!= 2)\n\t{\n\t\tprintf(\"Usage: %s <file>\\n\", argv[0]);\n\t\treturn 1;\n\t}\n\t\n\tf = fopen(argv[1], \"r\");\n\tif (f == NULL)\n\t{\n\t\tprintf(\"Cannot open file %s\\n\", argv[1]);\n\t\treturn 1;\n\t}\n\t\n\twhile ((line = fgets(line, 100, f))!= NULL)\n\t{\n\t\tif (line[0] == '#')\n\t\t\tcontinue;\n\t\t\n\t\tcommand = line;\n\t\t\n\t\tfor (i = 0; i < strlen(command); i++)\n\t\t{\n\t\t\t", "infer7b_after_item": "ruits.txt \n Output: echo \"Reading the first 10 lines from fruits.txt...\"\n\nfruits.txt | head -10 \n\n"} +{"id": 191, "instruction": "Please prepare a table containing a summary of the information of the participants at the conference.", "input": "The conference was called to discuss how to improve the quality of education in schools. The main speaker was Professor Crew Purdie (cpurdie@email.com), who spoke about the importance of setting high standards for students and teachers. He said that schools should be places where students can learn and be challenged. Other speakers included Teacher Oliver Malachi (oliver@email.com) and Parent Kolby Reese (kolbyreese82@email.com). The teacher talked about the need for teachers to be well-prepared and to have high expectations for their students. The parent spoke about the need for parents to be involved in their children's education. There was also a discussion about the importance of funding for schools.", "infer30b_before_item": " Output: Please prepare a table containing a summary of the information of the participants at the conference. The conference was called to discuss how to improve the quality of education in schools. The main speaker was Professor Crew Purdie (cpurdie@email.com), who spoke about the importance of setting high standards for students and teachers. He said that schools should be places where students can learn and be challenged. Other speakers included Teacher Oliver Malachi (oliver@email.com) and Parent Kolby Reese (kolbyreese82@email.com). The teacher talked about the need for teachers to be well-prepared and to have high expectations for their students. The parent spoke about the need for parents to be involved in their children's education. There was also a discussion about the importance of funding for schools. \n \n*/\n\n#include <iostream>\n#include <string>\n#include <vector>\n\nusing namespace std;\n\nint main() {\n string input;\n getline(cin, input);\n\n vector<string> words;\n string currentWord;\n int i = 0;\n for (int j = 0", "infer30b_after_item": " \n Output: | Name | Profession | Topic |\n|-----|------------|-------|\n|Professor Crew Purdie| Speaker | Setting high standards for students and teachers|\n|Teacher Oliver Malachi| Speaker | The need for teachers to be well-prepared and to have high expectations for their students|\n|Parent Kolby Reese| Speaker | The need for parents to be involved in their children's education|\n|All| Discussion | The importance of funding for schools| \n\n", "infer13b_before_item": " Output: Please prepare a table containing a summary of the information of the participants at the conference. The conference was called to discuss how to improve the quality of education in schools. The main speaker was Professor Crew Purdie (cpurdie@email.com), who spoke about the importance of setting high standards for students and teachers. He said that schools should be places where students can learn and be challenged. Other speakers included Teacher Oliver Malachi (oliver@email.com) and Parent Kolby Reese (kolbyreese82@email.com). The teacher talked about the need for teachers to be well-prepared and to have high expectations for their students. The parent spoke about the need for parents to be involved in their children's education. There was also a discussion about the importance of funding for schools. \n*/\n\n#include <stdio.h>\n#include <string.h>\n\nint main(void)\n{\n char *name;\n char *email;\n char *role;\n char *speech;\n char *speaker;\n char *speaker_email;\n char *speaker_role;\n char", "infer13b_after_item": " \n Output: | Participant | Email | Topic |\n| ----------------- | --------- | ------ |\n| Professor Crew Purdie | cpurdie@email.com | Setting high standards |\n| Teacher Oliver Malachi | oliver@email.com | Well-prepared teachers |\n| Parent Kolby Reese | kolbyreese82@email.com | Parental involvement |\n| Discussion about funding for schools | | | \n\n", "infer7b_before_item": " Output: Please write a summary of the information in the input. The conference was called to discuss how to improve the quality of education in schools. The main speaker was Professor Crew Purdie (cpurdie@email.com), who spoke about the importance of setting high standards for students and teachers. He said that schools should be places where students can learn and be challenged. Other speakers included Teacher Oliver Malachi (oliver@email.com) and Parent Kolby Reese (kolbyreese82@email.com). The teacher talked about the need for teachers to be well-prepared and to have high expectations for their students. The parent spoke about the need for parents to be involved in their children's education. There was also a discussion about the importance of funding for schools. \n\n### 2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.", "infer7b_after_item": " \n Output: | Participant | Name | Email |\n---|---|---|---|---|---|---|\nProfessor Crew Purdie | cpurdie@email.com |\nTeacher Oliver Malachi | oliver@email.com |\nParent Kolby Reese | kolbyreese82@email.com |\nFunding for Schools | |\nHigh Standards for Students and Teachers | |\nImportance of Parents' Involvement in Education | |\nHigh Expectations for Students | |\nImportance of Funding for Schools | |\nConclusion | |\n|\n|\n| \n\n"} +{"id": 192, "instruction": "List the concepts that should be learned before approaching the given complex concept.", "input": "Deep Learning", "infer30b_before_item": " Output: List the concepts that should be learned after approaching the given complex concept.\n\n \n*/\n\n#include <iostream>\n#include <list>\n#include <queue>\n#include <stack>\n#include <vector>\n#include <string>\n#include <cmath>\n#include <cstdio>\n#include <cstdlib>\n#include <cstring>\n#include <algorithm>\nusing namespace std;\n\n\nint main() {\n /* Enter your code here. Read input from STDIN. Print output to STDOUT */ \n int t;\n cin>>t;\n while(t--)\n {\n int n;\n cin>>n;\n list<int> l;\n for(int i=0;i<n;i++)\n {\n int a;\n cin>>a;\n l.push_back(a);\n }\n for(int i=0;i<n;i++)\n {\n if(l.size()==0)\n {\n cout<<\"0\"<<endl;\n break;", "infer30b_after_item": " \n Output: The concepts that should be learned before approaching Deep Learning include: \n- Linear Algebra \n- Probability and Statistics \n- Machine Learning Fundamentals \n- Computer Vision \n- Natural Language Processing \n- Neural Networks \n- Convolutional Neural Networks \n- Reinforcement Learning \n- Graphical Models \n- TensorFlow \n- Keras \n- PyTorch \n- OpenCV \n- NVIDIA DIGITS \n- Caffe \n- Theano \n- MXNet \n- Scikit-Learn \n- TensorFlow Gpu \n- Chainer \n- Deeplearning4j \n- Apache Mahout \n- Microsoft Cognitive Toolkit \n- Torch \n- OpenPose \n- Dlib \n- CNTK \n- MXNet Gpu \n- Caffe2 \n- TensorRT \n- Gluon \n- MXNet Automatic Differentiation \n- TensorFlow Metrics \n- TensorFlow Lite \n", "infer13b_before_item": " Output: List the concepts that should be learned after approaching the given complex concept.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Before approaching Deep Learning, it is important to have a basic understanding of Machine Learning, Artificial Intelligence, Neural Networks, and Mathematical Optimization. \n\n", "infer7b_before_item": " Output: List the concepts that should be learned after approaching the given complex concept. Deep Learning \n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Before approaching the complex concept of Deep Learning, it is important to understand the following concepts:\n- Machine Learning\n- Artificial Intelligence\n- Neural Networks\n- Supervised Learning\n- Unsupervised Learning\n- Reinforcement Learning\n- Natural Language Processing\n- Computer Vision\n- Generative Adversarial Networks\n- Convolutional Neural Networks\n- Recurrent Neural Networks\n- Long Short-Term Memory Networks\n- Autoencoders\n- Dropout\n- Batch Normalization\n- Adam Optimizer\n- Cross-Entropy Loss\n- Regression\n- Classification\n- Feedforward Networks\n- Backpropagation\n- Gradient Descent\n- Optimization\n- Regularization\n- Convolutional Neural Networks\n- Recurrent Neural Networks\n- Long Short-Term Memory Networks\n- Autoencoders\n- Dropout\n- Batch Normalization\n- Adam Optimizer\n- Cross-Entropy Loss\n- Regression\n- Classification\n- Feedforward Networks\n- Backpropagation\n- Gradient Descent\n- Optimization\n"} +{"id": 193, "instruction": "Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words.", "input": "The chicken is ready to eat.", "infer30b_before_item": " Output: The chicken is ready to be eaten.\n\n Input: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: Output: The chicken is ready to be eaten.\n\n Input: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: Output: The chicken is ready to be eaten.\n\n Input: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: Output: The chicken is ready to be eaten.\n\n Input: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: Output: The chicken is ready to be eaten.\n\n Input: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: Output: The chicken is ready to be eaten.\n\n Input:", "infer30b_after_item": " \n Output: The chicken is cooked and ready to be eaten. \n\n", "infer13b_before_item": " The chicken is ready to be eaten.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The chicken is prepared to be eaten. \n\n", "infer7b_before_item": " The chicken is ready to eat.\n\nInput: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: The chicken is ready to eat.\n\nInput: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: The chicken is ready to eat.\n\nInput: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: The chicken is ready to eat.\n\nInput: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: The chicken is ready to eat.\n\nInput: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is ready to eat. \n Output: The chicken is ready to eat.\n\nInput: Input: Given a sentence that may be ambiguous, rewrite the sentence using less ambiguous words. The chicken is", "infer7b_after_item": " \n Output: The chicken is prepared for consumption. \n\n"} +{"id": 194, "instruction": "Assess the sentiment of the given tweet and categorize it as Positive, Negative, or Neutral.", "input": "\"This is the best cheat-sheet paper that I've found in a LONG time.\"", "infer30b_before_item": " Output: \"Positive\"\n\n Input: Input: Assess the sentiment of the given tweet and categorize it as Positive, Negative, or Neutral. \"I'm so sick of this class.\" \n Output: Output: \"Negative\"\n\n Input: Input: Assess the sentiment of the given tweet and categorize it as Positive, Negative, or Neutral. \"I'm so sick of this class.\" \n Output: Output: \"Negative\"\n\n Input: Input: Assess the sentiment of the given tweet and categorize it as Positive, Negative, or Neutral. \"I'm so sick of this class.\" \n Output: Output: \"Negative\"\n\n Input: Input: Assess the sentiment of the given tweet and categorize it as Positive, Negative, or Neutral. \"I'm so sick of this class.\" \n Output: Output: \"Negative\"\n\n Input: Input: Assess the sentiment of the given tweet and categorize it as Positive, Negative, or Neutral. \"I'm so sick of this class.\" ", "infer30b_after_item": " \n Output: Positive \n\n", "infer13b_before_item": " Output: Assess the sentiment of the given tweet and categorize it as Positive, Negative, or Neutral. \"This is the best cheat-sheet paper that I've found in a LONG time.\" \n\n\"\"\"\n\nimport re\nimport nltk\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.stem.lancaster import LancasterStemmer\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.stem.snowball import SnowballStemmer\nfrom nltk.stem.s", "infer13b_after_item": " \n Output: Positive \n\n", "infer7b_before_item": " Output: Return the sentiment of the given tweet. \"This is the best cheat-sheet paper that I've found in a LONG time.\" \n\"\"\"\n\nimport re\nimport sys\nimport json\nimport time\nimport os\nimport random\nimport string\nimport math\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as mdates\nimport matplotlib.dates as", "infer7b_after_item": " \n Output: Positive \n\n"} +{"id": 195, "instruction": "You are given a Python command and are required to convert it into Ruby.", "input": "print(\"Hello World!\")", "infer30b_before_item": " Output: You are given a Python command and are required to convert it into Ruby. print(\"Hello World!\") \n Explanation: \n The print function in Python is equivalent to puts in Ruby. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ", "infer30b_after_item": " \n Output: puts(\"Hello World!\") \n\n", "infer13b_before_item": " Output: You are given a Ruby command and are required to convert it into Python. print(\"Hello World!\") \n\nInput: Input: You are given a Python command and are required to convert it into Ruby. print(\"Hello World!\") \n Output: Output: You are given a Ruby command and are required to convert it into Python. print(\"Hello World!\") \n\nInput: Input: You are given a Python command and are required to convert it into Ruby. print(\"Hello World!\") \n Output: Output: You are given a Ruby command and are required to convert it into Python. print(\"Hello World!\") \n\nInput: Input: You are given a Python command and are required to convert it into Ruby. print(\"Hello World!\") \n Output: Output: You are given a Ruby command and are required to convert it into Python. print(\"Hello World!\") \n\nInput: Input: You are given a Python command and are required to convert it into Ruby. print(\"Hello World!\") \n Output: Output: You are given a Ruby command and are required to convert it into Python. print(\"Hello World!\") \n\nInput: Input: You are given a Python command", "infer13b_after_item": " \n Output: puts \"Hello World!\" \n\n", "infer7b_before_item": " You are given a Ruby command and are required to convert it into Python. print(\"Hello World!\") \n\"\"\"\n\ndef convert(command):\n if command == \"print\":\n return \"puts\"\n elif command == \"puts\":\n return \"print\"\n elif command == \"print('Hello World!')\":\n return \"puts 'Hello World!'\"\n elif command == \"puts 'Hello World!'\" :\n return \"print('Hello World!')\"\n elif command == \"print('Hello World!')\" :\n return \"puts 'Hello World!'\"\n elif command == \"puts 'Hello World!'\" :\n return \"print('Hello World!')\"\n elif command == \"print('Hello World!')\" :\n return \"puts 'Hello World!'\"\n elif command == \"puts 'Hello World!'\" :\n return \"print('Hello World!')\"\n elif command == \"print('Hello World!')\" :\n return \"puts 'Hello World!'\"\n elif command == \"puts 'Hello World!'\" :\n return \"print('", "infer7b_after_item": " \n Output: puts \"Hello World!\" \n\n"} +{"id": 196, "instruction": "You are given a sentence in first-order logic. Translate it into natural language.", "input": "\u2203x\u2200y(soldier(x)\u2192general(x,y))", "infer30b_before_item": " Output: There is a soldier who is a general.\n\n*/\n\n#include <iostream>\n#include <string>\n#include <cstring>\n#include <cstdio>\n#include <cstdlib>\n#include <cmath>\n#include <algorithm>\nusing namespace std;\n\n#define ll long long\n\n#define mem(Arr,x) memset(Arr,x,sizeof(Arr))\n\nconst int maxN = 100000 + 10;\n\nint n, m;\nint S[maxN], T[maxN];\nint Cnt[maxN];\nint Id[maxN];\nint CntS[maxN], CntT[maxN];\nint CntS1[maxN], CntT1[maxN];\nint CntS2[maxN], CntT2[maxN];\nint CntS3[maxN], CntT3[maxN];\nint CntS4[maxN], CntT4[maxN];\nint CntS5[maxN], CntT5[maxN];\nint", "infer30b_after_item": " \n Output: There is an x such that if x is a soldier, then x is a general with respect to some y. \n\n", "infer13b_before_item": " You are given a sentence in first-order logic. Translate it into natural language. \u2203x\u2200y(soldier(x)\u2192general(x,y)) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Every soldier is a general. \n\n", "infer7b_before_item": " You are given a sentence in natural language. Translate it into first-order logic. \u2200x\u2200y(soldier(x)\u2192general(x,y)) \n*/\n\n// 1. Translate the sentence into first-order logic.\n// 2. Translate the sentence into natural language.\n\n// 1. Translate the sentence into first-order logic.\n// \u2200x\u2200y(soldier(x)\u2192general(x,y))\n\n// 2. Translate the sentence into natural language.\n// \u2200x\u2200y(soldier(x)\u2192general(x,y))\n\n// 3. Translate the sentence into natural language.\n// \u2200x\u2200y(soldier(x)\u2192general(x,y))\n\n// 4. Translate the sentence into natural language.\n// \u2200x\u2200y(soldier(x)\u2192general(x,y))\n\n// 5. Translate the sentence into natural language.\n// \u2200x\u2200y(soldier(x)\u2192general(x,y))\n\n// 6. Translate the sentence", "infer7b_after_item": " \n Output: \"For every soldier, if they are a general, then they are a general.\" \n\n"} +{"id": 197, "instruction": "You are given a search query and a document. Classify whether the document is relevant to the search query or not relevant.", "input": "Search: why sky is blue\nDocument: The Short Answer: Sunlight reaches Earth's atmosphere and is scattered in all directions by all the gases and particles in the air. Blue light is scattered more than the other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time.", "infer30b_before_item": " Output: Relevant\n\nInput: Input: You are given a search query and a document. Classify whether the document is relevant to the search query or not relevant. Search: why sky is blue\nDocument: The Short Answer: Sunlight reaches Earth's atmosphere and is scattered in all directions by all the gases and particles in the air. Blue light is scattered more than the other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time. \n Output: Output: Relevant\n\nInput: Input: You are given a search query and a document. Classify whether the document is relevant to the search query or not relevant. Search: why sky is blue\nDocument: The Short Answer: Sunlight reaches Earth's atmosphere and is scattered in all directions by all the gases and particles in the air. Blue light is scattered more than the other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time. \n Output: Output: Relevant\n\nInput: Input: You are given a search query and a document. Classify whether the document is relevant to the search query or not relevant. Search: why sky is", "infer30b_after_item": " \n Output: Relevant \n\n", "infer13b_before_item": " You are given a search query and a document. Classify whether the document is relevant to the search query or not relevant. Search: why sky is blue\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Relevant. \n\n", "infer7b_before_item": " Relevant\n\nInput: Input: You are given a search query and a document. Classify whether the document is relevant to the search query or not relevant. Search: why is the sky blue\nDocument: The Short Answer: Sunlight reaches Earth's atmosphere and is scattered in all directions by all the gases and particles in the air. Blue light is scattered more than the other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time. \n Output: Relevant\n\nInput: Input: You are given a search query and a document. Classify whether the document is relevant to the search query or not relevant. Search: why is the sky blue\nDocument: The Short Answer: Sunlight reaches Earth's atmosphere and is scattered in all directions by all the gases and particles in the air. Blue light is scattered more than the other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time. \n Output: Relevant\n\nInput: Input: You are given a search query and a document. Classify whether the document is relevant to the search query or not relevant. Search: why is the sky blue\nDocument", "infer7b_after_item": " \n Output: Relevant \n\n"} +{"id": 198, "instruction": "Create a list of subtopics for the given topic.", "input": "Music theory", "infer30b_before_item": " Output: Create a list of subtopics for the given topic. Music theory \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "infer30b_after_item": " \n Output: - Basic music theory\n- Intervals and chords\n- Scales and modes\n- Harmony and counterpoint\n- Rhythm and meter\n- Form and structure\n- Instrumentation and orchestration\n- Music notation and terminology\n- Musical analysis and interpretation\n- Music history and culture \n\n", "infer13b_before_item": " Output: Create a list of subtopics for the given topic. Music theory \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: - Fundamentals of Music Theory\n- Scales and Modes\n- Chords and Harmony\n- Rhythm and Meter\n- Notation and Transcription\n- Harmonic Analysis\n- Musical Forms\n- Musical Genres\n- Musical Instruments\n- Musical Styles\n- Musical Techniques\n- Musical Composition\n- Music History\n- Music Theory Applications\n- Music Theory Exercises\n- Music Theory Exams\n- Music Theory Examples\n- Music Theory Exercises for Kids\n- Music Theory for Beginners\n- Music Theory for Guitar\n- Music Theory for Piano\n- Music Theory for Singers\n- Music Theory for Violin\n- Music Theory for Bass\n- Music Theory for Drums\n- Music Theory for Ukulele\n- Music Theory for Saxophone\n- Music Theory for Flute\n- Music Theory for Clarinet\n- Music Theory for Trumpet\n- Music Theory for Trombone\n- Music Theory for Percussion\n- Music Theory for Harp\n- Music Theory for Guitar Pedagogy\n- Music Theory for Jazz\n- Music Theory for Classical Music\n- Music Theory for Pop Music\n", "infer7b_before_item": " Output: Create a list of subtopics for the given topic. Music theory\n*/\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: - Harmony\n- Melody\n- Rhythm\n- Form\n- Instruments\n- Composition\n- Arrangement\n- Improvisation\n- Analysis\n- History\n- Theory\n- Aesthetics\n- Technology\n- Education\n- Performance \n\n"} +{"id": 199, "instruction": "Summarize the movie in a snarky way. Try to explain the movie in just one sentence.", "input": "The Shining", "infer30b_before_item": " Output: Summarize the movie in a snarky way. Try to explain the movie in just one sentence.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The Shining is a story about a family's descent into madness and horror as they try to survive a haunted hotel. \n\n", "infer13b_before_item": " Output: Summarize the movie in a snarky way. Try to explain the movie in just one sentence. The Shining \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " (1980) \n Output: The Shining is a horror classic about a family who moves into a haunted hotel and experiences a series of terrifying events. \n\n", "infer7b_before_item": " Output: Summarize the movie in a snarky way. Try to explain the movie in just one sentence. The Shining \n\"\"\"\n\nimport re\n\ndef summarize(movie):\n \"\"\"\n Summarize the movie in a snarky way. Try to explain the movie in just one sentence.\n \"\"\"\n # Get the title\n title = movie.title\n # Get the year\n year = movie.year\n # Get the director\n director = movie.director\n # Get the genre\n genre = movie.genre\n # Get the runtime\n runtime = movie.runtime\n # Get the rating\n rating = movie.rating\n # Get the cast\n cast = movie.cast\n # Get the plot\n plot = movie.plot\n # Get the poster\n poster = movie.poster\n # Get the trailer\n trailer = movie.trailer\n # Get the summary\n summary = movie.summary\n # Get the synopsis\n synopsis = movie.synopsis\n # Get the release date\n release_date = movie.release_date", "infer7b_after_item": " \n Output: A family goes to a haunted hotel to find a new start, but instead they find a nightmare. \n\n"} +{"id": 200, "instruction": "Enter the words that satisfy the given condition.", "input": "5 Countries that Start with S", "infer30b_before_item": " Output: Print the names of the countries that satisfy the given condition.\n\n Expected Output:\n SRI LANKA\n SPAIN\n SINGAPORE\n SUDAN\n SURINAME\n\n */\n\n#include <stdio.h>\n#include <string.h>\n\nint main()\n{\n char str[100];\n scanf(\"%s\",str);\n int i=0;\n while(str[i]!='\\0')\n {\n if(str[i]=='S')\n {\n printf(\"%s\\n\",str);\n }\n i++;\n }\n return 0;\n}\n", "infer30b_after_item": " \n Output: Saudi Arabia, South Africa, Spain, Sweden, Switzerland. \n\n", "infer13b_before_item": " Output: Enter the words that satisfy the given condition. 5 Countries that Start with S \n\n\nclass Solution:\n # @param {string} s\n # @param {string} c\n # @return {string[]}\n def findWords(self, s, c):\n if s == '':\n return []\n if c == '':\n return []\n if s == c:\n return [s]\n if s[0] == c[0]:\n return self.findWords(s[1:], c[1:]) + self.findWords(s[:1], c)\n return self.findWords(s, c)\n\n\ns = Solution()\nprint s.findWords('', '')\nprint s.findWords('a', 'a')\nprint s.findWords('a', 'b')\nprint s.findWords('a', 'ab')\nprint s.findWords('a', 'abc')\nprint s.findWords('a', 'abcd')\nprint s.findWords('a', 'abcd')\n", "infer13b_after_item": " \n Output: Saudi Arabia, Singapore, South Africa, Spain, Sweden \n\n", "infer7b_before_item": " Output: The words that satisfy the given condition. 5 Countries that Start with S\n\nInput: Input: Enter the words that satisfy the given condition. 5 Countries that Start with S\n Output: Output: The words that satisfy the given condition. 5 Countries that Start with S\n\nInput: Input: Enter the words that satisfy the given condition. 5 Countries that Start with S\n Output: Output: The words that satisfy the given condition. 5 Countries that Start with S\n\nInput: Input: Enter the words that satisfy the given condition. 5 Countries that Start with S\n Output: Output: The words that satisfy the given condition. 5 Countries that Start with S\n\nInput: Input: Enter the words that satisfy the given condition. 5 Countries that Start with S\n Output: Output: The words that satisfy the given condition. 5 Countries that Start with S\n\nInput: Input: Enter the words that satisfy the given condition. 5 Countries that Start with S\n Output: Output: The words that satisfy the given condition. 5 Countries that Start with S\n\nInput: Input: Enter the words that satisfy the given condition. 5 Count", "infer7b_after_item": " \n Output: Spain, Sweden, Singapore, South Africa, and Switzerland. \n\n"} +{"id": 201, "instruction": "Provide a name for the dish given the ingredients and instructions.", "input": "INGREDIENTS:\n2 (5 oz) cans Bumble Bee\u00ae Solid White Albacore Tuna, drained\n1 avocado\n2 Tbsp Sriracha\n1 Tbsp Dijon mustard\n2 to 3 Tbsp celery, chopped\n2 Tbsp red onion, chopped\n2 green onions, chopped\n1 Tbsp fresh cilantro, chopped\nSalt and pepper, to taste\n2 heaping cups leafy green lettuce\n1 cup matchstick carrots\n4 (10 inch) whole wheat tortillas\nINSTRUCTIONS:\nIn a medium bowl, mash together tuna and avocado until combined. Add in the rest of the ingredients through the salt and pepper, mixing well.\nTo assemble, top each tortilla with a 1/2 cup leafy greens, 1/4 cup matchstick carrots and divide the tuna mixture evenly among the wraps. Tightly roll up the tortilla, slice and enjoy!", "infer30b_before_item": " Output: Provide a name for the dish given the ingredients and instructions. INGREDIENTS:\n1 (16 oz) package frozen chopped spinach, thawed and drained\n1 (14 oz) can diced tomatoes, undrained\n1 (14 oz) can chicken broth\n1 (10 oz) can condensed cream of chicken soup\n1 (10 oz) can condensed cream of mushroom soup\n1 (10 oz) can condensed cream of celery soup\n1 (10 oz) can condensed cream of asparagus soup\n1 (10 oz) can condensed cream of potato soup\n1 (10 oz) can condensed cream of broccoli soup\n1 (10 oz) can condensed cream of cauliflower soup\n1 (10 oz) can condensed cream of corn soup\n1 (10 oz) can condensed cream of tomato soup\n1 (10 oz) can condensed cream of cheddar cheese soup\n1", "infer30b_after_item": " \n Output: Tuna and Avocado Wraps. \n\n", "infer13b_before_item": " Output: Provide a name for the dish given the ingredients and instructions. INGREDIENTS:\n1 (15 oz) can chickpeas, drained\n1 (15 oz) can black beans, drained\n1 (15 oz) can kidney beans, drained\n1 (15 oz) can pinto beans, drained\n1 (15 oz) can garbanzo beans, drained\n1 (15 oz) can white beans, drained\n1 (15 oz) can navy beans, drained\n1 (15 oz) can cannellini beans, drained\n1 (15 oz) can great northern beans, drained\n1 (15 oz) can black-eyed peas, drained\n1 (15 oz) can pink beans, drained\n1 (15 oz) can lima beans, drained\n1 (15 oz) can butter beans, drained\n1 (15 oz) can green beans, drained\n1 (15 o", "infer13b_after_item": " \n Output: Tuna Tacos. \n\n", "infer7b_before_item": " Output: Provide a name for the dish given the ingredients and instructions. INGREDIENTS:\n2 (5 oz) cans Bumble Bee\u00ae Solid White Albacore Tuna, drained\n1 avocado\n2 Tbsp Sriracha\n1 Tbsp Dijon mustard\n2 to 3 Tbsp celery, chopped\n2 Tbsp red onion, chopped\n2 green onions, chopped\n1 Tbsp fresh cilantro, chopped\nSalt and pepper, to taste\n2 heaping cups leafy green lettuce\n1 cup matchstick carrots\n4 (10 inch) whole wheat tortillas\nINSTRUCTIONS:\nIn a medium bowl, mash together tuna and avocado until combined. Add in the rest of the ingredients through the salt and pepper, mixing well.\nTo assemble, top each tortilla with a 1/2 cup leafy greens, 1/4 cup matchstick carrots and divide the tuna mixture evenly among the wraps. Tightly roll up the tort", "infer7b_after_item": " \n Output: The dish is called \"Tuna Tostada\". \n\n"} +{"id": 202, "instruction": "Summarize a meeting from the given list of bullet points. Be sure to convert shorthand into a first-hand account.", "input": "Rose: Analyze data and presents findings\nJohn: propose new idea\nJane: appointed to head project\nTom: need more time to fix software bug", "infer30b_before_item": " Output: Summarize a meeting from the given list of bullet points. Be sure to convert shorthand into a first-hand account. Rose: Analyze data and presents findings\nJohn: propose new idea\nJane: appointed to head project\nTom: need more time to fix software bug \n Explanation: \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# \n# ", "infer30b_after_item": " \n Output: Rose presented her analysis of the data, which she had been working on for the past few weeks. John proposed a new idea that he believed could be beneficial to the project. Jane was appointed to head the project, and Tom requested more time to fix the software bug that he had been working on. \n\n", "infer13b_before_item": " Output: Summarize a meeting from the given list of bullet points. Be sure to convert shorthand into a first-hand account. Rose: Analyze data and presents findings\nJohn: propose new idea\nJane: appointed to head project\nTom: need more time to fix software bug \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Rose presented the data analysis and findings she had done, while John proposed a new idea. Jane was appointed to head the project, and Tom requested more time to fix the software bug. \n\n", "infer7b_before_item": " Output: Write a summary of the meeting.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: At the meeting, Rose presented the findings of the data analysis, John proposed a new idea, and Jane was appointed to head the project. Tom said that he needs more time to fix the software bug. \n\n"} +{"id": 203, "instruction": "List a few popular songs from the given album.", "input": "Back To Black", "infer30b_before_item": " Output: List the album name. Back To Black\n\n Input: Input: List a few popular songs from the given album. The Blueprint 3\n Output: Output: List the album name. The Blueprint 3\n\n Input: Input: List a few popular songs from the given album. The Blueprint 3\n Output: Output: List the album name. The Blueprint 3\n\n Input: Input: List a few popular songs from the given album. The Blueprint 3\n Output: Output: List the album name. The Blueprint 3\n\n Input: Input: List a few popular songs from the given album. The Blueprint 3\n Output: Output: List the album name. The Blueprint 3\n\n Input: Input: List a few popular songs from the given album. The Blueprint 3\n Output: Output: List the album name. The Blueprint 3\n\n Input: Input: List a few popular songs from the given album. The Blueprint 3\n Output: Output: List the album name. The Blueprint 3\n\n Input: Input: List a few popular songs from the given album. The Blueprint 3\n Output: Output: List the album name", "infer30b_after_item": " by Amy Winehouse \n Output: Popular songs from Back To Black by Amy Winehouse include \"Rehab\", \"You Know I'm No Good\", \"Love Is A Losing Game\", \"Tears Dry On Their Own\", \"Back To Black\", and \"Wake Up Alone\". \n\n", "infer13b_before_item": " Output: List the songs in the album in the order they appear in the album.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": ", Amy Winehouse \n Output: Popular songs from the album Back To Black by Amy Winehouse include \"Rehab\", \"You Know I'm No Good\", \"Tears Dry on Their Own\", \"Back to Black\", and \"Love Is a Losing Game\". \n\n", "infer7b_before_item": " Output: List the artists who have performed the songs.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": ", Amy Winehouse \n Output: 1. Rehab\n2. You Know I'm No Good\n3. Tears Dry on Their Own\n4. Love Is a Losing Game\n5. Me & Mr. Jones\n6. Back to Black\n7. Stronger Than Me\n8. Valerie\n9. Monkey Man\n10. Wake Up Little Suzie \n\n"} +{"id": 204, "instruction": "Invent a new word based on a description of the word.", "input": "The feeling of knowing, while you are still in the moment, that something occurring will be remembered forever.", "infer30b_before_item": " Output: Invent a new word based on a description of the word. The feeling of knowing, while you are still in the moment, that something occurring will be remembered forever.\n\nInput: Input: Invent a new word based on a description of the word. The feeling of knowing, while you are still in the moment, that something occurring will be remembered forever. \n Output: Output: Invent a new word based on a description of the word. The feeling of knowing, while you are still in the moment, that something occurring will be remembered forever.\n\nInput: Input: Invent a new word based on a description of the word. The feeling of knowing, while you are still in the moment, that something occurring will be remembered forever. \n Output: Output: Invent a new word based on a description of the word. The feeling of knowing, while you are still in the moment, that something occurring will be remembered forever.\n\nInput: Input: Invent a new word based on a description of the word. The feeling of knowing, while you are still in the moment, that something occurring will be remembered forever. \n Output: Output: Invent a new word based on a", "infer30b_after_item": " \n Output: Memorication - the act of experiencing something so powerful that it will be remembered forever. \n\n", "infer13b_before_item": " Output: A new word that describes the feeling of knowing, while you are still in the moment, that something occurring will be remembered forever.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Ephemeralize. \n\n", "infer7b_before_item": " Output: A word that describes the feeling of knowing, while you are still in the moment, that something occurring will be remembered forever.\n\nComment: I'm not sure what you mean by \"a word that describes the feeling of knowing, while you are still in the moment, that something occurring will be remembered forever.\" Do you mean a word that describes the feeling of knowing that something will be remembered forever? Or do you mean a word that describes the feeling of knowing that something will be remembered forever while you are still in the moment? Or do you mean a word that describes the feeling of knowing that something will be remembered forever while you are still in the moment?\n\nComment: I mean a word that describes the feeling of knowing that something will be remembered forever while you are still in the moment.\n\nComment: I'm not sure what you mean by \"a word that describes the feeling of knowing that something will be remembered forever while you are still in the moment.\" Do you mean a word that describes the feeling of knowing that something will be remembered forever while you are still in the moment? Or do you mean a word that describes the feeling of knowing that something will be remembered forever while you are still in the", "infer7b_after_item": " \n Output: Eternitymoment. \n\n"} +{"id": 205, "instruction": "Given a programming language and the name of a function, write a command to show how to use the function.", "input": "Language: Python\nFunction: input", "infer30b_before_item": " Output: The function's documentation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: print(\"To use the input function in Python, type:\")\nprint(\"input('Enter a string:')\") \n\n", "infer13b_before_item": "\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:\n\nInput:\n\nOutput:", "infer13b_after_item": "() \n Output: To use the input() function in Python, you can type the following command:\n\n>>> input(\"Enter your name: \")\n\nThis will prompt the user to enter their name and then return the inputted value. \n\n", "infer7b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "() \n Output: In Python, the input() function is used to read a line of input from the user. For example, to read a line of input from the user, you can use the following command:\n\ninput(\"Please enter a line of text: \") \n\n"} +{"id": 206, "instruction": "Provide a pleasant compliment based on the quality you have been given.", "input": "Truthfulness", "infer30b_before_item": " Output: Provide a pleasant compliment based on the quality you have been given. Truthfulness \n */\n\npublic class Compliment {\n\n public static void main(String[] args) {\n Scanner input = new Scanner(System.in);\n String quality = input.nextLine();\n String compliment = compliment(quality);\n System.out.println(compliment);\n }\n\n public static String compliment(String quality) {\n String compliment = \"\";\n if (quality.equals(\"smart\")) {\n compliment = \"You are so smart!\";\n } else if (quality.equals(\"funny\")) {\n compliment = \"You are so funny!\";\n } else if (quality.equals(\"attractive\")) {\n compliment = \"You are so attractive!\";\n } else if (quality.equals(\"kind\")) {\n compliment = \"You are so kind!\";\n } else if (quality.equals(\"truthful\")) {\n compliment = \"You are so truthful!\";\n } else {\n compliment =", "infer30b_after_item": " \n Output: You are so honest and trustworthy, I really appreciate that. \n\n", "infer13b_before_item": " Output: Provide a pleasant compliment based on the quality you have been given. Truthfulness \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: You are so honest and trustworthy, it's a quality that is rare to find. \n\n", "infer7b_before_item": " Output: Provide a pleasant compliment based on the quality you have been given. Truthfulness \n\n\"\"\"\n\n\nclass Truthfulness:\n def __init__(self):\n self.truthfulness = 0\n\n def __str__(self):\n return \"Truthfulness: \" + str(self.truthfulness)\n\n def __repr__(self):\n return \"Truthfulness: \" + str(self.truthfulness)\n\n def __add__(self, other):\n self.truthfulness += other\n return self\n\n def __sub__(self, other):\n self.truthfulness -= other\n return self\n\n def __mul__(self, other):\n self.truthfulness *= other\n return self\n\n def __div__(self, other):\n self.truthfulness /= other\n return self\n\n def __mod__(self, other):\n self.truthfulness %= other\n return self\n\n def __pow__(self, other):\n self.", "infer7b_after_item": " \n Output: You are so honest and trustworthy, I can always count on you to tell me the truth. \n\n"} +{"id": 207, "instruction": "Create a brief resume introduction using the following information.", "input": "name: Mark\nnationality: Italian\nlanguage: English\nattributes: creative, goal-oriented, motivated\nskills: software engineering, content marketing", "infer30b_before_item": " Output: Create a brief resume introduction using the following information. name: Mark\nnationality: Italian\nlanguage: English\nattributes: creative, goal-oriented, motivated\nskills: software engineering, content marketing \n Expected result: Expected result: Create a brief resume introduction using the following information. name: Mark\nnationality: Italian\nlanguage: English\nattributes: creative, goal-oriented, motivated\nskills: software engineering, content marketing \n Author: Author: Mark\n Date: Date: 2017-02-15\n----------------------------------------------------------------------------------------------------\n*/\n\n#include <iostream>\n#include <string>\n\nusing namespace std;\n\nint main() {\n string name;\n string nationality;\n string language;\n string attributes;\n string skills;\n\n cout << \"Input: Input: Create a brief resume introduction using the following information.\" << endl;\n cout << \"name: \";\n cin >> name;\n cout << \"nationality: \";\n cin >> nationality;\n cout << \"language: \";\n cin", "infer30b_after_item": ", web development \n Output: I am Mark, an Italian national with a strong command of the English language. I am a creative, goal-oriented and motivated individual with skills in software engineering, content marketing, and web development. \n\n", "infer13b_before_item": " Output: Create a brief resume introduction using the following information. name: Mark\nnationality: Italian\nlanguage: English\nattributes: creative, goal-oriented, motivated\nskills: software engineering, content marketing \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: I am a creative, goal-oriented, and motivated software engineer with a passion for content marketing. I am an Italian national with a strong command of the English language. I am highly skilled in software engineering and content marketing, and I am always looking for new ways to develop my skills and further my career. \n\n", "infer7b_before_item": " Create a brief resume introduction using the following information. name: Mark\nnationality: Italian\nlanguage: English\nattributes: creative, goal-oriented, motivated\nskills: software engineering, content marketing\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Mark is an Italian software engineer with a passion for content marketing. He is creative, goal-oriented, and motivated, with a strong background in software engineering and content marketing. \n\n"} +{"id": 208, "instruction": "Make a list of adjectives that can be used to describe the given brand.", "input": "a creative tech startup", "infer30b_before_item": " Output: Make a list of adjectives that can be used to describe the given brand. a creative tech startup \n Explanation: Output: Make a list of adjectives that can be used to describe the given brand. a creative tech startup \n */\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Innovative, cutting-edge, forward-thinking, disruptive, revolutionary, pioneering, progressive, dynamic, creative, original, visionary, groundbreaking, transformative. \n\n", "infer13b_before_item": " Output: Make a list of adjectives that can be used to describe the given brand. a creative tech startup \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Innovative, creative, cutting-edge, forward-thinking, tech-savvy, progressive, dynamic, ambitious, visionary, pioneering, entrepreneurial. \n\n", "infer7b_before_item": " Output: Make a list of adjectives that can be used to describe the given brand. a creative tech startup \n\"\"\"\n\n\ndef get_adjectives(brand):\n \"\"\"\n :type brand: str\n :rtype: list\n \"\"\"\n adjectives = []\n for word in brand.split():\n if word in adjectives:\n adjectives.remove(word)\n else:\n adjectives.append(word)\n return adjectives\n\n\ndef main():\n \"\"\"\n :rtype: None\n \"\"\"\n brand = input(\"Input: \")\n adjectives = get_adjectives(brand)\n print(adjectives)\n\n\nif __name__ == \"__main__\":\n main()\n", "infer7b_after_item": " \n Output: Innovative, cutting-edge, dynamic, creative, forward-thinking, disruptive, revolutionary, visionary, ambitious, agile, adaptable, resourceful, collaborative, passionate, and entrepreneurial. \n\n"} +{"id": 209, "instruction": "Someone gives you some information about where they want to go and what they enjoy seeing and doing. Describe a proper road trip for them and find the best stops and sctivities in the way.", "input": "I'm looking for a west coast road trip. My goal is to start near San Diego and travel to Los Angeles.", "infer30b_before_item": " Output: A list of places to stop and things to do along the way.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: For a west coast road trip from San Diego to Los Angeles, there are plenty of great stops and activities along the way. Start your journey in San Diego and make your way up the coast. Stop in Carlsbad to visit the beautiful Carlsbad Flower Fields and take a stroll along the beach. Then, head to San Clemente to explore the San Clemente State Beach and the San Onofre State Beach. Continue on to Laguna Beach and take a walk along the boardwalk, or take a surfing lesson. Next, stop in Newport Beach to explore the Newport Beach Pier and take a boat tour of the harbor. Finally, make your way to Los Angeles and explore the city. Visit the Hollywood Walk of Fame, take a tour of the Warner Brothers Studio, and take a stroll through the Santa Monica Pier. \n\n", "infer13b_before_item": " Output: A list of stops and activities that you think would be good for the person.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " I enjoy visiting beaches and exploring nature. \n Output: A proper road trip for you would start in San Diego and travel to Los Angeles. Along the way, you can stop at beaches like La Jolla, Carlsbad, and Coronado. You can also visit the San Diego Zoo and Balboa Park. In Los Angeles, you can visit the Getty Center, Griffith Observatory, and Santa Monica Pier. \n\n", "infer7b_before_item": " I want to find the best stops and activities along the way. I want to find the best places to stop and see and do things. I want to find the best places to eat and sleep. I want to find the best places to see and do things. I want to find the best places to eat and sleep. I want to find the best places to see and do things. I want to find the best places to eat and sleep. I want to find the best places to see and do things. I want to find the best places to eat and sleep. I want to find the best places to see and do things. I want to find the best places to eat and sleep. I want to find the best places to see and do things. I want to find the best places to eat and sleep. I want to find the best places to see and do things. I want to find the best places to eat and sleep. I want to find the best places to see and do things. I want to find the best places to eat and sleep. I want to find the best places to see and do things. I want to find the best places to eat and sleep. I want to find the best places to see and do things. I want to", "infer7b_after_item": " I enjoy seeing the ocean, hiking, and exploring new places. \n Output: A great road trip for the west coast would start in San Diego and take you to Los Angeles. Along the way, you can stop at the famous beaches of La Jolla, visit the famous Hollywood sign, and explore the beautiful canyons of Malibu. You can also take a day trip to the famous Joshua Tree National Park, or stop in Santa Barbara to explore the charming city. Along the way, you can also stop at the famous Hearst Castle, or take a day trip to the famous Yosemite National Park. \n\n"} +{"id": 210, "instruction": "Regex is a set of characters that define a search pattern. This kind of pattern is usually used by string-searching algorithms to find or replace strings or to validate inputs. Convert the natural english text to regex.", "input": "Match a valid ip address", "infer30b_before_item": " Output: Regex is a set of characters that define a search pattern. This kind of pattern is usually used by string-searching algorithms to find or replace strings or to validate inputs. Convert the natural english text to regex. Match a valid ip address \n\n Example: \n\n Input: \n\n Output: \n\n Explanation: \n\n */\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n\nint main() {\n char *input = \"1234567890\";\n char *output = \"\\\\d{10}\";\n printf(\"%s\\n\", output);\n return 0;\n}\n", "infer30b_after_item": ". \n Output: ^[0-9]{1,3}\\.[0-9]{1,3}\\.[0-9]{1,3}\\.[0-9]{1,3}$ \n\n", "infer13b_before_item": " Output: Regex is a set of characters that define a search pattern. This kind of pattern is usually used by string-searching algorithms to find or replace strings or to validate inputs. Convert the natural english text to regex. Match a valid ip address \n\n\nclass Solution:\n # @param {string} s\n # @return {string}\n def regex(self, s):\n s = s.replace(\" \", \"\")\n s = s.replace(\"(\", \"\")\n s = s.replace(\")\", \"\")\n s = s.replace(\"(\", \"\")\n s = s.replace(\")\", \"\")\n s = s.replace(\" \", \"\")\n s = s.replace(\"(\", \"\")\n s = s.replace(\")\", \"\")\n s = s.replace(\" \", \"\")\n s = s.replace(\"(\", \"\")\n s = s.replace(\")\", \"\")\n s = s.replace(\" \", \"\")\n s = s.replace(\"(\", \"\")\n s = s.replace(\")\", \"\")\n s = s.replace(\" \", \"\")\n s = s.replace(\"(\", \"\")\n s = s.replace(\")\", \"\")\n ", "infer13b_after_item": " \n Output: ^(?:(?!.*\\d+\\.\\d+\\.\\d+\\.\\d+).)*$ \n\n", "infer7b_before_item": " Match a valid ip address\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": ". 127.0.0.1 \n Output: ^(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})+$ \n\n"} +{"id": 211, "instruction": "Write an engaging and well-written property listing description for selling a house. Address of the house and some of the details are given to you. Fill in the information gap with hallucinations if needed.", "input": "Property Address: 412 Monterey Ave, Capitola, CA 95010\nParking: 3 cars + a finished single car garage\nDetails:\n- open floorplan\n- window with views to park/mountains\n- Kitchen with shaker cabinetry and white Calcatta quartz counters", "infer30b_before_item": "\n412 Monterey Ave, Capitola, CA 95010\nParking: 3 cars + a finished single car garage\nDetails:\n- open floorplan\n- window with views to park/mountains\n- Kitchen with shaker cabinetry and white Calcatta quartz counters\n# **Exercise 1.1**\nInput: Input: Write an engaging and well-written property listing description for selling a house. Address of the house and some of the details are given to you. Fill in the information gap with hallucinations if needed. Property Address: 412 Monterey Ave, Capitola, CA 95010\nParking: 3 cars + a finished single car garage\nDetails:\n- open floorplan\n- window with views to park/mountains\n- Kitchen with shaker cabinetry and white Calcatta quartz counters\n Output:\n412 Monterey Ave, Capitola, CA 95010\nParking: 3 cars + a finished single car garage\nDetails:\n- open floorplan\n- window", "infer30b_after_item": "\n- Master bedroom with walk-in closet and en-suite bathroom\n- Laundry room with storage \n Output: Welcome to 412 Monterey Ave, Capitola, CA 95010! This beautiful home is located in a quiet neighborhood and offers plenty of parking for up to 3 cars plus a finished single car garage. The open floorplan is perfect for entertaining, with a window that offers views to the park and mountains. The kitchen features shaker cabinetry and white Calcatta quartz counters, while the master bedroom has a walk-in closet and en-suite bathroom. The laundry room has storage, making it the perfect place to call home. \n\n", "infer13b_before_item": "\n- 3 bedrooms\n- 2 bathrooms\n- 1,500 sq. ft.\n- 1,000 sq. ft. lot\n- 1,000 sq. ft. garage\n- 1,000 sq. ft. deck\n- 1,000 sq. ft. basement\n- 1,000 sq. ft. attic\n- 1,000 sq. ft. crawlspace\n- 1,000 sq. ft. storage shed\n- 1,000 sq. ft. greenhouse\n- 1,000 sq. ft. pool\n- 1,000 sq. ft. hot tub\n- 1,000 sq. ft. sauna\n- 1,000 sq. ft. gym\n- 1,000 sq. ft. wine cellar\n- 1,000 sq. ft. home theater\n- 1,000 sq. ft. game room\n- 1,000 sq. ft. office\n- 1,000 sq", "infer13b_after_item": "\n- 3 bedrooms and 2 bathrooms \n Output: Welcome to 412 Monterey Ave, Capitola, CA 95010! This beautiful home offers a spacious open floorplan with plenty of natural light and views of the park and mountains. The kitchen is equipped with shaker cabinetry and white Calcatta quartz counters, perfect for entertaining. The master bedroom features an en-suite bathroom and walk-in closet. The other two bedrooms are also generously sized and share a full bathroom. The home also features a finished single car garage, perfect for storing your car or other belongings. This home is a must-see and is sure to impress! \n\n", "infer7b_before_item": "\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words\n- 1000 words", "infer7b_after_item": "\n- Master bedroom with walk-in closet and en-suite bathroom\n- 2 additional bedrooms\n- 2.5 bathrooms\n- 1,600 sq. ft. \n Output: 412 Monterey Ave, Capitola, CA 95010 is a stunning home with a perfect blend of modern and classic design. The open floorplan and large windows allow for plenty of natural light and views of the park and mountains. The kitchen features shaker cabinetry and white Calcatta quartz counters, while the master bedroom boasts a walk-in closet and en-suite bathroom. The 2.5 bathrooms and 1,600 sq. ft. of living space make this home perfect for any family. \n\n"} +{"id": 212, "instruction": "Given a real estate listing description, extract the information and details of the property from the text. The details consist of address, basic house attributes, etc.", "input": "WALK TO THE BEACH FROM CAPITOLA KNOLLS! Single-level upstairs condo with your own large private deck overlooking nature. Adorable home that is move-in ready and waiting for you to put your own personal touches on. Gorgeous and well-maintained landscaped park-like setting with lush green grasses with a community pool and spa to enjoy. Desirable location...walk to Capitola Village, Gayle's Bakery, restaurants, shopping, schools or jump on Hwy 1 right around the corner.", "infer30b_before_item": " Output: A list of the details of the property.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Address: 1234 Capitola Knolls, Capitola, CA 95010\n\nProperty Type: Condo\n\nBedrooms: 2\n\nBathrooms: 1\n\nLiving Area: 1,000 sq ft\n\nDeck Size: Large\n\nParking: 1\n\nAmenities: Private Deck, Community Pool and Spa, Lush Green Grasses, Walk to Capitola Village, Gayle's Bakery, Restaurants, Shopping, Schools. \n\n", "infer13b_before_item": " Output: A list of the extracted information and details of the property.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n\nWords: 225\n\nLink: https://www.redfin.com/CA/Capitola/100-Cabrillo-Dr-902-980/home/14000000 \n Output: Address: 100 Cabrillo Dr 902, Capitola, CA 95010\n\nBasic House Attributes:\n- Single-level upstairs condo\n- Large private deck\n- Move-in ready\n- Adorable home\n- Gorgeous and well-maintained landscaped park-like setting\n- Community pool and spa\n- Desirable location\n- Walk to Capitola Village, Gayle's Bakery, restaurants, shopping, schools\n- Jump on Hwy 1 right around the corner\n\nPrice: $1,400,000\n\nLink: https://www.redfin.com/CA/Capitola/100-Cabrillo-Dr-902-980/home/14000000 \n\n", "infer7b_before_item": " Output: Given a real estate listing description, extract the information and details of the property from the text. The details consist of address, basic house attributes, etc. WALK TO THE BEACH FROM CAPITOLA KNOLLS! Single-level upstairs condo with your own large private deck overlooking nature. Adorable home that is move-in ready and waiting for you to put your own personal touches on. Gorgeous and well-maintained landscaped park-like setting with lush green grasses with a community pool and spa to enjoy. Desirable location...walk to Capitola Village, Gayle's Bakery, restaurants, shopping, schools or jump on Hwy 1 right around the corner. \n\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"", "infer7b_after_item": " 1100 Capitola Knolls Drive, Capitola, CA 95010 831-476-1100 \n Output: Address: 1100 Capitola Knolls Drive, Capitola, CA 95010\nBasic House Attributes: Single-level upstairs condo with your own large private deck overlooking nature, Adorable home that is move-in ready and waiting for you to put your own personal touches on, Gorgeous and well-maintained landscaped park-like setting with lush green grasses with a community pool and spa to enjoy, Desirable location...walk to Capitola Village, Gayle's Bakery, restaurants, shopping, schools or jump on Hwy 1 right around the corner. \n\n"} +{"id": 213, "instruction": "Review the property description and tell us what is good and what is bad about it and list its key takeaways.", "input": "There's real magic in combining inherently compelling Victorian architecture with a modern aesthetic. Add in an extraordinary multi-level garden & the result is the undeniable elegance of 444 29th St. The front of the home is light-filled w/ a seamless connection between living, dining & kitchen. Newly updated kitchen w/ quartz counters & upgraded appliances. A lg dining rm has a bay of windows &, when paired w/ the adjacent living rm, provides an ideal space to entertain. 2 lg bdrms on this level are at the rear. Both w/ lg closets & walk out to the garden. Stylishly remodeled full bath on this level. 3rd bdrm/2nd full bath are located on the ground floor - perfect as a guest rm, home office, fitness area, etc. The enormous garden occupies an extra deep lot & is beautifully landscaped & terraced. Laundry/storage rm, 1-car garage pkg, Tesla solar panels. Prime, flat part of Noe Valley - short walk to all that Noe has to offer. Steps from the J-Church & convenient to shuttles/freeways.", "infer30b_before_item": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The property at 444 29th St. is a great combination of Victorian architecture and modern aesthetic. The front of the home is light-filled and has a seamless connection between living, dining and kitchen. The kitchen has been recently updated with quartz counters and upgraded appliances. The large dining room has a bay of windows and when paired with the adjacent living room, provides an ideal space to entertain. The two large bedrooms on this level have large closets and walk out to the garden. The third bedroom/second full bath is located on the ground floor and is perfect as a guest room, home office, fitness area, etc. The enormous garden occupies an extra deep lot and is beautifully landscaped and terraced. Laundry/storage room, 1-car garage parking, and Tesla solar panels are also included. The property is located in a prime, flat part of Noe Valley and is a short walk to all that Noe has to offer. It is also steps from the J-Church and convenient to shuttles and freeways. \n\n", "infer13b_before_item": " Output: Review the property description and tell us what is good and what is bad about it and list its key takeaways. There's real magic in combining inherently compelling Victorian architecture with a modern aesthetic. Add in an extraordinary multi-level garden & the result is the undeniable elegance of 444 29th St. The front of the home is light-filled w/ a seamless connection between living, dining & kitchen. Newly updated kitchen w/ quartz counters & upgraded appliances. A lg dining rm has a bay of windows &, when paired w/ the adjacent living rm, provides an ideal space to entertain. 2 lg bdrms on this level are at the rear. Both w/ lg closets & walk out to the garden. Stylishly remodeled full bath on this level. 3rd bdrm/2nd full bath are located on the ground floor - perfect as a guest rm, home office, fitness area, etc. The enormous garden occupies an extra deep lot & is beautifully landscaped & terraced. Laundry/storage rm, 1-car", "infer13b_after_item": " \n\n Output: Good:\n- Victorian architecture\n- Multi-level garden\n- Light-filled living, dining and kitchen\n- Updated kitchen with quartz counters and upgraded appliances\n- Dining room with bay of windows\n- Living room with adjacent dining room for entertaining\n- Two large bedrooms with large closets and windows\n- Two full bathrooms\n- Stylishly remodeled full bathroom\n- Laundry and storage room\n- One-car garage parking\n- Tesla solar panels\n\nBad:\n- Flat part of Noe Valley\n- Short walk to all that Noe has to offer\n- Convenient to shuttles and freeways \n\nKey Takeaways:\n- Victorian architecture with modern aesthetic\n- Multi-level garden\n- Light-filled living, dining and kitchen\n- Updated kitchen with quartz counters and upgraded appliances\n- Dining room with bay of windows\n- Living room with adjacent dining room for entertaining\n- Two large bedrooms with large closets and windows\n- Two full bathrooms\n- Stylishly remode", "infer7b_before_item": " Output: List the property's key selling points and tell us what you think the property's best features are. The garden is the property's best feature. It's a rare find in Noe Valley. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The garden is a perfect place to entertain, relax, or just enjoy the outdoors. The", "infer7b_after_item": " \n\nKey Takeaways:\n- Victorian architecture w/ modern aesthetic\n- Light-filled living spaces\n- Updated kitchen w/ quartz counters & upgraded appliances\n- Lg dining rm w/ bay of windows\n- Perfect for entertaining\n- 2 lg bdrms w/ lg closets & walk out to garden\n- Stylishly remodeled full bath\n- 3rd bdrm/2nd full bath\n- Prime, flat part of Noe Valley\n- Short walk to all that Noe has to offer\n- Steps from the J-Church & convenient to shuttles/freeways\n- 444 29th St. - real magic in combining inherently compelling Victorian architecture with a modern aesthetic. \n Output: 444 29th St. is a stunning Victorian home with a modern aesthetic. The front of the home is light-filled and seamlessly connects the living, dining, and kitchen areas. The kitchen has been updated with quartz counters and upgraded appliances. The dining room has"} +{"id": 214, "instruction": "List some of the top real estate marketing words to add value to the listing and engage more potential buyers.", "input": "", "infer30b_before_item": " Output: List some of the top real estate marketing words to add value to the listing and engage more potential buyers.\nInput: Input: List some of the top real estate marketing words to add value to the listing and engage more potential buyers. \n Output: Output: List some of the top real estate marketing words to add value to the listing and engage more potential buyers.\nInput: Input: List some of the top real estate marketing words to add value to the listing and engage more potential buyers. \n Output: Output: List some of the top real estate marketing words to add value to the listing and engage more potential buyers.\nInput: Input: List some of the top real estate marketing words to add value to the listing and engage more potential buyers. \n Output: Output: List some of the top real estate marketing words to add value to the listing and engage more potential buyers.\nInput: Input: List some of the top real estate marketing words to add value to the listing and engage more potential buyers. \n Output: Output: List some of the top real estate marketing words to add value to the listing and eng", "infer30b_after_item": "\n Output: Some of the top real estate marketing words to add value to the listing and engage more potential buyers include: luxurious, spacious, modern, convenient, charming, beautiful, private, gorgeous, stunning, and unique. \n\n", "infer13b_before_item": " Output: List some of the top real estate marketing words to add value to the listing and engage more potential buyers. \n**Note:** The output is the same as the input.\nExplanation:\nThe output is the same as the input because the input is a list of words, and the output is a list of words.\n##### **Question 11**\nInput: Input: The following is a list of words that are used in the English language. \nOutput: Output: The following is a list of words that are used in the English language. \n**Note:** The output is the same as the input.\nExplanation:\nThe output is the same as the input because the input is a list of words, and the output is a list of words.\n##### **Question 12**\nInput: Input: The following is a list of words that are used in the English language. \nOutput: Output: The following is a list of words that are used in the English language. \n**Note:** The output is the same as the input.\nExplanation:\nThe output is the same as the input because the input is a list of", "infer13b_after_item": "\n Output: Luxurious, spacious, modern, stylish, high-end, unique, comfortable, inviting, energy-efficient, eco-friendly, and well-maintained. \n\n", "infer7b_before_item": " Output: List some of the top real estate marketing words to add value to the listing and engage more potential buyers.\nInput: Input: List some of the top real estate marketing words to add value to the listing and engage more potential buyers. \n Output: Output: List some of the top real estate marketing words to add value to the listing and engage more potential buyers.\nInput: Input: List some of the top real estate marketing words to add value to the listing and engage more potential buyers. \n Output: Output: List some of the top real estate marketing words to add value to the listing and engage more potential buyers.\nInput: Input: List some of the top real estate marketing words to add value to the listing and engage more potential buyers. \n Output: Output: List some of the top real estate marketing words to add value to the listing and engage more potential buyers.\nInput: Input: List some of the top real estate marketing words to add value to the listing and engage more potential buyers. \n Output: Output: List some of the top real estate marketing words to add value to the listing and eng", "infer7b_after_item": "\n Output: Some of the top real estate marketing words to add value to the listing and engage more potential buyers include: luxury, modern, spacious, open concept, and move-in ready. \n\n"} +{"id": 215, "instruction": "Suggest some product bundles that are often purchased with a given product.", "input": "MacBook Air", "infer30b_before_item": " Output: Suggest some product bundles that are often purchased with a given product. MacBook Air \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "infer30b_after_item": " \n Output: Product bundles that are often purchased with a MacBook Air include a laptop case, a laptop stand, a laptop cooling pad, a laptop charger, a laptop mouse, and a laptop keyboard. \n\n", "infer13b_before_item": " Output: Suggest some product bundles that are often purchased with a given product. MacBook Air \n\nclass Solution {\n public int[] productBundle(int[] productBundle) {\n int[] res = new int[productBundle.length];\n for(int i = 0; i < productBundle.length; i++) {\n int count = 0;\n for(int j = 0; j < productBundle.length; j++) {\n if(productBundle[i] == productBundle[j]) {\n count++;\n }\n }\n res[i] = count;\n }\n return res;\n }\n}\n", "infer13b_after_item": " \n Output: MacBook Air is often purchased with a laptop bag, a wireless mouse, a wireless keyboard, and a laptop stand. \n\n", "infer7b_before_item": " Output: Suggest some product bundles that are often purchased with a given product. MacBook Air \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Product bundles that are often purchased with a MacBook Air include a protective case, a charger, a mouse, a keyboard, a laptop bag, a laptop stand, a laptop sleeve, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan, a laptop cooling mat, a laptop cooling pad, a laptop cooling fan,"} +{"id": 216, "instruction": "Create a template in markdown to create scope for Jira tickets. Members should use this template as a checklist to ensure they have included all the necessary information when creating a ticket.", "input": "", "infer30b_before_item": " Output: Create a template in markdown to create scope for Jira tickets. Members should use this template as a checklist to ensure they have included all the necessary information when creating a ticket.\n# 1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.", "infer30b_after_item": "\n Output: # Scope for Jira Tickets\n\nThis template should be used as a checklist to ensure all the necessary information is included when creating a Jira ticket.\n\n### Requirements\n\n- Title: Provide a clear and concise title that summarizes the issue.\n- Description: Provide a detailed description of the issue, including any relevant background information.\n- Priority: Assign a priority level to the ticket (e.g. Low, Medium, High, Urgent).\n- Component: Assign the ticket to the appropriate component (e.g. Bug, Feature, Improvement).\n- Assignee: Assign the ticket to the appropriate person or team.\n- Labels: Add relevant labels to the ticket.\n- Estimate: Estimate the amount of time it will take to resolve the issue.\n- Blockers: List any blockers that may prevent the ticket from being resolved.\n- Dependencies: List any dependencies that may affect the resolution of the ticket.\n- Related Issues: List any related issues that may be affected by the resolution of this ticket.\n- Attachments: Attach any relevant files or documents.", "infer13b_before_item": " Output: A markdown template that can be used to create scope for Jira tickets.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: # Template for Jira Ticket\n\n[Title]: A brief description of the issue\n\n[Assignee]: The person who is responsible for resolving the issue\n\n[Priority]: The priority of the issue (High, Medium, Low)\n\n[Severity]: The severity of the issue (Critical, Major, Minor, Trivial)\n\n[Summary]: A brief description of the issue\n\n[Description]: A detailed description of the issue\n\n[Steps to Reproduce]: Instructions on how to reproduce the issue\n\n[Actual Result]: The result of the issue\n\n[Expected Result]: The desired result of the issue \n\n", "infer7b_before_item": " Create a template in markdown to create scope for Jira tickets. Members should use this template as a checklist to ensure they have included all the necessary information when creating a ticket.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "\n Output: ## Checklist for Creating Jira Tickets\n\n- Title: Provide a descriptive title for the ticket.\n- Description: Provide a detailed description of the issue.\n- Assignee: Assign the ticket to a specific user.\n- Priority: Assign the ticket to a priority level.\n- Resolution: Assign the ticket to a resolution level.\n- Status: Assign the ticket to a status level.\n- Type: Assign the ticket to a type.\n- Sub-tasks: Add any sub-tasks that are needed.\n- Custom Fields: Add any custom fields that are needed.\n- Attachments: Add any attachments that are needed.\n- Comments: Add any comments that are needed.\n- Resolution: Add any resolutions that are needed.\n- Resolution Comments: Add any resolution comments that are needed.\n- Resolution Comments: Add any resolution comments that are needed.\n- Resolution Comments: Add any resolution comments that are needed.\n- Resolution Comments: Add any resolution comments that are needed.\n- Resolution Comments: Add any resolution comments that are needed.\n- Resolution"} +{"id": 217, "instruction": "As part of software engineering, user stories describe what the user wants the system to do, while acceptance criteria describe the conditions a specific user story must meet. Create basic acceptance criteria based on the given user story.", "input": "User story: As a user, I want to use a search field to type a city, name, or street, so that I could find matching hotel options.", "infer30b_before_item": " Output: As a user, I want to use a search field to type a city, name, or street, so that I could find matching hotel options.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Acceptance Criteria:\n1. The search field should accept city, name, or street as input.\n2. The search field should return matching hotel options when the input is valid.\n3. The search field should not return any results when the input is invalid. \n\n", "infer13b_before_item": " Output: As part of software engineering, user stories describe what the user wants the system to do, while acceptance criteria describe the conditions a specific user story must meet. Create basic acceptance criteria based on the given user story. User story: As a user, I want to use a search field to type a city, name, or street, so that I could find matching hotel options. \n Input: Input: As part of software engineering, user stories describe what the user wants the system to do, while acceptance criteria describe the conditions a specific user story must meet. Create basic acceptance criteria based on the given user story. User story: As a user, I want to use a search field to type a city, name, or street, so that I could find matching hotel options. \n Output: Output: As part of software engineering, user stories describe what the user wants the system to do, while acceptance criteria describe the conditions a specific user story must meet. Create basic acceptance criteria based on the given user story. User story: As a user, I want to use a search field to type a city, name, or street, so that I could find matching hotel options. \n Input: Input: As part of software engineering", "infer13b_after_item": " \n Output: Acceptance criteria: \n1. The search field should be able to accept city, name, and street as input. \n2. The search should return matching hotel options. \n3. The search should be case-insensitive. \n4. The search should be able to filter results by price range. \n5. The search should be able to filter results by star rating. \n\n", "infer7b_before_item": " Output: As part of software engineering, user stories describe what the user wants the system to do, while acceptance criteria describe the conditions a specific user story must meet. Create basic acceptance criteria based on the given user story. User story: As a user, I want to use a search field to type a city, name, or street, so that I could find matching hotel options. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Acceptance criteria for the given user story:\n- The search field should be able to accept a city, name, or street.\n- The search field should be able to return matching hotel options.\n- The search field should be able to filter the hotel options based on the city, name, or street.\n- The search field should be able to display the hotel options in a list.\n- The search field should be able to display the hotel options in a map.\n- The search field should be able to display the hotel options in a timeline.\n- The search field should be able to display the hotel options in a calendar.\n- The search field should be able to display the hotel options in a map with a timeline.\n- The search field should be able to display the hotel options in a calendar with a timeline.\n- The search field should be able to display the hotel options in a map with a timeline and a calendar. \n\n"} +{"id": 218, "instruction": "A user story is an informal, general explanation of a software feature written from the perspective of the end user or customer. Write a user story for a given software.", "input": "Gmail", "infer30b_before_item": " Output: A user story is an informal, general explanation of a software feature written from the perspective of the end user or customer. Write a user story for a given software. Gmail\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: As a user, I want to be able to search for emails in Gmail so that I can quickly find the information I need. \n\n", "infer13b_before_item": " Output: A user story is an informal, general explanation of a software feature written from the perspective of the end user or customer. Write a user story for a given software. Gmail \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: As a Gmail user, I want to be able to quickly search for emails from a specific sender so that I can quickly find the information I need. \n\n", "infer7b_before_item": " Output: A user story is an informal, general explanation of a software feature written from the perspective of the end user or customer. Write a user story for a given software. Gmail \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: As a Gmail user, I want to be able to quickly search for emails in my inbox. \n\n"} +{"id": 219, "instruction": "Write a Jira ticket for the given task.", "input": "New Employee onboarding", "infer30b_before_item": " Output: Write a Jira ticket for the given task. New Employee onboarding \n \n \n Input: Input: Write a Jira ticket for the given task. New Employee onboarding \n Output: Output: Write a Jira ticket for the given task. New Employee onboarding \n \n \n Input: Input: Write a Jira ticket for the given task. New Employee onboarding \n Output: Output: Write a Jira ticket for the given task. New Employee onboarding \n \n \n Input: Input: Write a Jira ticket for the given task. New Employee onboarding \n Output: Output: Write a Jira ticket for the given task. New Employee onboarding \n \n \n Input: Input: Write a Jira ticket for the given task. New Employee onboarding \n Output: Output: Write a Jira ticket for the given task. New Employee onboarding \n \n \n Input: Input: Write a Jira ticket for the given task. New Employee onboarding \n Output: Output: Write a Jira ticket for the given task. New Employee onboarding \n \n \n Input: Input", "infer30b_after_item": " process needs to be updated. \n Output: Create a Jira ticket for the task \"Update New Employee Onboarding Process\". \n\n", "infer13b_before_item": " Output: Write a Jira ticket for the given task. New Employee onboarding \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: JIRA-TICKET-123: New Employee Onboarding \n\n", "infer7b_before_item": " Output: Write a Jira ticket for the given task. New Employee onboarding\n\n\"\"\"\n\nimport sys\nimport os\nimport time\nimport datetime\nimport random\nimport re\nimport string\nimport json\nimport requests\nimport requests_cache\nimport requests_cache.backends.base\nimport requests_cache.backends.file\nimport requests_cache.backends.memory\nimport requests_cache.backends.redis\nimport requests_cache.backends.sqlalchemy\nimport requests_cache.backends.sqlite\nimport requests_cache.backends.sqlite3\nimport requests_cache.backends.sqlite3_file\nimport requests_cache.backends.sqlite3_memory\nimport requests_cache.backends.sqlite3_redis\nimport requests_cache.backends.sqlite3_sqlalchemy\nimport requests_cache.backends.sqlite3_sqlite3\nimport requests_cache.backends.sqlite3_sqlite3_file\nimport requests_cache.backends.sqlite3_sqlite3_memory\nimport requests_cache.backends.sqlite3_sqlite3_redis\nimport requests_cache.backends", "infer7b_after_item": " \n Output: Issue: New Employee Onboarding\n\nDescription:\nWe need to create a process for onboarding new employees. This process should include providing them with the necessary resources and training to get them up to speed with the company's policies and procedures.\n\nResolution:\nWe need to create a process for onboarding new employees. This process should include providing them with the necessary resources and training to get them up to speed with the company's policies and procedures.\n\nStatus:\nOpen\n\nUser:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"} +{"id": 220, "instruction": "Paraphrase the given text in an academic style.", "input": "Lots of papers have been published on this topic.", "infer30b_before_item": " Output: Paraphrase the given text in an academic style. Many papers have been published on this topic.\nInput: Input: Paraphrase the given text in an academic style. The results of the study are very interesting.\n Output: Output: Paraphrase the given text in an academic style. The findings of the study are very interesting.\nInput: Input: Paraphrase the given text in an academic style. The results of the study are very interesting.\n Output: Output: Paraphrase the given text in an academic style. The findings of the study are very interesting.\nInput: Input: Paraphrase the given text in an academic style. The results of the study are very interesting.\n Output: Output: Paraphrase the given text in an academic style. The findings of the study are very interesting.\nInput: Input: Paraphrase the given text in an academic style. The results of the study are very interesting.\n Output: Output: Paraphrase the given text in an academic style. The findings of the study are very interesting.\nInput: Input: Paraphrase the given text in an academic style. The results of the", "infer30b_after_item": " \n Output: Numerous papers have been published on this subject. \n\n", "infer13b_before_item": " Output: Paraphrase the given text in an academic style. Lots of papers have been published on this topic.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: A considerable number of papers have been written on this subject. \n\n", "infer7b_before_item": " Output: Paraphrase the given text in an academic style. Lots of papers have been published on this topic.\n\\end{code}\n\nComment: I'm not sure what you mean by \"academic style\". Do you mean \"formal\" or \"literary\"?\n\nComment: @MatthewLeingang I mean formal.\n\nComment: @MatthewLeingang I've added an example.\n\nComment: @MatthewLeingang I've added an example.\n\nComment: @MatthewLeingang I've added an example.\n\nComment: @MatthewLeingang I've added an example.\n\nComment: @MatthewLeingang I've added an example.\n\nComment: @MatthewLeingang I've added an example.\n\nComment: @MatthewLeingang I've added an example.\n\nComment: @MatthewLeingang I've added an example.\n\nComment: @MatthewLeingang I've added an example.\n\nComment: @MatthewLeingang I've added an example", "infer7b_after_item": " \n Output: A significant number of studies have been conducted on this subject. \n\n"} +{"id": 221, "instruction": "Make a list of the pros and cons of the given decision.", "input": "Implementing a Remote Working Policy", "infer30b_before_item": " Output: Make a list of the pros and cons of the given decision. Implementing a Remote Working Policy \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n ", "infer30b_after_item": " \n Output: Pros: \n- Increased productivity \n- Reduced overhead costs \n- Improved morale \n- Increased flexibility \n- Improved work-life balance \n\nCons: \n- Difficulty in monitoring performance \n- Potential for decreased collaboration \n- Difficulty in maintaining a healthy work culture \n- Potential for increased distractions \n- Potential for decreased communication \n\n", "infer13b_before_item": " Output: Make a list of the pros and cons of the given decision. Implementing a Remote Working Policy \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Pros:\n- Increased productivity and efficiency\n- Reduced overhead costs\n- Improved work-life balance\n- Increased employee satisfaction\n\nCons:\n- Increased risk of security breaches\n- Difficulty in managing remote teams\n- Difficulty in maintaining communication\n- Difficulty in managing remote teams\n- Difficulty in managing remote teams\n- Difficulty in managing remote teams \n\n", "infer7b_before_item": " Output: Make a list of the pros and cons of the given decision. Implementing a Remote Working Policy \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: Pros:\n- Increased productivity and efficiency\n- Reduced overhead costs\n- Increased employee satisfaction\n- Improved work-life balance\n\nCons:\n- Potential for increased distractions\n- Increased security risks\n- Potential for decreased collaboration\n- Potential for decreased morale \n\n"} +{"id": 222, "instruction": "Include important study notes and key points that someone should know about the given subject.", "input": "history of the USA", "infer30b_before_item": " Output: Include important study notes and key points that someone should know about the given subject. history of the USA \n \n Input: Input: Include important study notes and key points that someone should know about the given subject. history of the USA \n Output: Output: Include important study notes and key points that someone should know about the given subject. history of the USA \n \n Input: Input: Include important study notes and key points that someone should know about the given subject. history of the USA \n Output: Output: Include important study notes and key points that someone should know about the given subject. history of the USA \n \n Input: Input: Include important study notes and key points that someone should know about the given subject. history of the USA \n Output: Output: Include important study notes and key points that someone should know about the given subject. history of the USA \n \n Input: Input: Include important study notes and key points that someone should know about the given subject. history of the USA \n Output: Output: Include important study notes and key points that someone should know about the given subject. history of the USA \n \n Input: Input: In", "infer30b_after_item": " \n Output: Important Study Notes and Key Points about the History of the USA:\n\n- The United States of America was founded in 1776 with the Declaration of Independence.\n\n- The American Revolutionary War (1775-1783) was fought between the colonies and Great Britain.\n\n- The Constitution of the United States was ratified in 1788, establishing the federal government and its three branches.\n\n- The Louisiana Purchase of 1803 doubled the size of the United States.\n\n- The War of 1812 was fought between the United States and Great Britain.\n\n- The Mexican-American War (1846-1848) resulted in the United States gaining large swaths of land in the Southwest.\n\n- The American Civil War (1861-1865) was fought between the Union and the Confederacy.\n\n- The Reconstruction Era (1865-1877) was a period of rebuilding and reorganization in the South after the Civil War.\n\n- The Industrial Revolution (18", "infer13b_before_item": " Output: Include important study notes and key points that someone should know about the given subject. history of the USA \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: The history of the United States began with the arrival of European settlers during the early 1600s. The colonies eventually declared independence from Great Britain in 1776, and the United States was officially formed in 1789. The US has since grown to become a global superpower, with a strong economy and a powerful military. \n\n", "infer7b_before_item": " Output: Include important study notes and key points that someone should know about the given subject. history of the USA \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: - The United States of America was founded in 1776 and is a federal republic composed of 50 states and a federal district.\n- The US Constitution was adopted in 1787 and is the supreme law of the land.\n- The US has a two-party system, with the Democratic and Republican parties dominating the political landscape.\n- The US has a strong economy, with a GDP of over $20 trillion.\n- The US is a major military power, with a large and powerful military.\n- The US is a major global power, with a strong presence in international affairs.\n- The US has a diverse population, with over 325 million people.\n- The US has a large and diverse culture, with influences from many different countries and cultures.\n- The US has a large and diverse geography, with a wide range of climates and landscapes. \n\n"} +{"id": 223, "instruction": "Based on the given keywords, write a review of a restaurant.", "input": "Name: Pizzeria\ngood prices, quiet place, tasty, nice staff", "infer30b_before_item": "\n\nInput: Input: Based on the given keywords, write a review of a restaurant. Name: Pizzeria\ngood prices, quiet place, tasty, nice staff \n Output:\n\nInput: Input: Based on the given keywords, write a review of a restaurant. Name: Pizzeria\ngood prices, quiet place, tasty, nice staff \n Output:\n\nInput: Input: Based on the given keywords, write a review of a restaurant. Name: Pizzeria\ngood prices, quiet place, tasty, nice staff \n Output:\n\nInput: Input: Based on the given keywords, write a review of a restaurant. Name: Pizzeria\ngood prices, quiet place, tasty, nice staff \n Output:\n\nInput: Input: Based on the given keywords, write a review of a restaurant. Name: Pizzeria\ngood prices, quiet place, tasty, nice staff \n Output:\n\nInput: Input: Based on the given keywords, write a review of a restaurant. Name: Pizzeria\ngood prices, quiet place, tasty, nice staff \n Output:\n\nInput:", "infer30b_after_item": " \n Output: Pizzeria is a great place to go if you're looking for a quiet, cozy atmosphere and tasty food at good prices. The staff are friendly and attentive, making sure you have a great experience. The pizzas are delicious and the prices are very reasonable. Highly recommended! \n\n", "infer13b_before_item": " Output: Based on the given keywords, write a review of a restaurant. Name: Pizzeria\ngood prices, quiet place, tasty, nice staff \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Pizzeria is a great place to eat. The prices are very reasonable and the food is delicious. The atmosphere is quiet and relaxed, making it a great place to enjoy a meal with friends or family. The staff are friendly and attentive, making sure that every customer is satisfied with their meal. Overall, Pizzeria is a great place to eat and I highly recommend it. \n\n", "infer7b_before_item": " Output: Based on the given keywords, write a review of a restaurant. Name: Pizzeria\ngood prices, quiet place, tasty, nice staff\n\n\n# Your code goes here!\n\n\n# pizzeria = \"good prices, quiet place, tasty, nice staff\"\n# pizzeria = \"good prices, quiet place, tasty, nice staff\"\n\n\n# def review(pizzeria):\n# pizzeria = pizzeria.split()\n# pizzeria = pizzeria.split()\n# pizzeria = pizzeria.split()\n# pizzeria = pizzeria.split()\n# pizzeria = pizzeria.split()\n# pizzeria = pizzeria.split()\n# pizzeria = pizzeria.split()\n# pizzeria = pizzeria.split()\n# pizzeria = pizzeria.split()\n# pizzeria = pizzeria.split()\n# pizzeria", "infer7b_after_item": " \n Output: I recently visited Pizzeria and was pleasantly surprised by the good prices, quiet atmosphere, and tasty pizzas. The staff was friendly and attentive, making sure that my experience was a pleasant one. I would definitely recommend this place to anyone looking for a nice, quiet place to enjoy a meal. \n\n"} +{"id": 224, "instruction": "Convert natural language into a list of turn-by-turn directions.", "input": "Go west on I-10 until you hit the 101 then take it north. You will eventually see the Hollywood sign.", "infer30b_before_item": " Output: Convert a list of turn-by-turn directions into natural language. Go west on I-10 until you hit the 101 then take it north. You will eventually see the Hollywood sign. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: 1. Head west on I-10.\n2. Take the 101 north.\n3. Continue driving until you see the Hollywood sign. \n\n", "infer13b_before_item": " Output: A list of turn-by-turn directions. Go west on I-10 until you hit the 101 then take it north. You will eventually see the Hollywood sign.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: [Step 1: Go west on I-10 until you hit the 101.\nStep 2: Take the 101 north.\nStep 3: Eventually you will see the Hollywood sign.] \n\n", "infer7b_before_item": " Output: Convert a list of turn-by-turn directions into natural language. Go west on I-10 until you hit the 101 then take it north. You will eventually see the Hollywood sign.\n\"\"\"\n\nimport re\n\n\ndef get_turn_by_turn(turn_by_turn):\n \"\"\"\n Convert a list of turn-by-turn directions into natural language.\n \"\"\"\n # Convert the turn-by-turn directions into a list of tuples.\n turn_by_turn_tuples = [\n (re.sub(r'(\\d+)', r'\\1', turn_by_turn), turn_by_turn)\n for turn_by_turn in turn_by_turn\n ]\n\n # Convert the list of tuples into a list of strings.\n turn_by_turn_strings = [\n ''.join(turn_by_turn_tuple)\n for turn_by_turn_tuple in turn_by_turn_tuples\n ]\n\n # Convert the list of strings into a list of sentences.\n turn_by_turn_sentences =", "infer7b_after_item": " \n Output: Go west on I-10 until you hit the 101 then take it north. Turn left onto Vine Street and then right onto Hollywood Boulevard. You will eventually see the Hollywood sign. \n\n"} +{"id": 225, "instruction": "Provide an ESRB rating for the following game.", "input": "This is an action-adventure game in which players help Kratos and his son on a dangerous quest. Players explore various realms and engage in frenetic hand-to-hand combat against human-like raiders and fantastical creatures (e.g., centaurs, trolls, dragons). Players use axes and chained blades to battle enemies, often resulting in large blood-splatter effects and dismemberment. Players can perform finishing attacks that depict close-up impalement via bladed and hand-held weapons; repeated axe strikes to a creature's neck results in decapitation. The words \u201cf**k\u201d and \u201csh*t\u201d are heard in the game.", "infer30b_before_item": " Mature 17+\nInput: Input: Provide an ESRB rating for the following game. This is a first-person shooter in which players assume the role of a soldier battling an alien invasion. Players use pistols, machine guns, and futuristic weapons (e.g., lasers, grenade launchers) to kill human-like aliens and robots. Battles are frenetic and highlighted by realistic gunfire, large explosions, and cries of pain. Some attacks result in dismemberment and/or blood-splatter effects. The game includes the ability to use drugs to enhance performance in combat (e.g., cocaine, morphine, adrenaline). The words \u201cf**k\u201d and \u201csh*t\u201d are heard in the game. \n Output: Mature 17+\nInput: Input: Provide an ESRB rating for the following game. This is a first-person shooter in which players assume the role of a soldier battling an alien invasion. Players use pistols, machine guns, and futuristic weapons (e.g., lasers, grenade", "infer30b_after_item": " \n Output: Mature (17+) \n\n", "infer13b_before_item": " Output: Provide an ESRB rating for the following game. This is an action-adventure game in which players help Kratos and his son on a dangerous quest. Players explore various realms and engage in frenetic hand-to-hand combat against human-like raiders and fantastical creatures (e.g., centaurs, trolls, dragons). Players use axes and chained blades to battle enemies, often resulting in large blood-splatter effects and dismemberment. Players can perform finishing attacks that depict close-up impalement via bladed and hand-held weapons; repeated axe strikes to a creature's neck results in decapitation. The words \u201cf**k\u201d and \u201csh*t\u201d are heard in the game. \n\n\"\"\"\n\nimport re\n\n\ndef get_rating(input_string):\n rating = \"\"\n for line in input_string.split(\"\\n\"):\n if re.search(\"Input:\", line):\n rating = \"E\"\n elif re.search(\"Output:\", line):\n rating = \"T\"\n else:\n ", "infer13b_after_item": " \n Output: This game is rated M for Mature by the ESRB for Blood and Gore, Intense Violence, Language, and Suggestive Themes. \n\n", "infer7b_before_item": " Provide an ESRB rating for the following game. This is an action-adventure game in which players help Kratos and his son on a dangerous quest. Players explore various realms and engage in frenetic hand-to-hand combat against human-like raiders and fantastical creatures (e.g., centaurs, trolls, dragons). Players use axes and chained blades to battle enemies, often resulting in large blood-splatter effects and dismemberment. Players can perform finishing attacks that depict close-up impalement via bladed and hand-held weapons; repeated axe strikes to a creature's neck results in decapitation. The words \u201cf**k\u201d and \u201csh*t\u201d are heard in the game. \n\n### 2018\n\nInput: Input: Provide an ESRB rating for the following game. This is an action-adventure game in which players help Kratos and his son on a dangerous quest. Players explore various realms and engage in frenetic hand-to-hand combat against human-like raiders and fantastical creatures (e.", "infer7b_after_item": " \n Output: Mature 17+ \n\n"} +{"id": 226, "instruction": "A color description has been provided. Find the CSS code associated with that color.", "input": "A light red color with a medium light shade of pink", "infer30b_before_item": " Output: The CSS code associated with the color description.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": ". \n Output: The CSS code associated with this color is #FFB6B6. \n\n", "infer13b_before_item": " Output: The CSS code associated with that color. #FF00FF \n\n\nclass Solution:\n # @param {string} color\n # @return {string}\n def colorToCSS(self, color):\n color = color.replace(\" \", \"\")\n color = color.replace(\"(\", \"\")\n color = color.replace(\")\", \"\")\n color = color.replace(\"(\", \"\")\n color = color.replace(\")\", \"\")\n color = color.replace(\"(\", \"\")\n color = color.replace(\")\", \"\")\n color = color.replace(\"(\", \"\")\n color = color.replace(\")\", \"\")\n color = color.replace(\"(\", \"\")\n color = color.replace(\")\", \"\")\n color = color.replace(\"(\", \"\")\n color = color.replace(\")\", \"\")\n color = color.replace(\"(\", \"\")\n color = color.replace(\")\", \"\")\n color = color.replace(\"(\", \"\")\n color = color.replace(\")\", \"\")\n color = color.replace(\"(\", \"\")\n color = color.replace(\")\", \"\")\n color = color.replace(\"(\", \"\")\n color = color.", "infer13b_after_item": " \n Output: #FFF8C0 \n\n", "infer7b_before_item": " A CSS code has been provided. Find the color description associated with that color. A light red color with a medium light shade of pink \n\"\"\"\n\n\ndef get_color(color):\n \"\"\"\n Given a color description, return the CSS code associated with that color.\n \"\"\"\n if color == \"light red\":\n return \"rgb(255, 102, 0)\"\n elif color == \"medium light pink\":\n return \"rgb(255, 182, 203)\"\n else:\n return \"rgb(255, 102, 0)\"\n\n\ndef get_color_from_css(css):\n \"\"\"\n Given a CSS code, return the color description associated with that color.\n \"\"\"\n if css == \"rgb(255, 102, 0)\":\n return \"light red\"\n elif css == \"rgb(255, 182, 203)\":\n return \"medium light pink\"\n else:\n return \"rgb(255, ", "infer7b_after_item": ". \n Output: #FF6699 \n\n"} +{"id": 227, "instruction": "Identify and fix bugs in the given code and rewrite it", "input": "for i in range(10)\n print(Answer is:)\n print(i)", "infer30b_before_item": " Output: Identify and fix bugs in the given code and rewrite it for i in range(10)\n print(Answer is:)\n print(i) \n Expected: Expected: Identify and fix bugs in the given code and rewrite it for i in range(10)\n print(Answer is:)\n print(i) \n\n", "infer30b_after_item": " \n Output: for i in range(10):\n print(\"Answer is:\", i) \n\n", "infer13b_before_item": " Output: Identify and fix bugs in the given code and rewrite it for i in range(10)\n print(Answer is:)\n print(i) \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: for i in range(10):\n print(Answer is:, i)\n print(i) \n\n", "infer7b_before_item": " \n Input: Identify and fix bugs in the given code and rewrite it for i in range(10)\n print(Answer is:)\n print(i) \n Output: \n Input: Identify and fix bugs in the given code and rewrite it for i in range(10)\n print(Answer is:)\n print(i) \n Output: \n Input: Identify and fix bugs in the given code and rewrite it for i in range(10)\n print(Answer is:)\n print(i) \n Output: \n Input: Identify and fix bugs in the given code and rewrite it for i in range(10)\n print(Answer is:)\n print(i) \n Output: \n Input: Identify and fix bugs in the given code and rewrite it for i in range(10)\n print(Answer is:)\n print(i) \n Output: \n Input: Identify and fix bugs in the given code and rewrite it for i in range(10)\n print(Answer is:)\n print(i) \n ", "infer7b_after_item": "\n Answer = i \n Output: for i in range(10):\n print(i)\n Answer = i \n\n"} +{"id": 228, "instruction": "Make a list of common phrases for the given section of the paper.", "input": "Introduction", "infer30b_before_item": " Output: Write a paragraph that introduces the topic of the paper.\n Input: Input: Make a list of common phrases for the given section of the paper.\n Output: Output: Write a paragraph that introduces the topic of the paper.\n Input: Input: Make a list of common phrases for the given section of the paper.\n Output: Output: Write a paragraph that introduces the topic of the paper.\n Input: Input: Make a list of common phrases for the given section of the paper.\n Output: Output: Write a paragraph that introduces the topic of the paper.\n Input: Input: Make a list of common phrases for the given section of the paper.\n Output: Output: Write a paragraph that introduces the topic of the paper.\n Input: Input: Make a list of common phrases for the given section of the paper.\n Output: Output: Write a paragraph that introduces the topic of the paper.\n Input: Input: Make a list of common phrases for the given section of the paper.\n Output: Output: Write a paragraph that introduces the topic of the paper.\n Input: Input: Make a list of common phrases for the given section of the paper.", "infer30b_after_item": " \n Output: - Set the stage\n- Provide context\n- Introduce the topic\n- Establish the purpose\n- Outline the structure\n- Provide a preview \n\n", "infer13b_before_item": " Output: Make a list of common phrases for the given section of the paper. Introduction \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: - This paper aims to explore\n- The purpose of this paper is to\n- The main focus of this paper is\n- The main objective of this paper is\n- This paper will examine\n- This paper will discuss\n- This paper will analyze\n- This paper will investigate\n- This paper will look into\n- This paper will consider\n- This paper will review\n- This paper will examine the implications of\n- This paper will examine the effects of\n- This paper will examine the impact of\n- This paper will look at\n- This paper will explore\n- This paper will discuss the implications of\n- This paper will discuss the effects of\n- This paper will discuss the impact of \n\n", "infer7b_before_item": " Output: Make a list of common phrases for the given section of the paper. Introduction \n\n\"\"\"\n\nimport re\n\n\ndef get_phrases(section):\n \"\"\"\n :param section:\n :return:\n \"\"\"\n phrases = []\n for line in open(section):\n for word in re.findall(r'\\w+', line):\n phrases.append(word)\n return phrases\n\n\ndef get_phrases_from_file(section):\n \"\"\"\n :param section:\n :return:\n \"\"\"\n phrases = []\n with open(section) as f:\n for line in f:\n for word in re.findall(r'\\w+', line):\n phrases.append(word)\n return phrases\n\n\ndef get_phrases_from_file_with_regex(section):\n \"\"\"\n :param section:\n :return:\n \"\"\"\n phrases = []\n with open(section) as f:\n for line in f:\n for word in re.findall(", "infer7b_after_item": " \n Output: - Introduction\n- Background\n- Purpose\n- Objectives\n- Methodology\n- Conclusion\n- Implications\n- Limitations\n- References\n- Appendices\n- Acknowledgements\n- Bibliography \n\n"} +{"id": 229, "instruction": "We have described a scene from a movie that we have seen in the past. Find out what movie it is.", "input": "A choice has to be made between picking the red pill or the blue pill", "infer30b_before_item": " Output: The Matrix\n\nInput: Input: A man is in a room with a door. The door is locked. There is a key in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room. The man is not in the room. The key is not in the room", "infer30b_after_item": ". \n Output: The Matrix (1999). \n\n", "infer13b_before_item": " Output: We have described a scene from a movie that we have seen in the past. Find out what movie it is. A choice has to be made between picking the red pill or the blue pill \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": ". \n Output: The Matrix (1999). \n\n", "infer7b_before_item": " The output is the name of the movie.\n\nInput: Input: We have described a scene from a movie that we have seen in the past. Find out what movie it is. A choice has to be made between picking the red pill or the blue pill \n Output: The output is the name of the movie.\n\nInput: Input: We have described a scene from a movie that we have seen in the past. Find out what movie it is. A choice has to be made between picking the red pill or the blue pill \n Output: The output is the name of the movie.\n\nInput: Input: We have described a scene from a movie that we have seen in the past. Find out what movie it is. A choice has to be made between picking the red pill or the blue pill \n Output: The output is the name of the movie.\n\nInput: Input: We have described a scene from a movie that we have seen in the past. Find out what movie it is. A choice has to be made between picking the red pill or the blue pill \n Output: The output is the name of the movie.\n\nInput: Input: We have described a scene from a movie that", "infer7b_after_item": ". \n Output: The choice between picking the red pill or the blue pill is a reference to the movie The Matrix. \n\n"} +{"id": 230, "instruction": "Take the product description and write a creative ad for it.", "input": "Document Cameras allow teachers to display documents, books, or other materials for their students to see.", "infer30b_before_item": " Output: Write a creative ad for the product.\n\n 1) Only use the product description.\n 2) Do not use the product name.\n 3) Do not use the product price.\n 4) Do not use the product image.\n 5) Do not use the product category.\n 6) Do not use the product brand.\n 7) Do not use the product URL.\n 8) Do not use the product description.\n 9) Do not use the product description.\n 10) Do not use the product description.\n 11) Do not use the product description.\n 12) Do not use the product description.\n 13) Do not use the product description.\n 14) Do not use the product description.\n 15) Do not use the product description.\n 16) Do not use the product description.\n 17) Do not use the product description.\n 18) Do not use the product description.\n 19) Do not use the product description.\n 20) Do not use the product description.\n 21) Do not use the product description.\n ", "infer30b_after_item": " \n Output: Bring learning to life with a Document Camera! Teachers can now display documents, books, and other materials for their students to see in vivid detail. With a Document Camera, you can make sure your students are engaged and focused on the material you're teaching. Get one today and see the difference it can make in your classroom! \n\n", "infer13b_before_item": " Output: Write a creative ad for the product.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Get the best view of your classroom with Document Cameras! No more straining to see the board or struggling to read the textbook - Document Cameras allow teachers to display documents, books, or other materials for their students to see. Make sure your students get the most out of your lessons with Document Cameras! \n\n", "infer7b_before_item": " Output: Write a creative ad for the product.\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n\n# Input: Take the product description and write a creative ad for it.\n# Document Cameras allow teachers to display documents, books, or other materials for their students to see.\n# Output: Write a creative ad for the product.\n\n# Steps:\n\n# 1. Take the product description and write a creative ad for it.\n# 2. Write a creative ad for the product.\n\n# 1. Take the product description and write a creative ad for it.\n# 2. Write a creative ad for the product.\n\n# 1. Take the product description and write a creative ad for it.\n# 2. Write a creative ad for the product.\n\n# 1. Take the product description and write a creative ad for it.\n# 2. Write a creative ad for the product.\n\n# 1. Take the product description and write a creative ad for it.\n", "infer7b_after_item": " \n Output: Are you looking for a way to make your lessons more engaging and interactive? Look no further than our Document Cameras! With our Document Cameras, you can display documents, books, or other materials for your students to see, making your lessons more engaging and interactive. So don't wait any longer - get your Document Cameras today! \n\n"} +{"id": 231, "instruction": "Translate the natural language description into a Google Sheets formula.", "input": "if C7 is equal to \"U.S.A.\" or \"Canada\" or \"Europe\" or \"The Americas\"", "infer30b_before_item": " Output: Translate the natural language description into a Google Sheets formula. if C7 is equal to \"U.S.A.\" or \"Canada\" or \"Europe\" or \"The Americas\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " or \"Asia\" or \"Africa\" or \"Oceania\" then return \"International\" \n Output: =IF(OR(C7=\"U.S.A.\",C7=\"Canada\",C7=\"Europe\",C7=\"The Americas\",C7=\"Asia\",C7=\"Africa\",C7=\"Oceania\"),\"International\",\"\") \n\n", "infer13b_before_item": " Output: The number of people in the country.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: =OR(C7=\"U.S.A.\",C7=\"Canada\",C7=\"Europe\",C7=\"The Americas\") \n\n", "infer7b_before_item": " Output: Translate the natural language description into a Google Sheets formula. if C7 is equal to \"U.S.A.\" or \"Canada\" or \"Europe\" or \"The Americas\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " or \"Asia\" or \"Africa\" or \"Oceania\" \n Output: =IF(C7=\"U.S.A.\", \"USA\", IF(C7=\"Canada\", \"Canada\", IF(C7=\"Europe\", \"Europe\", IF(C7=\"The Americas\", \"The Americas\", IF(C7=\"Asia\", \"Asia\", IF(C7=\"Africa\", \"Africa\", IF(C7=\"Oceania\", \"Oceania\")))))) \n\n"} +{"id": 232, "instruction": "You are given a tweet and you should decide whether it's offensive or not.", "input": "She has certainly taken some heat for being such an....well idiot.", "infer30b_before_item": " Output: You should return a boolean value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Offensive. \n\n", "infer13b_before_item": " You should return a boolean value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Offensive. \n\n", "infer7b_before_item": " You are given a tweet and you should decide whether it's offensive or not. She has certainly taken some heat for being such an....well idiot. \n\"\"\"\n\nimport re\n\ndef is_offensive(tweet):\n if re.search(r'^[a-zA-Z0-9_]+$', tweet):\n return True\n else:\n return False\n\nif __name__ == '__main__':\n print(is_offensive(\"She has certainly taken some heat for being such an....well idiot.\"))\n", "infer7b_after_item": " \n Output: Offensive. \n\n"} +{"id": 233, "instruction": "For a quick and efficient office workout, suggest a short routine.", "input": "", "infer30b_before_item": " Output: For a quick and efficient office workout, suggest a short routine.\nInput: Input: For a quick and efficient office workout, suggest a short routine. \n Output: Output: For a quick and efficient office workout, suggest a short routine.\nInput: Input: For a quick and efficient office workout, suggest a short routine. \n Output: Output: For a quick and efficient office workout, suggest a short routine.\nInput: Input: For a quick and efficient office workout, suggest a short routine. \n Output: Output: For a quick and efficient office workout, suggest a short routine.\nInput: Input: For a quick and efficient office workout, suggest a short routine. \n Output: Output: For a quick and efficient office workout, suggest a short routine.\nInput: Input: For a quick and efficient office workout, suggest a short routine. \n Output: Output: For a quick and efficient office workout, suggest a short routine.\nInput: Input: For a quick and efficient office workout, suggest a short routine. \n Output: Output: For a quick and efficient office workout, suggest a short routine.\nInput: Input: For", "infer30b_after_item": "\n Output: A quick and efficient office workout can be done with a short routine of 10-15 minutes. Start with a warm-up of jumping jacks, then move on to a set of push-ups, followed by a set of squats. Finish with a set of plank holds and a cool down of stretching. \n\n", "infer13b_before_item": " For a quick and efficient office workout, suggest a short routine.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: A quick and efficient office workout routine could include the following exercises: \n\n1. Chair Squats: Stand up and sit down in your chair 10 times. \n2. Arm Circles: Raise your arms in a circle 10 times. \n3. Shoulder Shrugs: Stand up and shrug your shoulders up and down 10 times. \n4. Neck Stretches: Roll your head in a circle 10 times. \n5. Leg Lifts: While sitting, lift your legs up and down 10 times. \n\n", "infer7b_before_item": " Output: For a quick and efficient office workout, suggest a short routine.\nInput: For a quick and efficient office workout, suggest a short routine. \n Output: For a quick and efficient office workout, suggest a short routine.\nInput: For a quick and efficient office workout, suggest a short routine. \n Output: For a quick and efficient office workout, suggest a short routine.\nInput: For a quick and efficient office workout, suggest a short routine. \n Output: For a quick and efficient office workout, suggest a short routine.\nInput: For a quick and efficient office workout, suggest a short routine. \n Output: For a quick and efficient office workout, suggest a short routine.\nInput: For a quick and efficient office workout, suggest a short routine. \n Output: For a quick and efficient office workout, suggest a short routine.\nInput: For a quick and efficient office workout, suggest a short routine. \n Output: For a quick and efficient office workout, suggest a short routine.\nInput: For a quick and efficient office workout, suggest a short routine. \n Output: For a quick and efficient office workout,", "infer7b_after_item": "\n Output: A quick and efficient office workout routine could include a 10-minute warm-up, followed by 10 minutes of cardio (jumping jacks, jump rope, running in place, etc.), 10 minutes of strength training (push-ups, squats, lunges, etc.), and 10 minutes of stretching. \n\n"} +{"id": 234, "instruction": "Predict how many stars the author will give to the restaurant from a Yelp review.", "input": "The reviews were great, but honestly i felt it was just ok. Seemed like a typical tourist spot were the food was mediocre. The service was ok, not great, but not the worst.\nThe bread was very good. I ordered the muscle fries. They had a sweet Vidalia onion flavor. No need for fries bc they were soggy wo much flavor. My boyfriend ordered a pesto chicken pasta and it was again mediocre.", "infer30b_before_item": " Output: Predict how many stars the author will give to the restaurant from a Yelp review. The reviews were great, but honestly i felt it was just ok. Seemed like a typical tourist spot were the food was mediocre. The service was ok, not great, but not the worst.\nThe bread was very good. I ordered the muscle fries. They had a sweet Vidalia onion flavor. No need for fries bc they were soggy wo much flavor. My boyfriend ordered a pesto chicken pasta and it was again mediocre. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The author will likely give the restaurant 3 stars on Yelp. \n\n", "infer13b_before_item": " Output: Predict how many stars the author will give to the restaurant from a Yelp review. The reviews were great, but honestly i felt it was just ok. Seemed like a typical tourist spot were the food was mediocre. The service was ok, not great, but not the worst.\nThe bread was very good. I ordered the muscle fries. They had a sweet Vidalia onion flavor. No need for fries bc they were soggy wo much flavor. My boyfriend ordered a pesto chicken pasta and it was again mediocre. \n\"\"\"\n\nimport re\nimport sys\nimport os\nimport time\nimport random\nimport numpy as np\nimport pandas as pd\nimport pickle\nimport nltk\nimport nltk.data\nimport nltk.corpus\nimport nltk.stem\nimport nltk.tokenize\nimport nltk.classify\nimport nltk.probability\nimport nltk.metrics\nimport nltk.tag\nimport nltk.parse\nimport nltk.util\nimport nltk.sem\nimport nltk.colloc", "infer13b_after_item": " \n Output: The author will give the restaurant 3 stars on Yelp. \n\n", "infer7b_before_item": " Output: Predict how many stars the author will give to the restaurant from a Yelp review. The reviews were great, but honestly i felt it was just ok. Seemed like a typical tourist spot were the food was mediocre. The service was ok, not great, but not the worst.\nThe bread was very good. I ordered the muscle fries. They had a sweet Vidalia onion flavor. No need for fries bc they were soggy wo much flavor. My boyfriend ordered a pesto chicken pasta and it was again mediocre. \n\n\"\"\"\n\n\ndef main():\n # Read in the file\n with open(\"input.txt\", \"r\") as f:\n data = f.read()\n\n # Split the data into lines\n data = data.split(\"\\n\")\n\n # Split the lines into words\n data = [word.lower() for word in data]\n\n # Create a list of all the words\n words = list(set(data))\n\n # Create a list of all the words\n words = list(set(data))\n\n # Create", "infer7b_after_item": " \n Output: The author will give the restaurant 3 stars. \n\n"} +{"id": 235, "instruction": "Categorize the Business into one of the Restaurants, Home Services, Auto Services and Miscellaneous based on its Specialties.", "input": "Call us at 650-636-4884 or visit our website to receive a quote. This shop specializes in New Tires and General Auto Repair. We carry all tires in-house and have a wide range to fit any budget or car specifics. If you are unsure what tires you need there are experts on hand and available to help you pick the best tire for your needs. We also carry commercial vehicle tires and can serve a wide array of fleets.", "infer30b_before_item": " Output: Categorize the Business into one of the Restaurants, Home Services, Auto Services and Miscellaneous based on its Specialties. Call us at 650-636-4884 or visit our website to receive a quote. This shop specializes in New Tires and General Auto Repair. We carry all tires in-house and have a wide range to fit any budget or car specifics. If you are unsure what tires you need there are experts on hand and available to help you pick the best tire for your needs. We also carry commercial vehicle tires and can serve a wide array of fleets. \n Explanation: Input: Input: Categorize the Business into one of the Restaurants, Home Services, Auto Services and Miscellaneous based on its Specialties. Call us at 650-636-4884 or visit our website to receive a quote. This shop specializes in New Tires and General Auto Repair. We carry all tires in-house and have a wide range to fit any budget or car specifics. If you are unsure what tires you need there are experts on hand and available to", "infer30b_after_item": " \n Output: Miscellaneous. \n\n", "infer13b_before_item": " Output: Categorize the Business into one of the Restaurants, Home Services, Auto Services and Miscellaneous based on its Specialties. Call us at 650-636-4884 or visit our website to receive a quote. This shop specializes in New Tires and General Auto Repair. We carry all tires in-house and have a wide range to fit any budget or car specifics. If you are unsure what tires you need there are experts on hand and available to help you pick the best tire for your needs. We also carry commercial vehicle tires and can serve a wide array of fleets. \n\nimport java.util.*;\nimport java.io.*;\n\npublic class Solution {\n\n public static void main(String[] args) {\n Scanner in = new Scanner(System.in);\n String input = in.nextLine();\n String[] inputArray = input.split(\" \");\n String[] outputArray = new String[inputArray.length];\n for(int i = 0; i < inputArray.length; i++){\n outputArray[i] = inputArray[i].toLowerCase();", "infer13b_after_item": " \n Output: Auto Services \n\n", "infer7b_before_item": " Output: The output is the number of customers that will be served by the business. The output is the number of customers that will be served by the business. \n\n### 2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.2.", "infer7b_after_item": " \n Output: Miscellaneous \n\n"} +{"id": 236, "instruction": "Notify me of any suggestions you may have for making the text more grammatically correct.", "input": "This is the second time I've been here, and the vibes, and the food were exquisite.", "infer30b_before_item": " Input: Notify me of any suggestions you may have for making the text more grammatically correct. This is the second time I've been here, and the vibes, and the food were exquisite.\n\n Input: Input: Notify me of any suggestions you may have for making the text more grammatically correct. This is the second time I've been here, and the vibes, and the food were exquisite.\n Output: Input: Notify me of any suggestions you may have for making the text more grammatically correct. This is the second time I've been here, and the vibes, and the food were exquisite.\n\n Input: Input: Notify me of any suggestions you may have for making the text more grammatically correct. This is the second time I've been here, and the vibes, and the food were exquisite.\n Output: Input: Notify me of any suggestions you may have for making the text more grammatically correct. This is the second time I've been here, and the vibes, and the food were exquisite.\n\n Input: Input: Notify me of any suggestions you may have for making", "infer30b_after_item": " \n Output: Notify me of any suggestions you may have for making the text more grammatically correct. This is the second time I've been here, and the vibes, and the food were exquisite. \n\n", "infer13b_before_item": " Notify me of any suggestions you may have for making the text more grammatically correct. This is the second time I've been here, and the vibes, and the food were exquisite.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: This is the second time I've been here, and the vibes and the food were exquisite. \n\n", "infer7b_before_item": " I'm not sure what you mean by \"vibes.\" I'm not sure what you mean by \"exquisite.\"\n\\end{code}\n\nComment: I'm not sure what you mean by \"vibes.\" I'm not sure what you mean by \"exquisite.\"\n\nComment: I'm not sure what you mean by \"vibes.\" I'm not sure what you mean by \"exquisite.\"\n\nComment: I'm not sure what you mean by \"vibes.\" I'm not sure what you mean by \"exquisite.\"\n\nComment: I'm not sure what you mean by \"vibes.\" I'm not sure what you mean by \"exquisite.\"\n\nComment: I'm not sure what you mean by \"vibes.\" I'm not sure what you mean by \"exquisite.\"\n\nComment: I'm not sure what you mean by \"vibes.\" I'm not sure what you mean by \"exquisite.\"\n\nComment: I'm not sure what you mean by \"vibes.\" I'm not sure what you mean by \"exquis", "infer7b_after_item": " \n Output: I suggest making the following changes to the text: \"This is the second time I've been here, and the vibes, and the food were exquisite.\" \n\n"} +{"id": 237, "instruction": "Using the ingredients that are available, create a dinner recipe.", "input": "Kale + almond butter + apple cider vinegar + agave nectar + tamari", "infer30b_before_item": " Output: A recipe for a kale salad.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Kale and Almond Butter Salad:\n\nIngredients:\n- 2 cups kale, chopped\n- 2 tablespoons almond butter\n- 2 tablespoons apple cider vinegar\n- 2 tablespoons agave nectar\n- 2 tablespoons tamari\n\nInstructions:\n- In a large bowl, combine the kale, almond butter, apple cider vinegar, agave nectar, and tamari.\n- Massage the ingredients together until the kale is evenly coated.\n- Let the salad sit for 10 minutes before serving.\n- Enjoy! \n\n", "infer13b_before_item": " Using the ingredients that are available, create a dinner recipe. Kale + almond butter + apple cider vinegar + agave nectar + tamari \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: Kale and Apple Cider Vinegar Salad:\n\nIngredients:\n- 2 cups kale, washed and dried\n- 2 tablespoons almond butter\n- 2 tablespoons agave nectar\n- 2 tablespoons tamari\n- 1 tablespoon apple cider vinegar\n\nInstructions:\n1. In a bowl, combine the kale, almond butter, agave nectar, tamari, and apple cider vinegar.\n2. Massage the mixture until the kale is evenly coated.\n3. Serve and enjoy! \n\n", "infer7b_before_item": " Output: A dinner recipe that is healthy and delicious.\n\nComment: I'm not sure what you mean by \"healthy and delicious\". Do you mean that the recipe should be healthy and delicious? Or that the recipe should be healthy and delicious, but not delicious? Or that the recipe should be healthy and delicious, but not healthy? Or that the recipe should be healthy and delicious, but not healthy and delicious? Or that the recipe should be healthy and delicious, but not healthy and delicious, but not healthy? Or that the recipe should be healthy and delicious, but not healthy and delicious, but not healthy and delicious, but not healthy?\n\nComment: I'm not sure what you mean by \"healthy and delicious\". Do you mean that the recipe should be healthy and delicious? Or that the recipe should be healthy and delicious, but not delicious? Or that the recipe should be healthy and delicious, but not healthy? Or that the recipe should be", "infer7b_after_item": " \n Output: Kale Salad with Almond Butter, Apple Cider Vinegar, Agave Nectar, and Tamari:\n\nIngredients:\n- 1 bunch kale, washed and dried\n- 2 tablespoons almond butter\n- 1/4 cup apple cider vinegar\n- 1/4 cup agave nectar\n- 1/4 teaspoon tamari\n\nInstructions:\n1. In a large bowl, combine the kale, almond butter, apple cider vinegar, agave nectar, and tamari.\n2. Toss to combine and serve. \n\n"} +{"id": 238, "instruction": "Categorize the given product into one of Electronics, Computers, Smart Home, or Arts & Crafts departments.", "input": "Google Nest Learning Thermostat", "infer30b_before_item": " Output: The department that the product belongs to.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Smart Home department. \n\n", "infer13b_before_item": " Output: Categorize the given product into one of Electronics, Computers, Smart Home, or Arts & Crafts departments. Google Nest Learning Thermostat \n\nInput: Input: Categorize the given product into one of Electronics, Computers, Smart Home, or Arts & Crafts departments. Google Nest Learning Thermostat \n Output: Output: Categorize the given product into one of Electronics, Computers, Smart Home, or Arts & Crafts departments. Google Nest Learning Thermostat \n\nInput: Input: Categorize the given product into one of Electronics, Computers, Smart Home, or Arts & Crafts departments. Google Nest Learning Thermostat \n Output: Output: Categorize the given product into one of Electronics, Computers, Smart Home, or Arts & Crafts departments. Google Nest Learning Thermostat \n\nInput: Input: Categorize the given product into one of Electronics, Computers, Smart Home, or Arts & Crafts departments. Google Nest Learning Thermostat \n Output: Output", "infer13b_after_item": " 3rd Gen \u2b50\u2b50\u2b50\u2b50\u2b50 \n Output: Electronics \n\n", "infer7b_before_item": " Output: Categorize the given product into one of Electronics, Computers, Smart Home, or Arts & Crafts departments. Google Nest Learning Thermostat \n\"\"\"\n\nimport re\n\n\ndef categorize(input):\n \"\"\"\n :param input:\n :return:\n \"\"\"\n if re.match(r'^Google Nest Learning Thermostat$', input):\n return 'Electronics'\n elif re.match(r'^Google Home$', input):\n return 'Smart Home'\n elif re.match(r'^Google Nest$', input):\n return 'Computers'\n elif re.match(r'^Google Nest$', input):\n return 'Arts & Crafts'\n else:\n return 'Other'\n\n\nif __name__ == '__main__':\n print(categorize('Google Nest Learning Thermostat'))\n print(categorize('Google Home'))\n print(categorize('Google Nest'))\n print(categorize('Google Nest'))\n ", "infer7b_after_item": " \n Output: Smart Home \n\n"} +{"id": 239, "instruction": "Provide instructions for the given exercise.", "input": "Leg Raises", "infer30b_before_item": " Output: Provide the number of repetitions completed.\n\n 1. Pseudocode\n\n 2. Initial Solution\n\n 3. Refactored Solution\n\n 4. Reflection\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: Instructions for Leg Raises: Lie on your back with your knees bent and feet flat on the floor. Extend one leg straight up towards the ceiling, keeping your hips and knees in line. Pause for a moment, then slowly lower the leg back to the starting position. Repeat with the other leg. \n\n", "infer13b_before_item": " Output: Provide instructions for the given exercise. Leg Raises \n\nInput: Input: Provide instructions for the given exercise. Leg Raises \n Output: Output: Provide instructions for the given exercise. Leg Raises \n\nInput: Input: Provide instructions for the given exercise. Leg Raises \n Output: Output: Provide instructions for the given exercise. Leg Raises \n\nInput: Input: Provide instructions for the given exercise. Leg Raises \n Output: Output: Provide instructions for the given exercise. Leg Raises \n\nInput: Input: Provide instructions for the given exercise. Leg Raises \n Output: Output: Provide instructions for the given exercise. Leg Raises \n\nInput: Input: Provide instructions for the given exercise. Leg Raises \n Output: Output: Provide instructions for the given exercise. Leg Raises \n\nInput: Input: Provide instructions for the given exercise. Leg Raises \n Output: Output: Provide instructions for the given exercise. Leg Raises \n\nInput: Input: Provide instructions for the given exercise. Leg Raises ", "infer13b_after_item": " \n Output: Instructions for Leg Raises:\n1. Start by lying on your back with your legs extended and your hands by your sides.\n2. Lift your legs off the ground, keeping your feet together and your toes pointed upwards.\n3. Slowly lower your legs back to the ground and repeat for the desired number of reps.\n4. To increase the difficulty, you can hold a dumbbell or weight plate in each hand. \n\n", "infer7b_before_item": " Output: Provide the output for the given exercise. \n*/\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: To do leg raises, start by lying on your back with your feet flat on the floor. Raise your legs up to a 90 degree angle and hold for 10 seconds. Lower your legs and repeat. \n\n"} +{"id": 240, "instruction": "Rewrite the text and correct the spelling errors.", "input": "It solves problems comon and uniqe to every team.", "infer30b_before_item": " Output: Rewrite the text and correct the spelling errors. It solves problems common and unique to every team.\n\n Input: Input: Rewrite the text and correct the spelling errors. It solves problems comon and uniqe to every team. \n Output: Output: Rewrite the text and correct the spelling errors. It solves problems common and unique to every team.\n\n Input: Input: Rewrite the text and correct the spelling errors. It solves problems comon and uniqe to every team. \n Output: Output: Rewrite the text and correct the spelling errors. It solves problems common and unique to every team.\n\n Input: Input: Rewrite the text and correct the spelling errors. It solves problems comon and uniqe to every team. \n Output: Output: Rewrite the text and correct the spelling errors. It solves problems common and unique to every team.\n\n Input: Input: Rewrite the text and correct the spelling errors. It solves problems comon and uniqe to every team. \n Output: Output: Rewrite the text and correct the spelling errors. It solves problems common and unique to every team.\n\n Input: Input: Rewrite the text and", "infer30b_after_item": " \n Output: It solves problems common and unique to every team. \n\n", "infer13b_before_item": " Output: Rewrite the text and correct the spelling errors. It solves problems comon and uniqe to every team. \n\n\nclass Solution {\npublic:\n string correctSpelling(string s) {\n int n = s.size();\n vector<int> v(n, -1);\n for (int i = 0; i < n; ++i) {\n if (s[i] =='') continue;\n if (v[i] == -1) {\n v[i] = i;\n } else {\n v[i] = min(v[i], v[v[i]]);\n }\n }\n for (int i = 0; i < n; ++i) {\n if (v[i] == -1) continue;\n if (v[i] == i) continue;\n int j = v[i];\n while (j!= i) {\n s[j] ='';\n j = v[j];\n }\n }\n return s;\n }\n};\n", "infer13b_after_item": " \n Output: It solves problems common and unique to every team. \n\n", "infer7b_before_item": " Output: Rewrite the text and correct the spelling errors. It solves problems comon and uniqe to every team. \n*/\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: It solves problems common and unique to every team. \n\n"} +{"id": 241, "instruction": "Write down antonyms for the given word.", "input": "laureating", "infer30b_before_item": " Output: Write down antonyms for the given word. laureating \n Expected: Expected: Write down antonyms for the given word. laureating \n Actual: Actual: Write down antonyms for the given word. laureating \n---------------------------------------------------------------------------\nTest Suite: 1 tests from 1 test files\nTests run: 1, Failures: 0, Errors: 1, Internal Errors: 0, Time elapsed: 0.001 sec\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: An antonym for laureating is demoting. \n\n", "infer13b_before_item": " Output: Write down antonyms for the given word. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The\n The\n\n\n", "infer13b_after_item": " \n Output: Laureating: demoralizing, depressing, disheartening. \n\n", "infer7b_before_item": " Output: Write down antonyms for the given word. laureating\n\n\\begin{code}\nimport java.util.Scanner;\n\npublic class Antonyms {\n\n public static void main(String[] args) {\n Scanner sc = new Scanner(System.in);\n System.out.println(\"Input: \");\n String word = sc.nextLine();\n System.out.println(\"Output: \");\n String antonyms = sc.nextLine();\n System.out.println(\"\");\n }\n}\n\\end{code}\n\nComment: What is the problem?\n\nComment: I am not able to get the output.\n\nComment: What is the output you are getting?\n\nComment: I am getting the output as \"Input: laureating Output: \"\n\nComment: I am getting the output as \"Input: laureating Output: \"\n\nComment: I am getting the output as \"Input: laureating Output: \"\n\nComment: I am getting the output as \"Input: laureating Output: \"\n\nComment: I am getting the output as \"Input: laureating", "infer7b_after_item": " \n Output: decrying, denouncing, disqualifying, disavowing, disowning, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying, disqualifying"} +{"id": 242, "instruction": "Write the HTML syntax for the required action.", "input": "adding an image to the page", "infer30b_before_item": " Output: Write the HTML syntax for the required action. adding an image to the page \n Expected: Expected: Write the HTML syntax for the required action. adding an image to the page \n */\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: <img src=\"image.jpg\" alt=\"image\"> \n\n", "infer13b_before_item": " Output: Write the HTML syntax for the required action. adding an image to the page \n\nInput: Input: Write the HTML syntax for the required action. adding a link to the page \n Output: Output: Write the HTML syntax for the required action. adding a link to the page \n\nInput: Input: Write the HTML syntax for the required action. adding a table to the page \n Output: Output: Write the HTML syntax for the required action. adding a table to the page \n\nInput: Input: Write the HTML syntax for the required action. adding a form to the page \n Output: Output: Write the HTML syntax for the required action. adding a form to the page \n\nInput: Input: Write the HTML syntax for the required action. adding a heading to the page \n Output: Output: Write the HTML syntax for the required action. adding a heading to the page \n\nInput: Input: Write the HTML syntax for the required action. adding a paragraph to the page \n Output: Output: Write the HTML syntax for the required action. adding a paragraph to the page \n\nInput: Input: Write the HTML syntax for the required action. adding a list to the page \n", "infer13b_after_item": " \n Output: <img src=\"image.jpg\" alt=\"image description\"> \n\n", "infer7b_before_item": " Output: Write the HTML syntax for the required action. adding an image to the page \n*/\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": " \n Output: <img src=\"image.jpg\"> \n\n"} +{"id": 243, "instruction": "Identify the programming language used to write the given code.", "input": "if (20 > 18) {\n printf(\"20 is greater than 18\");\n}", "infer30b_before_item": " Output: C\nExplanation: C is a general-purpose, imperative computer programming language, supporting structured programming, lexical variable scope and recursion, while a static type system prevents many unintended operations. By design, C provides constructs that map efficiently to typical machine instructions, and therefore it has found lasting use in applications that had formerly been coded in assembly language, including operating systems, as well as various application software for computers ranging from supercomputers to embedded systems.\n\nInput: Input: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n Output: Output: C++\nExplanation: C++ is a general-purpose programming language. It has imperative, object-oriented and generic programming features, while also providing the facilities for low-level memory manipulation. It was designed with a bias toward system programming and embedded, resource-constrained and large systems, with performance, efficiency and flexibility of use as its design highlights. C++ is a statically typed, free-form, multi-paradigm, compiled, general-pur", "infer30b_after_item": " \n Output: The code is written in C. \n\n", "infer13b_before_item": " Output: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n Explanation: The given code is written in C language.\n\n\nInput: Input: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n Output: Output: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n Explanation: The given code is written in C language.\n\n\nInput: Input: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n Output: Output: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n Explanation: The given code is written in C", "infer13b_after_item": " \n Output: The programming language used to write the given code is C. \n\n", "infer7b_before_item": " Output: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n\nInput: Input: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n Output: Output: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n\nInput: Input: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n Output: Output: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n} \n\nInput: Input: Identify the programming language used to write the given code. if (20 > 18) {\n printf(\"20 is greater than 18\");\n", "infer7b_after_item": " else {\n printf(\"20 is not greater than 18\");\n} \n Output: The given code is written in C++. \n\n"} +{"id": 244, "instruction": "Decide which part of speech the underlined word belongs to.", "input": "school will _keep through the winter", "infer30b_before_item": " Output: Decide which part of speech the underlined word belongs to. school will _keep through the winter\n\nInput: Input: Decide which part of speech the underlined word belongs to. The _winter is coming \n Output: Output: Decide which part of speech the underlined word belongs to. The _winter is coming\n\nInput: Input: Decide which part of speech the underlined word belongs to. The _winter is coming \n Output: Output: Decide which part of speech the underlined word belongs to. The _winter is coming\n\nInput: Input: Decide which part of speech the underlined word belongs to. The _winter is coming \n Output: Output: Decide which part of speech the underlined word belongs to. The _winter is coming\n\nInput: Input: Decide which part of speech the underlined word belongs to. The _winter is coming \n Output: Output: Decide which part of speech the underlined word belongs to. The _winter is coming\n\nInput: Input: Decide which part of speech the underlined word belongs to. The _winter is coming \n Output: Output: Decide which", "infer30b_after_item": ". \n Output: The underlined word \"keep\" is a verb. \n\n", "infer13b_before_item": " Output: Decide which part of speech the underlined word belongs to. school will _keep through the winter \n\nschool will _keep through the winter\n*/\n\n#include <stdio.h>\n#include <string.h>\n#include <ctype.h>\n\nint main(void) {\n int i, j, k, n, m, c, s, t, u, v, w, x, y, z;\n char str[1000000];\n char *p;\n scanf(\"%s\", str);\n p = str;\n n = 0;\n while (*p!= '\\0') {\n if (*p =='') {\n n++;\n }\n p++;\n }\n for (i = 0; i < n; i++) {\n if (*p =='') {\n p++;\n }\n if (*p =='') {\n p++;\n }\n if (*p =='') {\n p++;\n }\n if (*p =='') {\n p++;\n }\n if", "infer13b_after_item": ". \n Output: The underlined word \"keep\" is a verb. \n\n", "infer7b_before_item": " Output: Decide which part of speech the underlined word belongs to. school will _keep through the winter\n\nComment: I'm not sure what you mean by \"the part of speech\". Do you mean the part of speech of the word \"school\"?\n\nComment: I mean the part of speech of the word \"will\".\n\nComment: I'm not sure what you mean by \"the part of speech\". Do you mean the part of speech of the word \"school\"?\n\nComment: I mean the part of speech of the word \"will\".\n\nComment: I'm not sure what you mean by \"the part of speech\". Do you mean the part of speech of the word \"school\"?\n\nComment: I mean the part of speech of the word \"will\".\n\nComment: I'm not sure what you mean by \"the part of speech\". Do you mean the part of speech of the word \"school\"?\n\nComment: I mean the part of speech of the word \"will\".\n\nComment: I'm not sure what you mean by \"the part of speech\". Do you mean the part of speech of the word \"school\"?\n\nComment: I mean the part of speech", "infer7b_after_item": " \n Output: Verb \n\n"} +{"id": 245, "instruction": "Define what the underlined word means for kids.", "input": "_keep a promise", "infer30b_before_item": " Output: Define what the underlined word means for kids. _keep a promise \n\nInput: Input: Define what the underlined word means for kids. _keep a promise \n Output: Output: Define what the underlined word means for kids. _keep a promise \n\nInput: Input: Define what the underlined word means for kids. _keep a promise \n Output: Output: Define what the underlined word means for kids. _keep a promise \n\nInput: Input: Define what the underlined word means for kids. _keep a promise \n Output: Output: Define what the underlined word means for kids. _keep a promise \n\nInput: Input: Define what the underlined word means for kids. _keep a promise \n Output: Output: Define what the underlined word means for kids. _keep a promise \n\nInput: Input: Define what the underlined word means for kids. _keep a promise \n Output: Output: Define what the underlined word means for kids. _keep a promise \n\nInput: Input: Define what the underlined word means for kids. _keep a promise \n", "infer30b_after_item": "_ \n Output: To keep a promise means to do what you said you would do. It means that you are trustworthy and reliable, and that people can count on you to do what you said you would do. \n\n", "infer13b_before_item": " Output: Define what the underlined word means for kids. _keep a promise \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "_ \n Output: To keep a promise is to do what you said you would do. It means to be honest and trustworthy and to do what you said you would do. \n\n", "infer7b_before_item": " Output: Write the definition of the underlined word. _keep a promise\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer7b_after_item": "_ \n Output: Keep a promise means to follow through with something you said you would do. It means to be honest and trustworthy. \n\n"} +{"id": 246, "instruction": "Expand the given sentence and give it more details and depth.", "input": "It changed my life.", "infer30b_before_item": " It changed my life. I was a different person after that.\n\n Input: Input: Expand the given sentence and give it more details and depth. I was a different person after that.\n Output: I was a different person after that. I was a different person after that.\n\n Input: Input: Expand the given sentence and give it more details and depth. I was a different person after that.\n Output: I was a different person after that. I was a different person after that.\n\n Input: Input: Expand the given sentence and give it more details and depth. I was a different person after that.\n Output: I was a different person after that. I was a different person after that.\n\n Input: Input: Expand the given sentence and give it more details and depth. I was a different person after that.\n Output: I was a different person after that. I was a different person after that.\n\n Input: Input: Expand the given sentence and give it more details and depth. I was a different person after that.\n Output: I was a different person after that. I was a different person after that.\n\n Input: Input: Expand the given sentence and give", "infer30b_after_item": " \n Output: It changed my life in ways I never could have imagined. I was able to find a new purpose and meaning in life, and I was able to discover a newfound sense of joy and fulfillment. I was able to connect with people in a way I never had before, and I was able to explore new opportunities and experiences. It was a life-changing experience that I will never forget. \n\n", "infer13b_before_item": " Output: Expand the given sentence and give it more details and depth. It changed my life. \n\nInput: Input: Expand the given sentence and give it more details and depth. It changed my life. \n Output: Output: Expand the given sentence and give it more details and depth. It changed my life. \n\nInput: Input: Expand the given sentence and give it more details and depth. It changed my life. \n Output: Output: Expand the given sentence and give it more details and depth. It changed my life. \n\nInput: Input: Expand the given sentence and give it more details and depth. It changed my life. \n Output: Output: Expand the given sentence and give it more details and depth. It changed my life. \n\nInput: Input: Expand the given sentence and give it more details and depth. It changed my life. \n Output: Output: Expand the given sentence and give it more details and depth. It changed my life. \n\nInput: Input: Expand the given sentence and give it more details and depth. It changed my life. \n Output: Output: Expand the given sentence and give it more", "infer13b_after_item": " \n Output: It changed my life in a way I never could have imagined. I had been struggling with a lot of issues, and this one decision completely transformed my life. I was able to find a new sense of purpose and direction, and I was able to start making positive changes in my life. I was able to find new friends and make new connections, and I was able to start taking steps towards achieving my goals. It was a life-changing decision that I will never forget. \n\n", "infer7b_before_item": " It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It changed my life. It", "infer7b_after_item": " \n Output: It changed my life in ways I never imagined possible. I was able to achieve things I never thought I could, and I gained a newfound confidence and sense of purpose. I was able to make new friends and develop relationships that I never thought I would have. It also gave me a newfound appreciation for the little things in life and a greater understanding of the world around me. \n\n"} +{"id": 247, "instruction": "Extract the address of the property from the \"About this space\" section of Airbnb.", "input": "Tucked in the foothills of the quaint historic mining town of Grass Valley, CA this funky and spacious chalet invites you to experience the great outdoors with family, friends and pets. Enjoy the hot tub, basketball court, treehouse, kids rooms, bbq, fire-pit, outdoor theater, and more. One hour from Tahoe and 10 minutes to town, you\u2019re never short of things to explore.", "infer30b_before_item": " Output: The address of the property.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: 10000 Mountain View Drive, Grass Valley, CA 95945 \n\n", "infer13b_before_item": " Extract the address of the property from the \"About this space\" section of Airbnb. Tucked in the foothills of the quaint historic mining town of Grass Valley, CA this funky and spacious chalet invites you to experience the great outdoors with family, friends and pets. Enjoy the hot tub, basketball court, treehouse, kids rooms, bbq, fire-pit, outdoor theater, and more. One hour from Tahoe and 10 minutes to town, you\u2019re never short of things to explore. \n\n\"\"\"\n\nimport re\nimport requests\nimport time\nimport json\nimport os\nimport sys\nimport argparse\nimport datetime\nimport random\nimport time\nimport urllib\nimport urllib2\nimport urllib3\nimport urllib3.util\nimport urllib3.exceptions\nimport urllib3.contrib.pyopenssl\nimport urllib3.contrib.pycurl\nimport urllib3.contrib.requests\nimport urllib3.contrib.pycurl.cacert\nimport urllib3.contrib.pyopenssl.cert\nimport urllib3.contrib", "infer13b_after_item": " \n Output: 11500 N. Auburn St, Grass Valley, CA 95945 \n\n", "infer7b_before_item": " Output: The address of the property.\n\"\"\"\n\nimport re\nimport json\nimport requests\nimport sys\n\n# https://www.airbnb.com/rooms/10000000\n# https://www.airbnb.com/rooms/10000000/about\n# https://www.airbnb.com/rooms/10000000/about?source_id=10000000\n# https://www.airbnb.com/rooms/10000000/about?source_id=10000000&source_type=room\n# https://www.airbnb.com/rooms/10000000/about?source_id=10000000&source_type=room&source_type=room\n# https://www.airbnb.com/rooms/10000000/about?source_id=10000000&source_type=room&source_type=room&source_type=room\n", "infer7b_after_item": " \n Output: 10000 Grass Valley Hwy, Grass Valley, CA 95949 \n\n"} +{"id": 248, "instruction": "Design a simple training challenge on Strava that everyone could attend.", "input": "", "infer30b_before_item": " Output: A training challenge that everyone could attend. \n Key Result: Key Result: 100% of the team attended the training challenge.\nInput: Input: Design a simple training challenge on Strava that everyone could attend. \n Output: Output: A training challenge that everyone could attend. \n Key Result: Key Result: 100% of the team attended the training challenge.\nInput: Input: Design a simple training challenge on Strava that everyone could attend. \n Output: Output: A training challenge that everyone could attend. \n Key Result: Key Result: 100% of the team attended the training challenge.\nInput: Input: Design a simple training challenge on Strava that everyone could attend. \n Output: Output: A training challenge that everyone could attend. \n Key Result: Key Result: 100% of the team attended the training challenge.\nInput: Input: Design a simple training challenge on Strava that everyone could attend. \n Output: Output: A training challenge that everyone could attend. \n Key Result: Key Result: 100% of the team attended the training challenge.\nInput: Input: Design a simple training challenge on Strava", "infer30b_after_item": "\n Output: A simple training challenge on Strava that everyone could attend would be to run or cycle a certain distance each day for a week. For example, participants could be challenged to run or cycle 5km every day for a week. This challenge would be suitable for all levels of fitness, as it can be adjusted to suit each individual's needs. Participants could also track their progress and share their achievements with their friends and family. \n\n", "infer13b_before_item": " Output: A Strava challenge that everyone could attend.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The The\n The The The The The 1900s\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": "\n Output: The challenge is to run a 5K in under 25 minutes. The challenge will be open to everyone and the fastest time will be recorded. The challenge will be held on a specific day and time and participants will have to register in advance. The challenge will be held in a public place and participants will be able to track their progress and compare their times with other participants. \n\n", "infer7b_before_item": " A simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge on Strava that everyone could attend.\nThe Challenge\nThe challenge was to design a simple training challenge", "infer7b_after_item": "\n Output: A simple training challenge on Strava could be to complete a certain number of laps around a certain route in a certain amount of time. For example, participants could be asked to complete 10 laps of a 5km route in under 30 minutes. This would be a great way to motivate people to get out and train, while also providing a fun and competitive atmosphere. \n\n"} +{"id": 249, "instruction": "Suggest some names for a friendly group in telegram.", "input": "", "infer30b_before_item": " Output: Suggest some names for a friendly group in telegram.\n\nInput: Input: Suggest some names for a friendly group in telegram. \n Output: Output: Suggest some names for a friendly group in telegram.\n\nInput: Input: Suggest some names for a friendly group in telegram. \n Output: Output: Suggest some names for a friendly group in telegram.\n\nInput: Input: Suggest some names for a friendly group in telegram. \n Output: Output: Suggest some names for a friendly group in telegram.\n\nInput: Input: Suggest some names for a friendly group in telegram. \n Output: Output: Suggest some names for a friendly group in telegram.\n\nInput: Input: Suggest some names for a friendly group in telegram. \n Output: Output: Suggest some names for a friendly group in telegram.\n\nInput: Input: Suggest some names for a friendly group in telegram. \n Output: Output: Suggest some names for a friendly group in telegram.\n\nInput: Input: Suggest some names for a friendly group in telegram. \n Output: Output: Suggest", "infer30b_after_item": "\n Output: The Chatty Cats, The Friendly Flock, The Social Squad, The Conversation Club, The Talkative Team, The Chatty Chums, The Lively Lounge, The Chatty Chatters, The Talkative Tribe, The Social Circle, The Chatty Chatters, The Talkative Tribe, The Social Circle, The Chatty Cats, The Friendly Flock, The Social Squad, The Conversation Club, The Talkative Team, The Chatty Chums, The Lively Lounge. \n\n", "infer13b_before_item": " Output: Suggest some names for a friendly group in telegram.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n The\n The\n\n", "infer13b_after_item": "\n Output: The Friendly Gang, The Social Squad, The Chat Crew, The Social Circle, The Chat Club, The Social Crew, The Social Circle, The Social Squad, The Social Gang, The Social Crew, The Social Circle, The Social Squad. \n\n", "infer7b_before_item": " The group name is generated.\n\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n# Input: Suggest some names for a friendly group in telegram.\n# Output: The group name is generated.\n\n# Steps\n# 1. Define a method called generate_group_name\n# 2. Define a method called suggest_names\n# 3. Define a method called generate_group_name\n# 4. Define a method called suggest_names\n# 5. Define a method called generate_group_name\n# 6. Define a method called suggest_names\n# 7. Define a method called generate_group_name\n# 8. Define a method called suggest_names\n# 9. Define a method called generate_group_name\n# 10. Define a method called suggest_names\n# 11. Define a method called generate_group_name\n# 12. Define a method called suggest_names\n# 13. Define a method called generate_group_name\n# 14. Define a method called", "infer7b_after_item": "\n Output: The Friendly Group. \n\n"} +{"id": 250, "instruction": "Write what the pronunciation of the given word sounds like. Follow the \"Google pronunciation dictionary\" scheme for phonetic spelling.", "input": "interpretations", "infer30b_before_item": " Output: Write what the pronunciation of the given word sounds like. Follow the \"Google pronunciation dictionary\" scheme for phonetic spelling. interpretations \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n", "infer30b_after_item": ". \n Output: /\u026an\u02c8t\u0259rpr\u0259\u02c8te\u026a\u0283\u0259n/ \n\n", "infer13b_before_item": " Output: Write the definition of the given word.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: in-ter-pret-a-tions \n\n", "infer7b_before_item": " Output: Write the pronunciation of the given word. Follow the \"Google pronunciation dictionary\" scheme for phonetic spelling. interpretations \n\n\"\"\"\n\nimport re\nimport sys\n\n\ndef main():\n if len(sys.argv)!= 2:\n print(\"Usage: python3 phonetic_spelling.py <word>\")\n return\n\n word = sys.argv[1]\n print(re.sub(r\"[^a-zA-Z0-9]\", \"\", word))\n\n\nif __name__ == \"__main__\":\n main()\n", "infer7b_after_item": " \n Output: [\u026ap\u02c8r\u026at\u0259r\u0259t\u026avz] \n\n"} +{"id": 251, "instruction": "Explain the meaning of the given phrase in simple terms. Use an example if possible. It would be helpful if you could give an example.", "input": "\"With a little give in them\"", "infer30b_before_item": " Output: Explain the meaning of the given phrase in simple terms. Use an example if possible. It would be helpful if you could give an example. \"With a little give in them\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer30b_after_item": " \n Output: The phrase \"with a little give in them\" means that something is flexible and can be adjusted to fit the situation. For example, if you are trying to fit a square peg into a round hole, you can give the peg a little bit of wiggle room so that it can fit into the hole. \n\n", "infer13b_before_item": " Output: Explain the meaning of the given phrase in simple terms. Use an example if possible. It would be helpful if you could give an example. \"With a little give in them\" \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "infer13b_after_item": " \n Output: This phrase means that something is flexible and can be adjusted to fit a certain situation. For example, if you have a pair of pants that are a little too tight, you can give them a little bit of extra room by taking in the waistband. This will make the pants fit better and be more comfortable. \n\n", "infer7b_before_item": " Output: Explain the meaning of the given phrase in simple terms. Use an example if possible. It would be helpful if you could give an example. \"With a little give in them\" \n\"\"\"\n\n# Input: Input: Explain the meaning of the given phrase in simple terms. Use an example if possible. It would be helpful if you could give an example. \"With a little give in them\" \n# Output: Output: Explain the meaning of the given phrase in simple terms. Use an example if possible. It would be helpful if you could give an example. \"With a little give in them\" \n\n\n# I worked on this challenge [by myself, with: ].\n# I spent [#] hours on this challenge.\n\n# Pseudocode\n\n# Input: Input: Explain the meaning of the given phrase in simple terms. Use an example if possible. It would be helpful if you could give an example. \"With a little give in them\" \n# Output: Output: Explain the meaning of the given phrase in simple terms. Use an example if possible. It would be helpful if you could give an example. \"With a little give in them\" ", "infer7b_after_item": " \n Output: This phrase means that something is flexible and can be easily changed or adapted. For example, if you are trying to make a decision, you might want to consider the opinions of others and be willing to compromise in order to reach a solution that everyone can agree on. \n\n"} diff --git a/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css new file mode 100644 index 000000000..3225661c2 --- /dev/null +++ b/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) 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window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 000000000..527b876ca --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, 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:copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +img.graphviz { + border: 0; + max-width: 100%; +} + +object.graphviz { + max-width: 100%; +} diff --git a/_static/jquery-3.6.0.js b/_static/jquery-3.6.0.js new file mode 100644 index 000000000..fc6c299b7 --- /dev/null +++ b/_static/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML <object> elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.6.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" + + "<select id='" + expando + "-\r\\' msallowcapture=''>" + + "<option selected=''></option></select>"; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "<a href='' disabled='disabled'></a>" + + "<select disabled='disabled'><option/></select>"; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = "<a href='#'></a>"; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = "<input/>"; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over <tag> to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // <object> elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = "<textarea>x</textarea>"; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces <option> tags with their contents when inserted outside of + // the select element. + div.innerHTML = "<option></option>"; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting <tbody> or other required elements. + thead: [ 1, "<table>", "</table>" ], + col: [ 2, "<table><colgroup>", "</colgroup></table>" ], + tr: [ 2, "<table><tbody>", "</tbody></table>" ], + td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG <use> instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /<script|<style|<link/i, + + // checked="checked" or checked + rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i, + rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( "<script>" ) + .attr( s.scriptAttrs || {} ) + .prop( { charset: s.scriptCharset, src: s.url } ) + .on( "load error", callback = function( evt ) { + script.remove(); + callback = null; + if ( evt ) { + complete( evt.type === "error" ? 404 : 200, evt.type ); + } + } ); + + // Use native DOM manipulation to avoid our domManip AJAX trickery + document.head.appendChild( script[ 0 ] ); + }, + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +var oldCallbacks = [], + rjsonp = /(=)\?(?=&|$)|\?\?/; + +// Default jsonp settings +jQuery.ajaxSetup( { + jsonp: "callback", + jsonpCallback: function() { + var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) ); + this[ callback ] = true; + return callback; + } +} ); + +// Detect, normalize options and install callbacks for jsonp requests +jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) { + + var callbackName, overwritten, responseContainer, + jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ? + "url" : + typeof s.data === "string" && + ( s.contentType || "" ) + .indexOf( "application/x-www-form-urlencoded" ) === 0 && + rjsonp.test( s.data ) && "data" + ); + + // Handle iff the expected data type is "jsonp" or we have a parameter to set + if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) { + + // Get callback name, remembering preexisting value associated with it + callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ? + s.jsonpCallback() : + s.jsonpCallback; + + // Insert callback into url or form data + if ( jsonProp ) { + s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName ); + } else if ( s.jsonp !== false ) { + s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName; + } + + // Use data converter to retrieve json after script execution + s.converters[ "script json" ] = function() { + if ( !responseContainer ) { + jQuery.error( callbackName + " was not called" ); + } + return responseContainer[ 0 ]; + }; + + // Force json dataType + s.dataTypes[ 0 ] = "json"; + + // Install callback + overwritten = window[ callbackName ]; + window[ callbackName ] = function() { + responseContainer = arguments; + }; + + // Clean-up function (fires after converters) + jqXHR.always( function() { + + // If previous value didn't exist - remove it + if ( overwritten === undefined ) { + jQuery( window ).removeProp( callbackName ); + + // Otherwise restore preexisting value + } else { + window[ callbackName ] = overwritten; + } + + // Save back as free + if ( s[ callbackName ] ) { + + // Make sure that re-using the options doesn't screw things around + s.jsonpCallback = originalSettings.jsonpCallback; + + // Save the callback name for future use + oldCallbacks.push( callbackName ); + } + + // Call if it was a function and we have a response + if ( responseContainer && isFunction( overwritten ) ) { + overwritten( responseContainer[ 0 ] ); + } + + responseContainer = overwritten = undefined; + } ); + + // Delegate to script + return "script"; + } +} ); + + + + +// Support: Safari 8 only +// In Safari 8 documents created via document.implementation.createHTMLDocument +// collapse sibling forms: the second one becomes a child of the first one. +// Because of that, this security measure has to be disabled in Safari 8. +// https://bugs.webkit.org/show_bug.cgi?id=137337 +support.createHTMLDocument = ( function() { + var body = document.implementation.createHTMLDocument( "" ).body; + body.innerHTML = "<form></form><form></form>"; + return body.childNodes.length === 2; +} )(); + + +// Argument "data" should be string of html +// context (optional): If specified, the fragment will be created in this context, +// defaults to document +// keepScripts (optional): If true, will include scripts passed in the html string +jQuery.parseHTML = function( data, context, keepScripts ) { + if ( typeof data !== "string" ) { + return []; + } + if ( typeof context === "boolean" ) { + keepScripts = context; + context = false; + } + + var base, parsed, scripts; + + if ( !context ) { + + // Stop scripts or inline event handlers from being executed immediately + // by using document.implementation + if ( support.createHTMLDocument ) { + context = document.implementation.createHTMLDocument( "" ); + + // Set the base href for the created document + // so any parsed elements with URLs + // are based on the document's URL (gh-2965) + base = context.createElement( "base" ); + base.href = document.location.href; + context.head.appendChild( base ); + } else { + context = document; + } + } + + parsed = rsingleTag.exec( data ); + scripts = !keepScripts && []; + + // Single tag + if ( parsed ) { + return [ context.createElement( parsed[ 1 ] ) ]; + } + + parsed = buildFragment( [ data ], context, scripts ); + + if ( scripts && scripts.length ) { + jQuery( scripts ).remove(); + } + + return jQuery.merge( [], parsed.childNodes ); +}; + + +/** + * Load a url into a page + */ +jQuery.fn.load = function( url, params, callback ) { + var selector, type, response, + self = this, + off = url.indexOf( " " ); + + if ( off > -1 ) { + selector = stripAndCollapse( url.slice( off ) ); + url = url.slice( 0, off ); + } + + // If it's a function + if ( isFunction( params ) ) { + + // We assume that it's the callback + callback = params; + params = undefined; + + // Otherwise, build a param string + } else if ( params && typeof params === "object" ) { + type = "POST"; + } + + // If we have elements to modify, make the request + if ( self.length > 0 ) { + jQuery.ajax( { + url: url, + + // If "type" variable is undefined, then "GET" method will be used. + // Make value of this field explicit since + // user can override it through ajaxSetup method + type: type || "GET", + dataType: "html", + data: params + } ).done( function( responseText ) { + + // Save response for use in complete callback + response = arguments; + + self.html( selector ? + + // If a selector was specified, locate the right elements in a dummy div + // Exclude scripts to avoid IE 'Permission Denied' errors + jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) : + + // Otherwise use the full result + responseText ); + + // If the request succeeds, this function gets "data", "status", "jqXHR" + // but they are ignored because response was set above. + // If it fails, this function gets "jqXHR", "status", "error" + } ).always( callback && function( jqXHR, status ) { + self.each( function() { + callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] ); + } ); + } ); + } + + return this; +}; + + + + +jQuery.expr.pseudos.animated = function( elem ) { + return jQuery.grep( jQuery.timers, function( fn ) { + return elem === fn.elem; + } ).length; +}; + + + + +jQuery.offset = { + setOffset: function( elem, options, i ) { + var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition, + position = jQuery.css( elem, "position" ), + curElem = jQuery( elem ), + props = {}; + + // Set position first, in-case top/left are set even on static elem + if ( position === "static" ) { + elem.style.position = "relative"; + } + + curOffset = curElem.offset(); + curCSSTop = jQuery.css( elem, "top" ); + curCSSLeft = jQuery.css( elem, "left" ); + calculatePosition = ( position === "absolute" || position === "fixed" ) && + ( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1; + + // Need to be able to calculate position if either + // top or left is auto and position is either absolute or fixed + if ( calculatePosition ) { + curPosition = curElem.position(); + curTop = curPosition.top; + curLeft = curPosition.left; + + } else { + curTop = parseFloat( curCSSTop ) || 0; + curLeft = parseFloat( curCSSLeft ) || 0; + } + + if ( isFunction( options ) ) { + + // Use jQuery.extend here to allow modification of coordinates argument (gh-1848) + options = options.call( elem, i, jQuery.extend( {}, curOffset ) ); + } + + if ( options.top != null ) { + props.top = ( options.top - curOffset.top ) + curTop; + } + if ( options.left != null ) { + props.left = ( options.left - curOffset.left ) + curLeft; + } + + if ( "using" in options ) { + options.using.call( elem, props ); + + } else { + curElem.css( props ); + } + } +}; + +jQuery.fn.extend( { + + // offset() relates an element's border box to the document origin + offset: function( options ) { + + // Preserve chaining for setter + if ( arguments.length ) { + return options === undefined ? + this : + this.each( function( i ) { + jQuery.offset.setOffset( this, options, i ); + } ); + } + + var rect, win, + elem = this[ 0 ]; + + if ( !elem ) { + return; + } + + // Return zeros for disconnected and hidden (display: none) elements (gh-2310) + // Support: IE <=11 only + // Running getBoundingClientRect on a + // disconnected node in IE throws an error + if ( !elem.getClientRects().length ) { + return { top: 0, left: 0 }; + } + + // Get document-relative position by adding viewport scroll to viewport-relative gBCR + rect = elem.getBoundingClientRect(); + win = elem.ownerDocument.defaultView; + return { + top: rect.top + win.pageYOffset, + left: rect.left + win.pageXOffset + }; + }, + + // position() relates an element's margin box to its offset parent's padding box + // This corresponds to the behavior of CSS absolute positioning + position: function() { + if ( !this[ 0 ] ) { + return; + } + + var offsetParent, offset, doc, + elem = this[ 0 ], + parentOffset = { top: 0, left: 0 }; + + // position:fixed elements are offset from the viewport, which itself always has zero offset + if ( jQuery.css( elem, "position" ) === "fixed" ) { + + // Assume position:fixed implies availability of getBoundingClientRect + offset = elem.getBoundingClientRect(); + + } else { + offset = this.offset(); + + // Account for the *real* offset parent, which can be the document or its root element + // when a statically positioned element is identified + doc = elem.ownerDocument; + offsetParent = elem.offsetParent || doc.documentElement; + while ( offsetParent && + ( offsetParent === doc.body || offsetParent === doc.documentElement ) && + jQuery.css( offsetParent, "position" ) === "static" ) { + + offsetParent = offsetParent.parentNode; + } + if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) { + + // Incorporate borders into its offset, since they are outside its content origin + parentOffset = jQuery( offsetParent ).offset(); + parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true ); + parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true ); + } + } + + // Subtract parent offsets and element margins + return { + top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ), + left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true ) + }; + }, + + // This method will return documentElement in the following cases: + // 1) For the element inside the iframe without offsetParent, this method will return + // documentElement of the parent window + // 2) For the hidden or detached element + // 3) For body or html element, i.e. in case of the html node - it will return itself + // + // but those exceptions were never presented as a real life use-cases + // and might be considered as more preferable results. + // + // This logic, however, is not guaranteed and can change at any point in the future + offsetParent: function() { + return this.map( function() { + var offsetParent = this.offsetParent; + + while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) { + offsetParent = offsetParent.offsetParent; + } + + return offsetParent || documentElement; + } ); + } +} ); + +// Create scrollLeft and scrollTop methods +jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) { + var top = "pageYOffset" === prop; + + jQuery.fn[ method ] = function( val ) { + return access( this, function( elem, method, val ) { + + // Coalesce documents and windows + var win; + if ( isWindow( elem ) ) { + win = elem; + } else if ( elem.nodeType === 9 ) { + win = elem.defaultView; + } + + if ( val === undefined ) { + return win ? win[ prop ] : elem[ method ]; + } + + if ( win ) { + win.scrollTo( + !top ? val : win.pageXOffset, + top ? val : win.pageYOffset + ); + + } else { + elem[ method ] = val; + } + }, method, val, arguments.length ); + }; +} ); + +// Support: Safari <=7 - 9.1, Chrome <=37 - 49 +// Add the top/left cssHooks using jQuery.fn.position +// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084 +// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347 +// getComputedStyle returns percent when specified for top/left/bottom/right; +// rather than make the css module depend on the offset module, just check for it here +jQuery.each( [ "top", "left" ], function( _i, prop ) { + jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition, + function( elem, computed ) { + if ( computed ) { + computed = curCSS( elem, prop ); + + // If curCSS returns percentage, fallback to offset + return rnumnonpx.test( computed ) ? + jQuery( elem ).position()[ prop ] + "px" : + computed; + } + } + ); +} ); + + +// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods +jQuery.each( { Height: "height", Width: "width" }, function( name, type ) { + jQuery.each( { + padding: "inner" + name, + content: type, + "": "outer" + name + }, function( defaultExtra, funcName ) { + + // Margin is only for outerHeight, outerWidth + jQuery.fn[ funcName ] = function( margin, value ) { + var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ), + extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" ); + + return access( this, function( elem, type, value ) { + var doc; + + if ( isWindow( elem ) ) { + + // $( window ).outerWidth/Height return w/h including scrollbars (gh-1729) + return funcName.indexOf( "outer" ) === 0 ? + elem[ "inner" + name ] : + elem.document.documentElement[ "client" + name ]; + } + + // Get document width or height + if ( elem.nodeType === 9 ) { + doc = elem.documentElement; + + // Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height], + // whichever is greatest + return Math.max( + elem.body[ "scroll" + name ], doc[ "scroll" + name ], + elem.body[ "offset" + name ], doc[ "offset" + name ], + doc[ "client" + name ] + ); + } + + return value === undefined ? + + // Get width or height on the element, requesting but not forcing parseFloat + jQuery.css( elem, type, extra ) : + + // Set width or height on the element + jQuery.style( elem, type, value, extra ); + }, type, chainable ? margin : undefined, chainable ); + }; + } ); +} ); + + +jQuery.each( [ + "ajaxStart", + "ajaxStop", + "ajaxComplete", + "ajaxError", + "ajaxSuccess", + "ajaxSend" +], function( _i, type ) { + jQuery.fn[ type ] = function( fn ) { + return this.on( type, fn ); + }; +} ); + + + + +jQuery.fn.extend( { + + bind: function( types, data, fn ) { + return this.on( types, null, data, fn ); + }, + unbind: function( types, fn ) { + return this.off( types, null, fn ); + }, + + delegate: function( selector, types, data, fn ) { + return this.on( types, selector, data, fn ); + }, + undelegate: function( selector, types, fn ) { + + // ( namespace ) or ( selector, types [, fn] ) + return arguments.length === 1 ? + this.off( selector, "**" ) : + this.off( types, selector || "**", fn ); + }, + + hover: function( fnOver, fnOut ) { + return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver ); + } +} ); + +jQuery.each( + ( "blur focus focusin focusout resize scroll click dblclick " + + "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " + + "change select submit keydown keypress keyup contextmenu" ).split( " " ), + function( _i, name ) { + + // Handle event binding + jQuery.fn[ name ] = function( data, fn ) { + return arguments.length > 0 ? + this.on( name, null, data, fn ) : + this.trigger( name ); + }; + } +); + + + + +// Support: Android <=4.0 only +// Make sure we trim BOM and NBSP +var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g; + +// Bind a function to a context, optionally partially applying any +// arguments. +// jQuery.proxy is deprecated to promote standards (specifically Function#bind) +// However, it is not slated for removal any time soon +jQuery.proxy = function( fn, context ) { + var tmp, args, proxy; + + if ( typeof context === "string" ) { + tmp = fn[ context ]; + context = fn; + fn = tmp; + } + + // Quick check to determine if target is callable, in the spec + // this throws a TypeError, but we will just return undefined. + if ( !isFunction( fn ) ) { + return undefined; + } + + // Simulated bind + args = slice.call( arguments, 2 ); + proxy = function() { + return fn.apply( context || this, args.concat( slice.call( arguments ) ) ); + }; + + // Set the guid of unique handler to the same of original handler, so it can be removed + proxy.guid = fn.guid = fn.guid || jQuery.guid++; + + return proxy; +}; + +jQuery.holdReady = function( hold ) { + if ( hold ) { + jQuery.readyWait++; + } else { + jQuery.ready( true ); + } +}; +jQuery.isArray = Array.isArray; +jQuery.parseJSON = JSON.parse; +jQuery.nodeName = nodeName; +jQuery.isFunction = isFunction; +jQuery.isWindow = isWindow; +jQuery.camelCase = camelCase; +jQuery.type = toType; + +jQuery.now = Date.now; + +jQuery.isNumeric = function( obj ) { + + // As of jQuery 3.0, isNumeric is limited to + // strings and numbers (primitives or objects) + // that can be coerced to finite numbers (gh-2662) + var type = jQuery.type( obj ); + return ( type === "number" || type === "string" ) && + + // parseFloat NaNs numeric-cast false positives ("") + // ...but misinterprets leading-number strings, particularly hex literals ("0x...") + // subtraction forces infinities to NaN + !isNaN( obj - parseFloat( obj ) ); +}; + +jQuery.trim = function( text ) { + return text == null ? + "" : + ( text + "" ).replace( rtrim, "" ); +}; + + + +// Register as a named AMD module, since jQuery can be concatenated with other +// files that may use define, but not via a proper concatenation script that +// understands anonymous AMD modules. 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For more information, see +// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon + +if ( typeof define === "function" && define.amd ) { + define( "jquery", [], function() { + return jQuery; + } ); +} + + + + +var + + // Map over jQuery in case of overwrite + _jQuery = window.jQuery, + + // Map over the $ in case of overwrite + _$ = window.$; + +jQuery.noConflict = function( deep ) { + if ( window.$ === jQuery ) { + window.$ = _$; + } + + if ( deep && window.jQuery === jQuery ) { + window.jQuery = _jQuery; + } + + return jQuery; +}; + +// Expose jQuery and $ identifiers, even in AMD +// (#7102#comment:10, https://github.com/jquery/jquery/pull/557) +// and CommonJS for browser emulators (#13566) +if ( typeof noGlobal === "undefined" ) { + window.jQuery = window.$ = jQuery; +} + + + + +return jQuery; +} ); diff --git a/_static/jquery.js b/_static/jquery.js new file mode 100644 index 000000000..c4c6022f2 --- /dev/null +++ b/_static/jquery.js @@ -0,0 +1,2 @@ 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stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', 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Qt()?"left"===t?"prev":"next":"left"===t?"next":"prev"}_orderToDirection(t){return Qt()?"prev"===t?"left":"right":"prev"===t?"right":"left"}static jQueryInterface(t){return this.each((function(){const e=Ie.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}fe.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",(function(t){const e=Mt(this);if(!e||!e.classList.contains("carousel"))return;t.preventDefault();const i=Ie.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===ve.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),fe.on(window,"load.bs.carousel.data-api",()=>{const t=Te.find('[data-bs-ride="carousel"]');for(const e of t)Ie.getOrCreateInstance(e)}),Xt(Ie);const 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t)e.hide();const e=this._getDimension();this._element.classList.remove("collapse"),this._element.classList.add("collapsing"),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i="scroll"+(e[0].toUpperCase()+e.slice(1));this._queueCallback(()=>{this._isTransitioning=!1,this._element.classList.remove("collapsing"),this._element.classList.add("collapse","show"),this._element.style[e]="",fe.trigger(this._element,"shown.bs.collapse")},this._element,!0),this._element.style[e]=this._element[i]+"px"}hide(){if(this._isTransitioning||!this._isShown())return;if(fe.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=this._element.getBoundingClientRect()[t]+"px",Vt(this._element),this._element.classList.add("collapsing"),this._element.classList.remove("collapse","show");for(const t of this._triggerArray){const e=Mt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0;this._element.style[t]="",this._queueCallback(()=>{this._isTransitioning=!1,this._element.classList.remove("collapsing"),this._element.classList.add("collapse"),fe.trigger(this._element,"hidden.bs.collapse")},this._element,!0)}_isShown(t=this._element){return t.classList.contains("show")}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ft(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren('[data-bs-toggle="collapse"]');for(const e of t){const t=Mt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=Te.find(":scope .collapse .collapse",this._config.parent);return Te.find(t,this._config.parent).filter(t=>!e.includes(t))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=je.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}fe.on(document,"click.bs.collapse.data-api",'[data-bs-toggle="collapse"]',(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=jt(this),i=Te.find(e);for(const t of i)je.getOrCreateInstance(t,{toggle:!1}).toggle()})),Xt(je);const Me="ArrowUp",He="ArrowDown",We='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Fe=Qt()?"top-end":"top-start",Be=Qt()?"top-start":"top-end",ze=Qt()?"bottom-end":"bottom-start",qe=Qt()?"bottom-start":"bottom-end",Re=Qt()?"left-start":"right-start",Ve=Qt()?"right-start":"left-start",$e={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ke={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Qe extends we{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=Te.next(this._element,".dropdown-menu")[0]||Te.prev(this._element,".dropdown-menu")[0]||Te.findOne(".dropdown-menu",this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return $e}static get DefaultType(){return Ke}static get NAME(){return"dropdown"}toggle(){return this._isShown()?this.hide():this.show()}show(){if(zt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!fe.trigger(this._element,"show.bs.dropdown",t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add("show"),this._element.classList.add("show"),fe.trigger(this._element,"shown.bs.dropdown",t)}}hide(){if(zt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!fe.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._popper&&this._popper.destroy(),this._menu.classList.remove("show"),this._element.classList.remove("show"),this._element.setAttribute("aria-expanded","false"),ve.removeDataAttribute(this._menu,"popper"),fe.trigger(this._element,"hidden.bs.dropdown",t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Wt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError("dropdown".toUpperCase()+': Option "reference" provided type "object" without a required "getBoundingClientRect" method.');return t}_createPopper(){if(void 0===n)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Wt(this._config.reference)?t=Ft(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=It(t,this._menu,e)}_isShown(){return this._menu.classList.contains("show")}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Re;if(t.classList.contains("dropstart"))return Ve;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Be:Fe:e?qe:ze}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(ve.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=Te.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(t=>Bt(t));i.length&&Gt(i,e,t===He,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Qe.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=Te.find('[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled).show');for(const i of e){const e=Qe.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Me,He].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(We)?this:Te.prev(this,We)[0]||Te.next(this,We)[0]||Te.findOne(We,t.delegateTarget.parentNode),o=Qe.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}fe.on(document,"keydown.bs.dropdown.data-api",We,Qe.dataApiKeydownHandler),fe.on(document,"keydown.bs.dropdown.data-api",".dropdown-menu",Qe.dataApiKeydownHandler),fe.on(document,"click.bs.dropdown.data-api",Qe.clearMenus),fe.on(document,"keyup.bs.dropdown.data-api",Qe.clearMenus),fe.on(document,"click.bs.dropdown.data-api",We,(function(t){t.preventDefault(),Qe.getOrCreateInstance(this).toggle()})),Xt(Qe);class Xe{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"padding-right",e=>e+t),this._setElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","padding-right",e=>e+t),this._setElementAttributes(".sticky-top","margin-right",e=>e-t)}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"padding-right"),this._resetElementAttributes(".fixed-top, .fixed-bottom, .is-fixed, .sticky-top","padding-right"),this._resetElementAttributes(".sticky-top","margin-right")}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,i(Number.parseFloat(s))+"px")})}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&ve.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,t=>{const i=ve.getDataAttribute(t,e);null!==i?(ve.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)})}_applyManipulationCallback(t,e){if(Wt(t))e(t);else for(const i of Te.find(t,this._element))e(i)}}const Ye={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Ue={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ge extends ye{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Ye}static get DefaultType(){return Ue}static get NAME(){return"backdrop"}show(t){if(!this._config.isVisible)return void Yt(t);this._append();const e=this._getElement();this._config.isAnimated&&Vt(e),e.classList.add("show"),this._emulateAnimation(()=>{Yt(t)})}hide(t){this._config.isVisible?(this._getElement().classList.remove("show"),this._emulateAnimation(()=>{this.dispose(),Yt(t)})):Yt(t)}dispose(){this._isAppended&&(fe.off(this._element,"mousedown.bs.backdrop"),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ft(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),fe.on(t,"mousedown.bs.backdrop",()=>{Yt(this._config.clickCallback)}),this._isAppended=!0}_emulateAnimation(t){Ut(t,this._getElement(),this._config.isAnimated)}}const Je={autofocus:!0,trapElement:null},Ze={autofocus:"boolean",trapElement:"element"};class ti extends ye{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return Je}static get DefaultType(){return Ze}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),fe.off(document,".bs.focustrap"),fe.on(document,"focusin.bs.focustrap",t=>this._handleFocusin(t)),fe.on(document,"keydown.tab.bs.focustrap",t=>this._handleKeydown(t)),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,fe.off(document,".bs.focustrap"))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=Te.focusableChildren(e);0===i.length?e.focus():"backward"===this._lastTabNavDirection?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?"backward":"forward")}}const ei={backdrop:!0,focus:!0,keyboard:!0},ii={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class ni extends we{constructor(t,e){super(t,e),this._dialog=Te.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Xe,this._addEventListeners()}static get Default(){return ei}static get DefaultType(){return ii}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){if(this._isShown||this._isTransitioning)return;fe.trigger(this._element,"show.bs.modal",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add("modal-open"),this._adjustDialog(),this._backdrop.show(()=>this._showElement(t)))}hide(){if(!this._isShown||this._isTransitioning)return;fe.trigger(this._element,"hide.bs.modal").defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove("show"),this._queueCallback(()=>this._hideModal(),this._element,this._isAnimated()))}dispose(){for(const t of[window,this._dialog])fe.off(t,".bs.modal");this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ge({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new ti({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=Te.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),Vt(this._element),this._element.classList.add("show");this._queueCallback(()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,fe.trigger(this._element,"shown.bs.modal",{relatedTarget:t})},this._dialog,this._isAnimated())}_addEventListeners(){fe.on(this._element,"keydown.dismiss.bs.modal",t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()}),fe.on(window,"resize.bs.modal",()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()}),fe.on(this._element,"mousedown.dismiss.bs.modal",t=>{fe.one(this._element,"click.dismiss.bs.modal",e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())})})}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide(()=>{document.body.classList.remove("modal-open"),this._resetAdjustments(),this._scrollBar.reset(),fe.trigger(this._element,"hidden.bs.modal")})}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(fe.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains("modal-static")||(t||(this._element.style.overflowY="hidden"),this._element.classList.add("modal-static"),this._queueCallback(()=>{this._element.classList.remove("modal-static"),this._queueCallback(()=>{this._element.style.overflowY=e},this._dialog)},this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Qt()?"paddingLeft":"paddingRight";this._element.style[t]=e+"px"}if(!i&&t){const t=Qt()?"paddingRight":"paddingLeft";this._element.style[t]=e+"px"}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=ni.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}fe.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=Mt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),fe.one(e,"show.bs.modal",t=>{t.defaultPrevented||fe.one(e,"hidden.bs.modal",()=>{Bt(this)&&this.focus()})});const i=Te.findOne(".modal.show");i&&ni.getInstance(i).hide();ni.getOrCreateInstance(e).toggle(this)})),Ae(ni),Xt(ni);const si=".bs.offcanvas",oi=`load${si}.data-api`,ri=`click${si}.data-api`,ai={backdrop:!0,keyboard:!0,scroll:!1},li={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class ci extends we{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return ai}static get DefaultType(){return li}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){if(this._isShown)return;if(fe.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented)return;this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Xe).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add("showing");this._queueCallback(()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add("show"),this._element.classList.remove("showing"),fe.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})},this._element,!0)}hide(){if(!this._isShown)return;if(fe.trigger(this._element,"hide.bs.offcanvas").defaultPrevented)return;this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add("hiding"),this._backdrop.hide();this._queueCallback(()=>{this._element.classList.remove("show","hiding"),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Xe).reset(),fe.trigger(this._element,"hidden.bs.offcanvas")},this._element,!0)}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ge({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():fe.trigger(this._element,"hidePrevented.bs.offcanvas")}:null})}_initializeFocusTrap(){return new ti({trapElement:this._element})}_addEventListeners(){fe.on(this._element,"keydown.dismiss.bs.offcanvas",t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():fe.trigger(this._element,"hidePrevented.bs.offcanvas"))})}static jQueryInterface(t){return this.each((function(){const e=ci.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}fe.on(document,ri,'[data-bs-toggle="offcanvas"]',(function(t){const e=Mt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),zt(this))return;fe.one(e,"hidden.bs.offcanvas",()=>{Bt(this)&&this.focus()});const i=Te.findOne(".offcanvas.show");i&&i!==e&&ci.getInstance(i).hide();ci.getOrCreateInstance(e).toggle(this)})),fe.on(window,oi,()=>{for(const t of Te.find(".offcanvas.show"))ci.getOrCreateInstance(t).show()}),fe.on(window,"resize.bs.offcanvas",()=>{for(const t of Te.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&ci.getOrCreateInstance(t).hide()}),Ae(ci),Xt(ci);const ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),hi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,di=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,fi=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!ui.has(i)||Boolean(hi.test(t.nodeValue)||di.test(t.nodeValue)):e.filter(t=>t instanceof RegExp).some(t=>t.test(i))},pi={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]};const gi={allowList:pi,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},mi={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},_i={entry:"(string|element|function|null)",selector:"(string|element)"};class bi extends ye{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return gi}static get DefaultType(){return mi}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map(t=>this._resolvePossibleFunction(t)).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},_i)}_setContent(t,e,i){const n=Te.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Wt(e)?this._putElementInTemplate(Ft(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new 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t=fe.trigger(this._element,this.constructor.eventName("show")),e=(qt(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this.tip&&(this.tip.remove(),this.tip=null);const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),fe.trigger(this._element,this.constructor.eventName("inserted"))),this._popper?this._popper.update():this._popper=this._createPopper(i),i.classList.add("show"),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.on(t,"mouseover",Rt);this._queueCallback(()=>{fe.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1},this.tip,this._isAnimated())}hide(){if(!this._isShown())return;if(fe.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented)return;const t=this._getTipElement();if(t.classList.remove("show"),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))fe.off(t,"mouseover",Rt);this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1,this._isHovered=null;this._queueCallback(()=>{this._isWithActiveTrigger()||(this._isHovered||t.remove(),this._element.removeAttribute("aria-describedby"),fe.trigger(this._element,this.constructor.eventName("hidden")),this._disposePopper())},this.tip,this._isAnimated())}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove("fade","show"),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add("fade"),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new bi({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains("fade")}_isShown(){return this.tip&&this.tip.classList.contains("show")}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=yi[e.toUpperCase()];return It(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map(t=>Number.parseInt(t,10)):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout(()=>{this._isHovered&&this.show()},this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout(()=>{this._isHovered||this.hide()},this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=ve.getDataAttributes(this._element);for(const t of Object.keys(e))vi.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ft(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=Ei.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Xt(Ei);const Ci={...Ei.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},Ti={...Ei.DefaultType,content:"(null|string|element|function)"};class Oi extends Ei{static get Default(){return Ci}static get DefaultType(){return Ti}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=Oi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Xt(Oi);const xi={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},ki={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Li extends we{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new 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t.threshold&&(t.threshold=t.threshold.split(",").map(t=>Number.parseFloat(t))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(fe.off(this._config.target,"click.bs.scrollspy"),fe.on(this._config.target,"click.bs.scrollspy","[href]",t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}}))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver(t=>this._observerCallback(t),t)}_observerCallback(t){const e=t=>this._targetLinks.get("#"+t.target.id),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=Te.find("[href]",this._config.target);for(const e of t){if(!e.hash||zt(e))continue;const t=Te.findOne(e.hash,this._element);Bt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add("active"),this._activateParents(t),fe.trigger(this._element,"activate.bs.scrollspy",{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))Te.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add("active");else for(const e of Te.parents(t,".nav, .list-group"))for(const t of Te.prev(e,".nav-link, .nav-item > .nav-link, .list-group-item"))t.classList.add("active")}_clearActiveClass(t){t.classList.remove("active");const e=Te.find("[href].active",t);for(const t of e)t.classList.remove("active")}static jQueryInterface(t){return this.each((function(){const e=Li.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(window,"load.bs.scrollspy.data-api",()=>{for(const t of Te.find('[data-bs-spy="scroll"]'))Li.getOrCreateInstance(t)}),Xt(Li);const Di="ArrowLeft",Si="ArrowRight",Ii="ArrowUp",Ni="ArrowDown",Pi='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',ji='.nav-link:not(.dropdown-toggle), .list-group-item:not(.dropdown-toggle), [role="tab"]:not(.dropdown-toggle), '+Pi;class Mi extends we{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, 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e=this._getActiveElem(),i=e?fe.trigger(e,"hide.bs.tab",{relatedTarget:t}):null;fe.trigger(t,"show.bs.tab",{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){if(!t)return;t.classList.add("active"),this._activate(Mt(t));this._queueCallback(()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),fe.trigger(t,"shown.bs.tab",{relatedTarget:e})):t.classList.add("show")},t,t.classList.contains("fade"))}_deactivate(t,e){if(!t)return;t.classList.remove("active"),t.blur(),this._deactivate(Mt(t));this._queueCallback(()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),fe.trigger(t,"hidden.bs.tab",{relatedTarget:e})):t.classList.remove("show")},t,t.classList.contains("fade"))}_keydown(t){if(![Di,Si,Ii,Ni].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Si,Ni].includes(t.key),i=Gt(this._getChildren().filter(t=>!zt(t)),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Mi.getOrCreateInstance(i).show())}_getChildren(){return Te.find(ji,this._parent)}_getActiveElem(){return this._getChildren().find(t=>this._elemIsActive(t))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Mt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby","#"+t.id))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=Te.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle","active"),n(".dropdown-menu","show"),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains("active")}_getInnerElement(t){return t.matches(ji)?t:Te.findOne(ji,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Mi.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}fe.on(document,"click.bs.tab",Pi,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),zt(this)||Mi.getOrCreateInstance(this).show()})),fe.on(window,"load.bs.tab",()=>{for(const t of Te.find('.active[data-bs-toggle="tab"], .active[data-bs-toggle="pill"], .active[data-bs-toggle="list"]'))Mi.getOrCreateInstance(t)}),Xt(Mi);const Hi={animation:"boolean",autohide:"boolean",delay:"number"},Wi={animation:!0,autohide:!0,delay:5e3};class Fi extends we{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return Wi}static get DefaultType(){return Hi}static get NAME(){return"toast"}show(){if(fe.trigger(this._element,"show.bs.toast").defaultPrevented)return;this._clearTimeout(),this._config.animation&&this._element.classList.add("fade");this._element.classList.remove("hide"),Vt(this._element),this._element.classList.add("show","showing"),this._queueCallback(()=>{this._element.classList.remove("showing"),fe.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()},this._element,this._config.animation)}hide(){if(!this.isShown())return;if(fe.trigger(this._element,"hide.bs.toast").defaultPrevented)return;this._element.classList.add("showing"),this._queueCallback(()=>{this._element.classList.add("hide"),this._element.classList.remove("showing","show"),fe.trigger(this._element,"hidden.bs.toast")},this._element,this._config.animation)}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove("show"),super.dispose()}isShown(){return 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\ No newline at end of file diff --git a/_static/searchtools.js b/_static/searchtools.js new file mode 100644 index 000000000..e89e34d4e --- /dev/null +++ b/_static/searchtools.js @@ -0,0 +1,566 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = docUrlRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = docUrlRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 000000000..aae669d7e --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,144 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '<p class="highlight-link">' + + '<a href="javascript:SphinxHighlight.hideSearchWords()">' + + _("Hide Search Matches") + + "</a></p>" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(SphinxHighlight.highlightSearchWords); +_ready(SphinxHighlight.initEscapeListener); diff --git a/_static/styles/bootstrap.css b/_static/styles/bootstrap.css new file mode 100644 index 000000000..b258fd9ea --- /dev/null +++ b/_static/styles/bootstrap.css @@ -0,0 +1,6 @@ +/*! + * Bootstrap 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button.copybtn.o-tooltip--left:after{background-color:var(--pst-color-surface);color:var(--pst-color-text)}#ethical-ad-placement .ethical-footer a,#ethical-ad-placement .ethical-footer a:active,#ethical-ad-placement .ethical-footer a:hover,#ethical-ad-placement .ethical-footer a:visited,#ethical-ad-placement .ethical-sidebar a,#ethical-ad-placement .ethical-sidebar a:active,#ethical-ad-placement .ethical-sidebar a:hover,#ethical-ad-placement .ethical-sidebar a:visited{color:var(--pst-color-text-base)}#ethical-ad-placement .ethical-footer,#ethical-ad-placement .ethical-sidebar{background-color:var(--pst-color-background);border:1px solid var(--pst-color-border);border-radius:5px;color:var(--pst-color-text-base);font-size:14px;line-height:20px}.bd-content div.jupyter_container{background-color:unset;border:none;box-shadow:none}.bd-content div.jupyter_container div.highlight,.bd-content div.jupyter_container div.output{border-radius:.25rem}.bd-content div.jupyter_container div.highlight{background-color:var(--pst-color-surface)}.bd-content div.jupyter_container .cell_input,.bd-content div.jupyter_container .cell_output{border-radius:.25rem}.bd-content div.jupyter_container .cell_input pre,.bd-content div.jupyter_container .cell_output pre{padding:1rem}.xr-wrap[hidden]{display:block!important}@use "../variables/color" as *;html[data-theme=light]{--pst-color-primary:#459db9;--pst-color-primary-text:#fff;--pst-color-primary-highlight:#306e81;--sd-color-primary:var(--pst-color-primary);--sd-color-primary-text:var(--pst-color-primary-text);--sd-color-primary-highlight:var(--pst-color-primary-highlight);--pst-color-secondary:#ee9040;--pst-color-secondary-text:#fff;--pst-color-secondary-highlight:#cf6912;--sd-color-secondary:var(--pst-color-secondary);--sd-color-secondary-text:var(--pst-color-secondary-text);--sd-color-secondary-highlight:var(--pst-color-secondary-highlight);--pst-color-success:#28a745;--pst-color-success-text:#fff;--pst-color-success-highlight:#19692c;--sd-color-success:var(--pst-color-success);--sd-color-success-text:var(--pst-color-success-text);--sd-color-success-highlight:var(--pst-color-success-highlight);--pst-color-info:#459db9;--pst-color-info-text:#fff;--pst-color-info-highlight:#306e81;--sd-color-info:var(--pst-color-info);--sd-color-info-text:var(--pst-color-info-text);--sd-color-info-highlight:var(--pst-color-info-highlight);--pst-color-warning:#ee9040;--pst-color-warning-text:#fff;--pst-color-warning-highlight:#cf6912;--sd-color-warning:var(--pst-color-warning);--sd-color-warning-text:var(--pst-color-warning-text);--sd-color-warning-highlight:var(--pst-color-warning-highlight);--pst-color-danger:#dc3545;--pst-color-danger-text:#fff;--pst-color-danger-highlight:#a71d2a;--sd-color-danger:var(--pst-color-danger);--sd-color-danger-text:var(--pst-color-danger-text);--sd-color-danger-highlight:var(--pst-color-danger-highlight);--pst-color-light:#c9c9c9;--pst-color-light-text:#000;--pst-color-light-highlight:#a3a3a3;--sd-color-light:var(--pst-color-light);--sd-color-light-text:var(--pst-color-light-text);--sd-color-light-highlight:var(--pst-color-light-highlight);--pst-color-muted:#646464;--pst-color-muted-text:#fff;--pst-color-muted-highlight:#3e3e3e;--sd-color-muted:var(--pst-color-muted);--sd-color-muted-text:var(--pst-color-muted-text);--sd-color-muted-highlight:var(--pst-color-muted-highlight);--pst-color-dark:#323232;--pst-color-dark-text:#fff;--pst-color-dark-highlight:#0c0c0c;--sd-color-dark:var(--pst-color-dark);--sd-color-dark-text:var(--pst-color-dark-text);--sd-color-dark-highlight:var(--pst-color-dark-highlight);--pst-color-black:#000;--pst-color-black-text:#fff;--pst-color-black-highlight:#000;--sd-color-black:var(--pst-color-black);--sd-color-black-text:var(--pst-color-black-text);--sd-color-black-highlight:var(--pst-color-black-highlight);--pst-color-white:#fff;--pst-color-white-text:#000;--pst-color-white-highlight:#d9d9d9;--sd-color-white:var(--pst-color-white);--sd-color-white-text:var(--pst-color-white-text);--sd-color-white-highlight:var(--pst-color-white-highlight)}html[data-theme=dark]{--pst-color-primary:#459db9;--pst-color-primary-text:#fff;--pst-color-primary-highlight:#306e81;--sd-color-primary:var(--pst-color-primary);--sd-color-primary-text:var(--pst-color-primary-text);--sd-color-primary-highlight:var(--pst-color-primary-highlight);--pst-color-secondary:#ee9040;--pst-color-secondary-text:#fff;--pst-color-secondary-highlight:#cf6912;--sd-color-secondary:var(--pst-color-secondary);--sd-color-secondary-text:var(--pst-color-secondary-text);--sd-color-secondary-highlight:var(--pst-color-secondary-highlight);--pst-color-success:#488757;--pst-color-success-text:#fff;--pst-color-success-highlight:#2d5537;--sd-color-success:var(--pst-color-success);--sd-color-success-text:var(--pst-color-success-text);--sd-color-success-highlight:var(--pst-color-success-highlight);--pst-color-info:#459db9;--pst-color-info-text:#fff;--pst-color-info-highlight:#306e81;--sd-color-info:var(--pst-color-info);--sd-color-info-text:var(--pst-color-info-text);--sd-color-info-highlight:var(--pst-color-info-highlight);--pst-color-warning:#ee9040;--pst-color-warning-text:#fff;--pst-color-warning-highlight:#cf6912;--sd-color-warning:var(--pst-color-warning);--sd-color-warning-text:var(--pst-color-warning-text);--sd-color-warning-highlight:var(--pst-color-warning-highlight);--pst-color-danger:#cb4653;--pst-color-danger-text:#fff;--pst-color-danger-highlight:#992b36;--sd-color-danger:var(--pst-color-danger);--sd-color-danger-text:var(--pst-color-danger-text);--sd-color-danger-highlight:var(--pst-color-danger-highlight);--pst-color-light:#c9c9c9;--pst-color-light-text:#000;--pst-color-light-highlight:#a3a3a3;--sd-color-light:var(--pst-color-light);--sd-color-light-text:var(--pst-color-light-text);--sd-color-light-highlight:var(--pst-color-light-highlight);--pst-color-muted:#a6a6a6;--pst-color-muted-text:#fff;--pst-color-muted-highlight:gray;--sd-color-muted:var(--pst-color-muted);--sd-color-muted-text:var(--pst-color-muted-text);--sd-color-muted-highlight:var(--pst-color-muted-highlight);--pst-color-dark:#cecece;--pst-color-dark-text:#000;--pst-color-dark-highlight:#a8a8a8;--sd-color-dark:var(--pst-color-dark);--sd-color-dark-text:var(--pst-color-dark-text);--sd-color-dark-highlight:var(--pst-color-dark-highlight);--pst-color-black:#000;--pst-color-black-text:#fff;--pst-color-black-highlight:#000;--sd-color-black:var(--pst-color-black);--sd-color-black-text:var(--pst-color-black-text);--sd-color-black-highlight:var(--pst-color-black-highlight);--pst-color-white:#fff;--pst-color-white-text:#000;--pst-color-white-highlight:#d9d9d9;--sd-color-white:var(--pst-color-white);--sd-color-white-text:var(--pst-color-white-text);--sd-color-white-highlight:var(--pst-color-white-highlight)}html[data-theme=dark],html[data-theme=light]{--sd-color-card-border:var(--pst-color-border)}html[data-theme=light] .sd-shadow-lg,html[data-theme=light] .sd-shadow-md,html[data-theme=light] .sd-shadow-sm,html[data-theme=light] .sd-shadow-xs{box-shadow:0 .2rem .5rem var(--pst-color-shadow),0 0 .0625rem var(--pst-color-shadow)!important}.bd-content .sd-card{border:1px solid var(--pst-color-border)}.bd-content .sd-card .sd-card-header{background-color:var(--pst-color-panel-background);border-bottom:1px solid var(--pst-color-border)}.bd-content .sd-card .sd-card-footer{border-top:1px solid var(--pst-color-border)}.bd-content .sd-card .sd-card-body,.bd-content .sd-card .sd-card-footer{background-color:var(--pst-color-panel-background)}.bd-content .sd-tab-set>input:checked+label,.bd-content .sd-tab-set>input:not(:checked)+label:hover{border-color:var(--pst-color-primary);color:var(--pst-color-primary)}.bd-content .sd-tab-set>input:not(:checked)+label:hover{opacity:.5}.bd-content .sd-tab-set>label{color:var(--pst-color-text-muted)}html .bd-content .sd-tab-set>label:hover{border-color:var(--pst-color-primary);color:var(--pst-color-primary);opacity:.5}details.sd-dropdown{border:0!important;box-shadow:0 .2rem .5rem var(--pst-color-shadow),0 0 .0625rem var(--pst-color-shadow)!important}details.sd-dropdown summary.sd-card-header{border:0!important}details.sd-dropdown summary.sd-card-header+div.sd-summary-content{border:0}details.sd-dropdown summary.sd-card-header{align-items:center;background-color:unset!important;border-left:.2rem solid var(--pst-sd-dropdown-color)!important;color:var(--pst-color-text)!important;display:flex;font-weight:600;padding-bottom:.5rem;padding-top:.5rem;position:relative}details.sd-dropdown summary.sd-card-header,details.sd-dropdown summary.sd-card-header+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-card-border)}details.sd-dropdown summary.sd-card-header.sd-bg-primary,details.sd-dropdown summary.sd-card-header.sd-bg-primary+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-primary)}details.sd-dropdown summary.sd-card-header.sd-bg-secondary,details.sd-dropdown summary.sd-card-header.sd-bg-secondary+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-secondary)}details.sd-dropdown summary.sd-card-header.sd-bg-success,details.sd-dropdown summary.sd-card-header.sd-bg-success+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-success)}details.sd-dropdown summary.sd-card-header.sd-bg-info,details.sd-dropdown summary.sd-card-header.sd-bg-info+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-info)}details.sd-dropdown summary.sd-card-header.sd-bg-warning,details.sd-dropdown summary.sd-card-header.sd-bg-warning+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-warning)}details.sd-dropdown summary.sd-card-header.sd-bg-danger,details.sd-dropdown summary.sd-card-header.sd-bg-danger+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-danger)}details.sd-dropdown summary.sd-card-header.sd-bg-light,details.sd-dropdown summary.sd-card-header.sd-bg-light+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-light)}details.sd-dropdown summary.sd-card-header.sd-bg-muted,details.sd-dropdown summary.sd-card-header.sd-bg-muted+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-muted)}details.sd-dropdown summary.sd-card-header.sd-bg-dark,details.sd-dropdown summary.sd-card-header.sd-bg-dark+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-dark)}details.sd-dropdown summary.sd-card-header.sd-bg-black,details.sd-dropdown summary.sd-card-header.sd-bg-black+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-black)}details.sd-dropdown summary.sd-card-header.sd-bg-white,details.sd-dropdown summary.sd-card-header.sd-bg-white+div.sd-summary-content{--pst-sd-dropdown-color:var(--sd-color-white)}details.sd-dropdown summary.sd-card-header:before{background-color:var(--pst-sd-dropdown-color);content:"";height:100%;left:0;opacity:.1;pointer-events:none;position:absolute;top:0;width:100%}details.sd-dropdown summary.sd-card-header+div.sd-summary-content{border-bottom-left-radius:calc(.25rem - 1px);border-left:.2rem solid var(--pst-sd-dropdown-color)!important}details.sd-dropdown summary.sd-card-header span.sd-summary-icon{align-items:center;color:var(--pst-sd-dropdown-color)!important;display:inline-flex}details.sd-dropdown summary.sd-card-header .sd-summary-down,details.sd-dropdown summary.sd-card-header .sd-summary-up{top:.7rem}.bd-content .admonition button.toggle-button{color:inherit}.bd-content details.toggle-details summary{border-left:3px solid var(--pst-color-primary)}html div.rendered_html html .jp-RenderedHTMLCommon table{table-layout:auto}html[data-theme=dark] .bd-content .nboutput .output_area.rendered_html{background-color:var(--pst-color-text-base);border-radius:.25rem;color:var(--pst-color-on-background);padding:.5rem}html[data-theme=dark] .bd-content .nboutput .output_area.stderr{background:var(--pst-color-danger)}div.nblast.container{margin-bottom:1rem}div.cell_output .output{max-width:100%;overflow-x:auto}div.cell_output .output::-webkit-scrollbar{height:.5rem;width:.5rem}div.cell_output .output::-webkit-scrollbar-thumb{background:var(--pst-color-text-muted);border-radius:.25rem}div.cell_output .output::-webkit-scrollbar-track{background:transparent}div.cell_output .output::-webkit-scrollbar-thumb{background:var(--pst-color-on-surface)}div.cell_output .output::-webkit-scrollbar-thumb:hover,div.cell_output .output:hover::-webkit-scrollbar-thumb{background:var(--pst-color-text-muted)}html[data-theme=dark] .bd-content div.cell_output .text_html,html[data-theme=dark] .bd-content div.cell_output img{background-color:var(--pst-color-text-base);border-radius:.25rem;color:var(--pst-color-on-background);padding:.5rem}.bd-content div.cell_input{display:flex;flex-direction:column;justify-content:stretch}.bd-content div.cell_input,.bd-content div.output{border-radius:.25rem}.bd-content div.output table{table-layout:auto}html[data-theme=dark] .bd-content img.leaflet-tile.leaflet-tile-loaded{border-radius:0;padding:0}.bd-search-container div#search-results>h2{font-size:var(--pst-font-size-icon);margin-top:0}.bd-search-container div#search-results p.search-summary{color:var(--pst-color-text-muted)}.bd-search-container ul.search{list-style:none;margin:0}.bd-search-container ul.search li{background-image:none;border-top:1px solid var(--pst-color-text-muted);margin:1rem 0;padding:1rem 0}.bd-search-container ul.search li>a{font-size:1.2em}.bd-search-container ul.search li div.context,.bd-search-container ul.search li p.context{color:var(--pst-color-text-base);margin:.5em 0 0}.bd-search-container ul.search li div.context a:before,.bd-search-container ul.search li p.context a:before{color:var(--pst-color-text-muted);content:"#";padding-right:.2em} \ No newline at end of file diff --git a/_static/styles/theme.css b/_static/styles/theme.css new file mode 100644 index 000000000..4519dd912 --- /dev/null +++ b/_static/styles/theme.css @@ -0,0 +1,2 @@ +/* Provided by Sphinx's 'basic' theme, and included in the final set of assets */ +@import "../basic.css"; diff --git a/_static/underscore-1.13.1.js b/_static/underscore-1.13.1.js new file mode 100644 index 000000000..ffd77af96 --- /dev/null +++ b/_static/underscore-1.13.1.js @@ -0,0 +1,2042 @@ +(function (global, factory) { + typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : + typeof define === 'function' && define.amd ? define('underscore', factory) : + (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () { + var current = global._; + var exports = global._ = factory(); + exports.noConflict = function () { global._ = current; return exports; }; + }())); +}(this, (function () { + // Underscore.js 1.13.1 + // https://underscorejs.org + // (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors + // Underscore may be freely distributed under the MIT license. + + // Current version. + var VERSION = '1.13.1'; + + // Establish the root object, `window` (`self`) in the browser, `global` + // on the server, or `this` in some virtual machines. We use `self` + // instead of `window` for `WebWorker` support. + var root = typeof self == 'object' && self.self === self && self || + typeof global == 'object' && global.global === global && global || + Function('return this')() || + {}; + + // Save bytes in the minified (but not gzipped) version: + var ArrayProto = Array.prototype, ObjProto = Object.prototype; + var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null; + + // Create quick reference variables for speed access to core prototypes. + var push = ArrayProto.push, + slice = ArrayProto.slice, + toString = ObjProto.toString, + hasOwnProperty = ObjProto.hasOwnProperty; + + // Modern feature detection. + var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined', + supportsDataView = typeof DataView !== 'undefined'; + + // All **ECMAScript 5+** native function implementations that we hope to use + // are declared here. + var nativeIsArray = Array.isArray, + nativeKeys = Object.keys, + nativeCreate = Object.create, + nativeIsView = supportsArrayBuffer && ArrayBuffer.isView; + + // Create references to these builtin functions because we override them. + var _isNaN = isNaN, + _isFinite = isFinite; + + // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed. + var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString'); + var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString', + 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString']; + + // The largest integer that can be represented exactly. + var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1; + + // Some functions take a variable number of arguments, or a few expected + // arguments at the beginning and then a variable number of values to operate + // on. This helper accumulates all remaining arguments past the function’s + // argument length (or an explicit `startIndex`), into an array that becomes + // the last argument. Similar to ES6’s "rest parameter". + function restArguments(func, startIndex) { + startIndex = startIndex == null ? func.length - 1 : +startIndex; + return function() { + var length = Math.max(arguments.length - startIndex, 0), + rest = Array(length), + index = 0; + for (; index < length; index++) { + rest[index] = arguments[index + startIndex]; + } + switch (startIndex) { + case 0: return func.call(this, rest); + case 1: return func.call(this, arguments[0], rest); + case 2: return func.call(this, arguments[0], arguments[1], rest); + } + var args = Array(startIndex + 1); + for (index = 0; index < startIndex; index++) { + args[index] = arguments[index]; + } + args[startIndex] = rest; + return func.apply(this, args); + }; + } + + // Is a given variable an object? + function isObject(obj) { + var type = typeof obj; + return type === 'function' || type === 'object' && !!obj; + } + + // Is a given value equal to null? + function isNull(obj) { + return obj === null; + } + + // Is a given variable undefined? + function isUndefined(obj) { + return obj === void 0; + } + + // Is a given value a boolean? + function isBoolean(obj) { + return obj === true || obj === false || toString.call(obj) === '[object Boolean]'; + } + + // Is a given value a DOM element? + function isElement(obj) { + return !!(obj && obj.nodeType === 1); + } + + // Internal function for creating a `toString`-based type tester. + function tagTester(name) { + var tag = '[object ' + name + ']'; + return function(obj) { + return toString.call(obj) === tag; + }; + } + + var isString = tagTester('String'); + + var isNumber = tagTester('Number'); + + var isDate = tagTester('Date'); + + var isRegExp = tagTester('RegExp'); + + var isError = tagTester('Error'); + + var isSymbol = tagTester('Symbol'); + + var isArrayBuffer = tagTester('ArrayBuffer'); + + var isFunction = tagTester('Function'); + + // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old + // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236). + var nodelist = root.document && root.document.childNodes; + if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') { + isFunction = function(obj) { + return typeof obj == 'function' || false; + }; + } + + var isFunction$1 = isFunction; + + var hasObjectTag = tagTester('Object'); + + // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`. + // In IE 11, the most common among them, this problem also applies to + // `Map`, `WeakMap` and `Set`. + var hasStringTagBug = ( + supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8))) + ), + isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map)); + + var isDataView = tagTester('DataView'); + + // In IE 10 - Edge 13, we need a different heuristic + // to determine whether an object is a `DataView`. + function ie10IsDataView(obj) { + return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer); + } + + var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView); + + // Is a given value an array? + // Delegates to ECMA5's native `Array.isArray`. + var isArray = nativeIsArray || tagTester('Array'); + + // Internal function to check whether `key` is an own property name of `obj`. + function has$1(obj, key) { + return obj != null && hasOwnProperty.call(obj, key); + } + + var isArguments = tagTester('Arguments'); + + // Define a fallback version of the method in browsers (ahem, IE < 9), where + // there isn't any inspectable "Arguments" type. + (function() { + if (!isArguments(arguments)) { + isArguments = function(obj) { + return has$1(obj, 'callee'); + }; + } + }()); + + var isArguments$1 = isArguments; + + // Is a given object a finite number? + function isFinite$1(obj) { + return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj)); + } + + // Is the given value `NaN`? + function isNaN$1(obj) { + return isNumber(obj) && _isNaN(obj); + } + + // Predicate-generating function. Often useful outside of Underscore. + function constant(value) { + return function() { + return value; + }; + } + + // Common internal logic for `isArrayLike` and `isBufferLike`. + function createSizePropertyCheck(getSizeProperty) { + return function(collection) { + var sizeProperty = getSizeProperty(collection); + return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX; + } + } + + // Internal helper to generate a function to obtain property `key` from `obj`. + function shallowProperty(key) { + return function(obj) { + return obj == null ? void 0 : obj[key]; + }; + } + + // Internal helper to obtain the `byteLength` property of an object. + var getByteLength = shallowProperty('byteLength'); + + // Internal helper to determine whether we should spend extensive checks against + // `ArrayBuffer` et al. + var isBufferLike = createSizePropertyCheck(getByteLength); + + // Is a given value a typed array? + var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/; + function isTypedArray(obj) { + // `ArrayBuffer.isView` is the most future-proof, so use it when available. + // Otherwise, fall back on the above regular expression. + return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) : + isBufferLike(obj) && typedArrayPattern.test(toString.call(obj)); + } + + var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false); + + // Internal helper to obtain the `length` property of an object. + var getLength = shallowProperty('length'); + + // Internal helper to create a simple lookup structure. + // `collectNonEnumProps` used to depend on `_.contains`, but this led to + // circular imports. `emulatedSet` is a one-off solution that only works for + // arrays of strings. + function emulatedSet(keys) { + var hash = {}; + for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true; + return { + contains: function(key) { return hash[key]; }, + push: function(key) { + hash[key] = true; + return keys.push(key); + } + }; + } + + // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't + // be iterated by `for key in ...` and thus missed. Extends `keys` in place if + // needed. + function collectNonEnumProps(obj, keys) { + keys = emulatedSet(keys); + var nonEnumIdx = nonEnumerableProps.length; + var constructor = obj.constructor; + var proto = isFunction$1(constructor) && constructor.prototype || ObjProto; + + // Constructor is a special case. + var prop = 'constructor'; + if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop); + + while (nonEnumIdx--) { + prop = nonEnumerableProps[nonEnumIdx]; + if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) { + keys.push(prop); + } + } + } + + // Retrieve the names of an object's own properties. + // Delegates to **ECMAScript 5**'s native `Object.keys`. + function keys(obj) { + if (!isObject(obj)) return []; + if (nativeKeys) return nativeKeys(obj); + var keys = []; + for (var key in obj) if (has$1(obj, key)) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + } + + // Is a given array, string, or object empty? + // An "empty" object has no enumerable own-properties. + function isEmpty(obj) { + if (obj == null) return true; + // Skip the more expensive `toString`-based type checks if `obj` has no + // `.length`. + var length = getLength(obj); + if (typeof length == 'number' && ( + isArray(obj) || isString(obj) || isArguments$1(obj) + )) return length === 0; + return getLength(keys(obj)) === 0; + } + + // Returns whether an object has a given set of `key:value` pairs. + function isMatch(object, attrs) { + var _keys = keys(attrs), length = _keys.length; + if (object == null) return !length; + var obj = Object(object); + for (var i = 0; i < length; i++) { + var key = _keys[i]; + if (attrs[key] !== obj[key] || !(key in obj)) return false; + } + return true; + } + + // If Underscore is called as a function, it returns a wrapped object that can + // be used OO-style. This wrapper holds altered versions of all functions added + // through `_.mixin`. Wrapped objects may be chained. + function _$1(obj) { + if (obj instanceof _$1) return obj; + if (!(this instanceof _$1)) return new _$1(obj); + this._wrapped = obj; + } + + _$1.VERSION = VERSION; + + // Extracts the result from a wrapped and chained object. + _$1.prototype.value = function() { + return this._wrapped; + }; + + // Provide unwrapping proxies for some methods used in engine operations + // such as arithmetic and JSON stringification. + _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value; + + _$1.prototype.toString = function() { + return String(this._wrapped); + }; + + // Internal function to wrap or shallow-copy an ArrayBuffer, + // typed array or DataView to a new view, reusing the buffer. + function toBufferView(bufferSource) { + return new Uint8Array( + bufferSource.buffer || bufferSource, + bufferSource.byteOffset || 0, + getByteLength(bufferSource) + ); + } + + // We use this string twice, so give it a name for minification. + var tagDataView = '[object DataView]'; + + // Internal recursive comparison function for `_.isEqual`. + function eq(a, b, aStack, bStack) { + // Identical objects are equal. `0 === -0`, but they aren't identical. + // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal). + if (a === b) return a !== 0 || 1 / a === 1 / b; + // `null` or `undefined` only equal to itself (strict comparison). + if (a == null || b == null) return false; + // `NaN`s are equivalent, but non-reflexive. + if (a !== a) return b !== b; + // Exhaust primitive checks + var type = typeof a; + if (type !== 'function' && type !== 'object' && typeof b != 'object') return false; + return deepEq(a, b, aStack, bStack); + } + + // Internal recursive comparison function for `_.isEqual`. + function deepEq(a, b, aStack, bStack) { + // Unwrap any wrapped objects. + if (a instanceof _$1) a = a._wrapped; + if (b instanceof _$1) b = b._wrapped; + // Compare `[[Class]]` names. + var className = toString.call(a); + if (className !== toString.call(b)) return false; + // Work around a bug in IE 10 - Edge 13. + if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) { + if (!isDataView$1(b)) return false; + className = tagDataView; + } + switch (className) { + // These types are compared by value. + case '[object RegExp]': + // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i') + case '[object String]': + // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is + // equivalent to `new String("5")`. + return '' + a === '' + b; + case '[object Number]': + // `NaN`s are equivalent, but non-reflexive. + // Object(NaN) is equivalent to NaN. + if (+a !== +a) return +b !== +b; + // An `egal` comparison is performed for other numeric values. + return +a === 0 ? 1 / +a === 1 / b : +a === +b; + case '[object Date]': + case '[object Boolean]': + // Coerce dates and booleans to numeric primitive values. Dates are compared by their + // millisecond representations. Note that invalid dates with millisecond representations + // of `NaN` are not equivalent. + return +a === +b; + case '[object Symbol]': + return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b); + case '[object ArrayBuffer]': + case tagDataView: + // Coerce to typed array so we can fall through. + return deepEq(toBufferView(a), toBufferView(b), aStack, bStack); + } + + var areArrays = className === '[object Array]'; + if (!areArrays && isTypedArray$1(a)) { + var byteLength = getByteLength(a); + if (byteLength !== getByteLength(b)) return false; + if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true; + areArrays = true; + } + if (!areArrays) { + if (typeof a != 'object' || typeof b != 'object') return false; + + // Objects with different constructors are not equivalent, but `Object`s or `Array`s + // from different frames are. + var aCtor = a.constructor, bCtor = b.constructor; + if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor && + isFunction$1(bCtor) && bCtor instanceof bCtor) + && ('constructor' in a && 'constructor' in b)) { + return false; + } + } + // Assume equality for cyclic structures. The algorithm for detecting cyclic + // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`. + + // Initializing stack of traversed objects. + // It's done here since we only need them for objects and arrays comparison. + aStack = aStack || []; + bStack = bStack || []; + var length = aStack.length; + while (length--) { + // Linear search. Performance is inversely proportional to the number of + // unique nested structures. + if (aStack[length] === a) return bStack[length] === b; + } + + // Add the first object to the stack of traversed objects. + aStack.push(a); + bStack.push(b); + + // Recursively compare objects and arrays. + if (areArrays) { + // Compare array lengths to determine if a deep comparison is necessary. + length = a.length; + if (length !== b.length) return false; + // Deep compare the contents, ignoring non-numeric properties. + while (length--) { + if (!eq(a[length], b[length], aStack, bStack)) return false; + } + } else { + // Deep compare objects. + var _keys = keys(a), key; + length = _keys.length; + // Ensure that both objects contain the same number of properties before comparing deep equality. + if (keys(b).length !== length) return false; + while (length--) { + // Deep compare each member + key = _keys[length]; + if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false; + } + } + // Remove the first object from the stack of traversed objects. + aStack.pop(); + bStack.pop(); + return true; + } + + // Perform a deep comparison to check if two objects are equal. + function isEqual(a, b) { + return eq(a, b); + } + + // Retrieve all the enumerable property names of an object. + function allKeys(obj) { + if (!isObject(obj)) return []; + var keys = []; + for (var key in obj) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + } + + // Since the regular `Object.prototype.toString` type tests don't work for + // some types in IE 11, we use a fingerprinting heuristic instead, based + // on the methods. It's not great, but it's the best we got. + // The fingerprint method lists are defined below. + function ie11fingerprint(methods) { + var length = getLength(methods); + return function(obj) { + if (obj == null) return false; + // `Map`, `WeakMap` and `Set` have no enumerable keys. + var keys = allKeys(obj); + if (getLength(keys)) return false; + for (var i = 0; i < length; i++) { + if (!isFunction$1(obj[methods[i]])) return false; + } + // If we are testing against `WeakMap`, we need to ensure that + // `obj` doesn't have a `forEach` method in order to distinguish + // it from a regular `Map`. + return methods !== weakMapMethods || !isFunction$1(obj[forEachName]); + }; + } + + // In the interest of compact minification, we write + // each string in the fingerprints only once. + var forEachName = 'forEach', + hasName = 'has', + commonInit = ['clear', 'delete'], + mapTail = ['get', hasName, 'set']; + + // `Map`, `WeakMap` and `Set` each have slightly different + // combinations of the above sublists. + var mapMethods = commonInit.concat(forEachName, mapTail), + weakMapMethods = commonInit.concat(mapTail), + setMethods = ['add'].concat(commonInit, forEachName, hasName); + + var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map'); + + var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap'); + + var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set'); + + var isWeakSet = tagTester('WeakSet'); + + // Retrieve the values of an object's properties. + function values(obj) { + var _keys = keys(obj); + var length = _keys.length; + var values = Array(length); + for (var i = 0; i < length; i++) { + values[i] = obj[_keys[i]]; + } + return values; + } + + // Convert an object into a list of `[key, value]` pairs. + // The opposite of `_.object` with one argument. + function pairs(obj) { + var _keys = keys(obj); + var length = _keys.length; + var pairs = Array(length); + for (var i = 0; i < length; i++) { + pairs[i] = [_keys[i], obj[_keys[i]]]; + } + return pairs; + } + + // Invert the keys and values of an object. The values must be serializable. + function invert(obj) { + var result = {}; + var _keys = keys(obj); + for (var i = 0, length = _keys.length; i < length; i++) { + result[obj[_keys[i]]] = _keys[i]; + } + return result; + } + + // Return a sorted list of the function names available on the object. + function functions(obj) { + var names = []; + for (var key in obj) { + if (isFunction$1(obj[key])) names.push(key); + } + return names.sort(); + } + + // An internal function for creating assigner functions. + function createAssigner(keysFunc, defaults) { + return function(obj) { + var length = arguments.length; + if (defaults) obj = Object(obj); + if (length < 2 || obj == null) return obj; + for (var index = 1; index < length; index++) { + var source = arguments[index], + keys = keysFunc(source), + l = keys.length; + for (var i = 0; i < l; i++) { + var key = keys[i]; + if (!defaults || obj[key] === void 0) obj[key] = source[key]; + } + } + return obj; + }; + } + + // Extend a given object with all the properties in passed-in object(s). + var extend = createAssigner(allKeys); + + // Assigns a given object with all the own properties in the passed-in + // object(s). + // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign) + var extendOwn = createAssigner(keys); + + // Fill in a given object with default properties. + var defaults = createAssigner(allKeys, true); + + // Create a naked function reference for surrogate-prototype-swapping. + function ctor() { + return function(){}; + } + + // An internal function for creating a new object that inherits from another. + function baseCreate(prototype) { + if (!isObject(prototype)) return {}; + if (nativeCreate) return nativeCreate(prototype); + var Ctor = ctor(); + Ctor.prototype = prototype; + var result = new Ctor; + Ctor.prototype = null; + return result; + } + + // Creates an object that inherits from the given prototype object. + // If additional properties are provided then they will be added to the + // created object. + function create(prototype, props) { + var result = baseCreate(prototype); + if (props) extendOwn(result, props); + return result; + } + + // Create a (shallow-cloned) duplicate of an object. + function clone(obj) { + if (!isObject(obj)) return obj; + return isArray(obj) ? obj.slice() : extend({}, obj); + } + + // Invokes `interceptor` with the `obj` and then returns `obj`. + // The primary purpose of this method is to "tap into" a method chain, in + // order to perform operations on intermediate results within the chain. + function tap(obj, interceptor) { + interceptor(obj); + return obj; + } + + // Normalize a (deep) property `path` to array. + // Like `_.iteratee`, this function can be customized. + function toPath$1(path) { + return isArray(path) ? path : [path]; + } + _$1.toPath = toPath$1; + + // Internal wrapper for `_.toPath` to enable minification. + // Similar to `cb` for `_.iteratee`. + function toPath(path) { + return _$1.toPath(path); + } + + // Internal function to obtain a nested property in `obj` along `path`. + function deepGet(obj, path) { + var length = path.length; + for (var i = 0; i < length; i++) { + if (obj == null) return void 0; + obj = obj[path[i]]; + } + return length ? obj : void 0; + } + + // Get the value of the (deep) property on `path` from `object`. + // If any property in `path` does not exist or if the value is + // `undefined`, return `defaultValue` instead. + // The `path` is normalized through `_.toPath`. + function get(object, path, defaultValue) { + var value = deepGet(object, toPath(path)); + return isUndefined(value) ? defaultValue : value; + } + + // Shortcut function for checking if an object has a given property directly on + // itself (in other words, not on a prototype). Unlike the internal `has` + // function, this public version can also traverse nested properties. + function has(obj, path) { + path = toPath(path); + var length = path.length; + for (var i = 0; i < length; i++) { + var key = path[i]; + if (!has$1(obj, key)) return false; + obj = obj[key]; + } + return !!length; + } + + // Keep the identity function around for default iteratees. + function identity(value) { + return value; + } + + // Returns a predicate for checking whether an object has a given set of + // `key:value` pairs. + function matcher(attrs) { + attrs = extendOwn({}, attrs); + return function(obj) { + return isMatch(obj, attrs); + }; + } + + // Creates a function that, when passed an object, will traverse that object’s + // properties down the given `path`, specified as an array of keys or indices. + function property(path) { + path = toPath(path); + return function(obj) { + return deepGet(obj, path); + }; + } + + // Internal function that returns an efficient (for current engines) version + // of the passed-in callback, to be repeatedly applied in other Underscore + // functions. + function optimizeCb(func, context, argCount) { + if (context === void 0) return func; + switch (argCount == null ? 3 : argCount) { + case 1: return function(value) { + return func.call(context, value); + }; + // The 2-argument case is omitted because we’re not using it. + case 3: return function(value, index, collection) { + return func.call(context, value, index, collection); + }; + case 4: return function(accumulator, value, index, collection) { + return func.call(context, accumulator, value, index, collection); + }; + } + return function() { + return func.apply(context, arguments); + }; + } + + // An internal function to generate callbacks that can be applied to each + // element in a collection, returning the desired result — either `_.identity`, + // an arbitrary callback, a property matcher, or a property accessor. + function baseIteratee(value, context, argCount) { + if (value == null) return identity; + if (isFunction$1(value)) return optimizeCb(value, context, argCount); + if (isObject(value) && !isArray(value)) return matcher(value); + return property(value); + } + + // External wrapper for our callback generator. Users may customize + // `_.iteratee` if they want additional predicate/iteratee shorthand styles. + // This abstraction hides the internal-only `argCount` argument. + function iteratee(value, context) { + return baseIteratee(value, context, Infinity); + } + _$1.iteratee = iteratee; + + // The function we call internally to generate a callback. It invokes + // `_.iteratee` if overridden, otherwise `baseIteratee`. + function cb(value, context, argCount) { + if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context); + return baseIteratee(value, context, argCount); + } + + // Returns the results of applying the `iteratee` to each element of `obj`. + // In contrast to `_.map` it returns an object. + function mapObject(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var _keys = keys(obj), + length = _keys.length, + results = {}; + for (var index = 0; index < length; index++) { + var currentKey = _keys[index]; + results[currentKey] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + } + + // Predicate-generating function. Often useful outside of Underscore. + function noop(){} + + // Generates a function for a given object that returns a given property. + function propertyOf(obj) { + if (obj == null) return noop; + return function(path) { + return get(obj, path); + }; + } + + // Run a function **n** times. + function times(n, iteratee, context) { + var accum = Array(Math.max(0, n)); + iteratee = optimizeCb(iteratee, context, 1); + for (var i = 0; i < n; i++) accum[i] = iteratee(i); + return accum; + } + + // Return a random integer between `min` and `max` (inclusive). + function random(min, max) { + if (max == null) { + max = min; + min = 0; + } + return min + Math.floor(Math.random() * (max - min + 1)); + } + + // A (possibly faster) way to get the current timestamp as an integer. + var now = Date.now || function() { + return new Date().getTime(); + }; + + // Internal helper to generate functions for escaping and unescaping strings + // to/from HTML interpolation. + function createEscaper(map) { + var escaper = function(match) { + return map[match]; + }; + // Regexes for identifying a key that needs to be escaped. + var source = '(?:' + keys(map).join('|') + ')'; + var testRegexp = RegExp(source); + var replaceRegexp = RegExp(source, 'g'); + return function(string) { + string = string == null ? '' : '' + string; + return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string; + }; + } + + // Internal list of HTML entities for escaping. + var escapeMap = { + '&': '&amp;', + '<': '&lt;', + '>': '&gt;', + '"': '&quot;', + "'": '&#x27;', + '`': '&#x60;' + }; + + // Function for escaping strings to HTML interpolation. + var _escape = createEscaper(escapeMap); + + // Internal list of HTML entities for unescaping. + var unescapeMap = invert(escapeMap); + + // Function for unescaping strings from HTML interpolation. + var _unescape = createEscaper(unescapeMap); + + // By default, Underscore uses ERB-style template delimiters. Change the + // following template settings to use alternative delimiters. + var templateSettings = _$1.templateSettings = { + evaluate: /<%([\s\S]+?)%>/g, + interpolate: /<%=([\s\S]+?)%>/g, + escape: /<%-([\s\S]+?)%>/g + }; + + // When customizing `_.templateSettings`, if you don't want to define an + // interpolation, evaluation or escaping regex, we need one that is + // guaranteed not to match. + var noMatch = /(.)^/; + + // Certain characters need to be escaped so that they can be put into a + // string literal. + var escapes = { + "'": "'", + '\\': '\\', + '\r': 'r', + '\n': 'n', + '\u2028': 'u2028', + '\u2029': 'u2029' + }; + + var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g; + + function escapeChar(match) { + return '\\' + escapes[match]; + } + + // In order to prevent third-party code injection through + // `_.templateSettings.variable`, we test it against the following regular + // expression. It is intentionally a bit more liberal than just matching valid + // identifiers, but still prevents possible loopholes through defaults or + // destructuring assignment. + var bareIdentifier = /^\s*(\w|\$)+\s*$/; + + // JavaScript micro-templating, similar to John Resig's implementation. + // Underscore templating handles arbitrary delimiters, preserves whitespace, + // and correctly escapes quotes within interpolated code. + // NB: `oldSettings` only exists for backwards compatibility. + function template(text, settings, oldSettings) { + if (!settings && oldSettings) settings = oldSettings; + settings = defaults({}, settings, _$1.templateSettings); + + // Combine delimiters into one regular expression via alternation. + var matcher = RegExp([ + (settings.escape || noMatch).source, + (settings.interpolate || noMatch).source, + (settings.evaluate || noMatch).source + ].join('|') + '|$', 'g'); + + // Compile the template source, escaping string literals appropriately. + var index = 0; + var source = "__p+='"; + text.replace(matcher, function(match, escape, interpolate, evaluate, offset) { + source += text.slice(index, offset).replace(escapeRegExp, escapeChar); + index = offset + match.length; + + if (escape) { + source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'"; + } else if (interpolate) { + source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'"; + } else if (evaluate) { + source += "';\n" + evaluate + "\n__p+='"; + } + + // Adobe VMs need the match returned to produce the correct offset. + return match; + }); + source += "';\n"; + + var argument = settings.variable; + if (argument) { + // Insure against third-party code injection. (CVE-2021-23358) + if (!bareIdentifier.test(argument)) throw new Error( + 'variable is not a bare identifier: ' + argument + ); + } else { + // If a variable is not specified, place data values in local scope. + source = 'with(obj||{}){\n' + source + '}\n'; + argument = 'obj'; + } + + source = "var __t,__p='',__j=Array.prototype.join," + + "print=function(){__p+=__j.call(arguments,'');};\n" + + source + 'return __p;\n'; + + var render; + try { + render = new Function(argument, '_', source); + } catch (e) { + e.source = source; + throw e; + } + + var template = function(data) { + return render.call(this, data, _$1); + }; + + // Provide the compiled source as a convenience for precompilation. + template.source = 'function(' + argument + '){\n' + source + '}'; + + return template; + } + + // Traverses the children of `obj` along `path`. If a child is a function, it + // is invoked with its parent as context. Returns the value of the final + // child, or `fallback` if any child is undefined. + function result(obj, path, fallback) { + path = toPath(path); + var length = path.length; + if (!length) { + return isFunction$1(fallback) ? fallback.call(obj) : fallback; + } + for (var i = 0; i < length; i++) { + var prop = obj == null ? void 0 : obj[path[i]]; + if (prop === void 0) { + prop = fallback; + i = length; // Ensure we don't continue iterating. + } + obj = isFunction$1(prop) ? prop.call(obj) : prop; + } + return obj; + } + + // Generate a unique integer id (unique within the entire client session). + // Useful for temporary DOM ids. + var idCounter = 0; + function uniqueId(prefix) { + var id = ++idCounter + ''; + return prefix ? prefix + id : id; + } + + // Start chaining a wrapped Underscore object. + function chain(obj) { + var instance = _$1(obj); + instance._chain = true; + return instance; + } + + // Internal function to execute `sourceFunc` bound to `context` with optional + // `args`. Determines whether to execute a function as a constructor or as a + // normal function. + function executeBound(sourceFunc, boundFunc, context, callingContext, args) { + if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args); + var self = baseCreate(sourceFunc.prototype); + var result = sourceFunc.apply(self, args); + if (isObject(result)) return result; + return self; + } + + // Partially apply a function by creating a version that has had some of its + // arguments pre-filled, without changing its dynamic `this` context. `_` acts + // as a placeholder by default, allowing any combination of arguments to be + // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument. + var partial = restArguments(function(func, boundArgs) { + var placeholder = partial.placeholder; + var bound = function() { + var position = 0, length = boundArgs.length; + var args = Array(length); + for (var i = 0; i < length; i++) { + args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i]; + } + while (position < arguments.length) args.push(arguments[position++]); + return executeBound(func, bound, this, this, args); + }; + return bound; + }); + + partial.placeholder = _$1; + + // Create a function bound to a given object (assigning `this`, and arguments, + // optionally). + var bind = restArguments(function(func, context, args) { + if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function'); + var bound = restArguments(function(callArgs) { + return executeBound(func, bound, context, this, args.concat(callArgs)); + }); + return bound; + }); + + // Internal helper for collection methods to determine whether a collection + // should be iterated as an array or as an object. + // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength + // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094 + var isArrayLike = createSizePropertyCheck(getLength); + + // Internal implementation of a recursive `flatten` function. + function flatten$1(input, depth, strict, output) { + output = output || []; + if (!depth && depth !== 0) { + depth = Infinity; + } else if (depth <= 0) { + return output.concat(input); + } + var idx = output.length; + for (var i = 0, length = getLength(input); i < length; i++) { + var value = input[i]; + if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) { + // Flatten current level of array or arguments object. + if (depth > 1) { + flatten$1(value, depth - 1, strict, output); + idx = output.length; + } else { + var j = 0, len = value.length; + while (j < len) output[idx++] = value[j++]; + } + } else if (!strict) { + output[idx++] = value; + } + } + return output; + } + + // Bind a number of an object's methods to that object. Remaining arguments + // are the method names to be bound. Useful for ensuring that all callbacks + // defined on an object belong to it. + var bindAll = restArguments(function(obj, keys) { + keys = flatten$1(keys, false, false); + var index = keys.length; + if (index < 1) throw new Error('bindAll must be passed function names'); + while (index--) { + var key = keys[index]; + obj[key] = bind(obj[key], obj); + } + return obj; + }); + + // Memoize an expensive function by storing its results. + function memoize(func, hasher) { + var memoize = function(key) { + var cache = memoize.cache; + var address = '' + (hasher ? hasher.apply(this, arguments) : key); + if (!has$1(cache, address)) cache[address] = func.apply(this, arguments); + return cache[address]; + }; + memoize.cache = {}; + return memoize; + } + + // Delays a function for the given number of milliseconds, and then calls + // it with the arguments supplied. + var delay = restArguments(function(func, wait, args) { + return setTimeout(function() { + return func.apply(null, args); + }, wait); + }); + + // Defers a function, scheduling it to run after the current call stack has + // cleared. + var defer = partial(delay, _$1, 1); + + // Returns a function, that, when invoked, will only be triggered at most once + // during a given window of time. Normally, the throttled function will run + // as much as it can, without ever going more than once per `wait` duration; + // but if you'd like to disable the execution on the leading edge, pass + // `{leading: false}`. To disable execution on the trailing edge, ditto. + function throttle(func, wait, options) { + var timeout, context, args, result; + var previous = 0; + if (!options) options = {}; + + var later = function() { + previous = options.leading === false ? 0 : now(); + timeout = null; + result = func.apply(context, args); + if (!timeout) context = args = null; + }; + + var throttled = function() { + var _now = now(); + if (!previous && options.leading === false) previous = _now; + var remaining = wait - (_now - previous); + context = this; + args = arguments; + if (remaining <= 0 || remaining > wait) { + if (timeout) { + clearTimeout(timeout); + timeout = null; + } + previous = _now; + result = func.apply(context, args); + if (!timeout) context = args = null; + } else if (!timeout && options.trailing !== false) { + timeout = setTimeout(later, remaining); + } + return result; + }; + + throttled.cancel = function() { + clearTimeout(timeout); + previous = 0; + timeout = context = args = null; + }; + + return throttled; + } + + // When a sequence of calls of the returned function ends, the argument + // function is triggered. The end of a sequence is defined by the `wait` + // parameter. If `immediate` is passed, the argument function will be + // triggered at the beginning of the sequence instead of at the end. + function debounce(func, wait, immediate) { + var timeout, previous, args, result, context; + + var later = function() { + var passed = now() - previous; + if (wait > passed) { + timeout = setTimeout(later, wait - passed); + } else { + timeout = null; + if (!immediate) result = func.apply(context, args); + // This check is needed because `func` can recursively invoke `debounced`. + if (!timeout) args = context = null; + } + }; + + var debounced = restArguments(function(_args) { + context = this; + args = _args; + previous = now(); + if (!timeout) { + timeout = setTimeout(later, wait); + if (immediate) result = func.apply(context, args); + } + return result; + }); + + debounced.cancel = function() { + clearTimeout(timeout); + timeout = args = context = null; + }; + + return debounced; + } + + // Returns the first function passed as an argument to the second, + // allowing you to adjust arguments, run code before and after, and + // conditionally execute the original function. + function wrap(func, wrapper) { + return partial(wrapper, func); + } + + // Returns a negated version of the passed-in predicate. + function negate(predicate) { + return function() { + return !predicate.apply(this, arguments); + }; + } + + // Returns a function that is the composition of a list of functions, each + // consuming the return value of the function that follows. + function compose() { + var args = arguments; + var start = args.length - 1; + return function() { + var i = start; + var result = args[start].apply(this, arguments); + while (i--) result = args[i].call(this, result); + return result; + }; + } + + // Returns a function that will only be executed on and after the Nth call. + function after(times, func) { + return function() { + if (--times < 1) { + return func.apply(this, arguments); + } + }; + } + + // Returns a function that will only be executed up to (but not including) the + // Nth call. + function before(times, func) { + var memo; + return function() { + if (--times > 0) { + memo = func.apply(this, arguments); + } + if (times <= 1) func = null; + return memo; + }; + } + + // Returns a function that will be executed at most one time, no matter how + // often you call it. Useful for lazy initialization. + var once = partial(before, 2); + + // Returns the first key on an object that passes a truth test. + function findKey(obj, predicate, context) { + predicate = cb(predicate, context); + var _keys = keys(obj), key; + for (var i = 0, length = _keys.length; i < length; i++) { + key = _keys[i]; + if (predicate(obj[key], key, obj)) return key; + } + } + + // Internal function to generate `_.findIndex` and `_.findLastIndex`. + function createPredicateIndexFinder(dir) { + return function(array, predicate, context) { + predicate = cb(predicate, context); + var length = getLength(array); + var index = dir > 0 ? 0 : length - 1; + for (; index >= 0 && index < length; index += dir) { + if (predicate(array[index], index, array)) return index; + } + return -1; + }; + } + + // Returns the first index on an array-like that passes a truth test. + var findIndex = createPredicateIndexFinder(1); + + // Returns the last index on an array-like that passes a truth test. + var findLastIndex = createPredicateIndexFinder(-1); + + // Use a comparator function to figure out the smallest index at which + // an object should be inserted so as to maintain order. Uses binary search. + function sortedIndex(array, obj, iteratee, context) { + iteratee = cb(iteratee, context, 1); + var value = iteratee(obj); + var low = 0, high = getLength(array); + while (low < high) { + var mid = Math.floor((low + high) / 2); + if (iteratee(array[mid]) < value) low = mid + 1; else high = mid; + } + return low; + } + + // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions. + function createIndexFinder(dir, predicateFind, sortedIndex) { + return function(array, item, idx) { + var i = 0, length = getLength(array); + if (typeof idx == 'number') { + if (dir > 0) { + i = idx >= 0 ? idx : Math.max(idx + length, i); + } else { + length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1; + } + } else if (sortedIndex && idx && length) { + idx = sortedIndex(array, item); + return array[idx] === item ? idx : -1; + } + if (item !== item) { + idx = predicateFind(slice.call(array, i, length), isNaN$1); + return idx >= 0 ? idx + i : -1; + } + for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) { + if (array[idx] === item) return idx; + } + return -1; + }; + } + + // Return the position of the first occurrence of an item in an array, + // or -1 if the item is not included in the array. + // If the array is large and already in sort order, pass `true` + // for **isSorted** to use binary search. + var indexOf = createIndexFinder(1, findIndex, sortedIndex); + + // Return the position of the last occurrence of an item in an array, + // or -1 if the item is not included in the array. + var lastIndexOf = createIndexFinder(-1, findLastIndex); + + // Return the first value which passes a truth test. + function find(obj, predicate, context) { + var keyFinder = isArrayLike(obj) ? findIndex : findKey; + var key = keyFinder(obj, predicate, context); + if (key !== void 0 && key !== -1) return obj[key]; + } + + // Convenience version of a common use case of `_.find`: getting the first + // object containing specific `key:value` pairs. + function findWhere(obj, attrs) { + return find(obj, matcher(attrs)); + } + + // The cornerstone for collection functions, an `each` + // implementation, aka `forEach`. + // Handles raw objects in addition to array-likes. Treats all + // sparse array-likes as if they were dense. + function each(obj, iteratee, context) { + iteratee = optimizeCb(iteratee, context); + var i, length; + if (isArrayLike(obj)) { + for (i = 0, length = obj.length; i < length; i++) { + iteratee(obj[i], i, obj); + } + } else { + var _keys = keys(obj); + for (i = 0, length = _keys.length; i < length; i++) { + iteratee(obj[_keys[i]], _keys[i], obj); + } + } + return obj; + } + + // Return the results of applying the iteratee to each element. + function map(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var _keys = !isArrayLike(obj) && keys(obj), + length = (_keys || obj).length, + results = Array(length); + for (var index = 0; index < length; index++) { + var currentKey = _keys ? _keys[index] : index; + results[index] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + } + + // Internal helper to create a reducing function, iterating left or right. + function createReduce(dir) { + // Wrap code that reassigns argument variables in a separate function than + // the one that accesses `arguments.length` to avoid a perf hit. (#1991) + var reducer = function(obj, iteratee, memo, initial) { + var _keys = !isArrayLike(obj) && keys(obj), + length = (_keys || obj).length, + index = dir > 0 ? 0 : length - 1; + if (!initial) { + memo = obj[_keys ? _keys[index] : index]; + index += dir; + } + for (; index >= 0 && index < length; index += dir) { + var currentKey = _keys ? _keys[index] : index; + memo = iteratee(memo, obj[currentKey], currentKey, obj); + } + return memo; + }; + + return function(obj, iteratee, memo, context) { + var initial = arguments.length >= 3; + return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial); + }; + } + + // **Reduce** builds up a single result from a list of values, aka `inject`, + // or `foldl`. + var reduce = createReduce(1); + + // The right-associative version of reduce, also known as `foldr`. + var reduceRight = createReduce(-1); + + // Return all the elements that pass a truth test. + function filter(obj, predicate, context) { + var results = []; + predicate = cb(predicate, context); + each(obj, function(value, index, list) { + if (predicate(value, index, list)) results.push(value); + }); + return results; + } + + // Return all the elements for which a truth test fails. + function reject(obj, predicate, context) { + return filter(obj, negate(cb(predicate)), context); + } + + // Determine whether all of the elements pass a truth test. + function every(obj, predicate, context) { + predicate = cb(predicate, context); + var _keys = !isArrayLike(obj) && keys(obj), + length = (_keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = _keys ? _keys[index] : index; + if (!predicate(obj[currentKey], currentKey, obj)) return false; + } + return true; + } + + // Determine if at least one element in the object passes a truth test. + function some(obj, predicate, context) { + predicate = cb(predicate, context); + var _keys = !isArrayLike(obj) && keys(obj), + length = (_keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = _keys ? _keys[index] : index; + if (predicate(obj[currentKey], currentKey, obj)) return true; + } + return false; + } + + // Determine if the array or object contains a given item (using `===`). + function contains(obj, item, fromIndex, guard) { + if (!isArrayLike(obj)) obj = values(obj); + if (typeof fromIndex != 'number' || guard) fromIndex = 0; + return indexOf(obj, item, fromIndex) >= 0; + } + + // Invoke a method (with arguments) on every item in a collection. + var invoke = restArguments(function(obj, path, args) { + var contextPath, func; + if (isFunction$1(path)) { + func = path; + } else { + path = toPath(path); + contextPath = path.slice(0, -1); + path = path[path.length - 1]; + } + return map(obj, function(context) { + var method = func; + if (!method) { + if (contextPath && contextPath.length) { + context = deepGet(context, contextPath); + } + if (context == null) return void 0; + method = context[path]; + } + return method == null ? method : method.apply(context, args); + }); + }); + + // Convenience version of a common use case of `_.map`: fetching a property. + function pluck(obj, key) { + return map(obj, property(key)); + } + + // Convenience version of a common use case of `_.filter`: selecting only + // objects containing specific `key:value` pairs. + function where(obj, attrs) { + return filter(obj, matcher(attrs)); + } + + // Return the maximum element (or element-based computation). + function max(obj, iteratee, context) { + var result = -Infinity, lastComputed = -Infinity, + value, computed; + if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) { + obj = isArrayLike(obj) ? obj : values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value != null && value > result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + each(obj, function(v, index, list) { + computed = iteratee(v, index, list); + if (computed > lastComputed || computed === -Infinity && result === -Infinity) { + result = v; + lastComputed = computed; + } + }); + } + return result; + } + + // Return the minimum element (or element-based computation). + function min(obj, iteratee, context) { + var result = Infinity, lastComputed = Infinity, + value, computed; + if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) { + obj = isArrayLike(obj) ? obj : values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value != null && value < result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + each(obj, function(v, index, list) { + computed = iteratee(v, index, list); + if (computed < lastComputed || computed === Infinity && result === Infinity) { + result = v; + lastComputed = computed; + } + }); + } + return result; + } + + // Sample **n** random values from a collection using the modern version of the + // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle). + // If **n** is not specified, returns a single random element. + // The internal `guard` argument allows it to work with `_.map`. + function sample(obj, n, guard) { + if (n == null || guard) { + if (!isArrayLike(obj)) obj = values(obj); + return obj[random(obj.length - 1)]; + } + var sample = isArrayLike(obj) ? clone(obj) : values(obj); + var length = getLength(sample); + n = Math.max(Math.min(n, length), 0); + var last = length - 1; + for (var index = 0; index < n; index++) { + var rand = random(index, last); + var temp = sample[index]; + sample[index] = sample[rand]; + sample[rand] = temp; + } + return sample.slice(0, n); + } + + // Shuffle a collection. + function shuffle(obj) { + return sample(obj, Infinity); + } + + // Sort the object's values by a criterion produced by an iteratee. + function sortBy(obj, iteratee, context) { + var index = 0; + iteratee = cb(iteratee, context); + return pluck(map(obj, function(value, key, list) { + return { + value: value, + index: index++, + criteria: iteratee(value, key, list) + }; + }).sort(function(left, right) { + var a = left.criteria; + var b = right.criteria; + if (a !== b) { + if (a > b || a === void 0) return 1; + if (a < b || b === void 0) return -1; + } + return left.index - right.index; + }), 'value'); + } + + // An internal function used for aggregate "group by" operations. + function group(behavior, partition) { + return function(obj, iteratee, context) { + var result = partition ? [[], []] : {}; + iteratee = cb(iteratee, context); + each(obj, function(value, index) { + var key = iteratee(value, index, obj); + behavior(result, value, key); + }); + return result; + }; + } + + // Groups the object's values by a criterion. Pass either a string attribute + // to group by, or a function that returns the criterion. + var groupBy = group(function(result, value, key) { + if (has$1(result, key)) result[key].push(value); else result[key] = [value]; + }); + + // Indexes the object's values by a criterion, similar to `_.groupBy`, but for + // when you know that your index values will be unique. + var indexBy = group(function(result, value, key) { + result[key] = value; + }); + + // Counts instances of an object that group by a certain criterion. Pass + // either a string attribute to count by, or a function that returns the + // criterion. + var countBy = group(function(result, value, key) { + if (has$1(result, key)) result[key]++; else result[key] = 1; + }); + + // Split a collection into two arrays: one whose elements all pass the given + // truth test, and one whose elements all do not pass the truth test. + var partition = group(function(result, value, pass) { + result[pass ? 0 : 1].push(value); + }, true); + + // Safely create a real, live array from anything iterable. + var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g; + function toArray(obj) { + if (!obj) return []; + if (isArray(obj)) return slice.call(obj); + if (isString(obj)) { + // Keep surrogate pair characters together. + return obj.match(reStrSymbol); + } + if (isArrayLike(obj)) return map(obj, identity); + return values(obj); + } + + // Return the number of elements in a collection. + function size(obj) { + if (obj == null) return 0; + return isArrayLike(obj) ? obj.length : keys(obj).length; + } + + // Internal `_.pick` helper function to determine whether `key` is an enumerable + // property name of `obj`. + function keyInObj(value, key, obj) { + return key in obj; + } + + // Return a copy of the object only containing the allowed properties. + var pick = restArguments(function(obj, keys) { + var result = {}, iteratee = keys[0]; + if (obj == null) return result; + if (isFunction$1(iteratee)) { + if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]); + keys = allKeys(obj); + } else { + iteratee = keyInObj; + keys = flatten$1(keys, false, false); + obj = Object(obj); + } + for (var i = 0, length = keys.length; i < length; i++) { + var key = keys[i]; + var value = obj[key]; + if (iteratee(value, key, obj)) result[key] = value; + } + return result; + }); + + // Return a copy of the object without the disallowed properties. + var omit = restArguments(function(obj, keys) { + var iteratee = keys[0], context; + if (isFunction$1(iteratee)) { + iteratee = negate(iteratee); + if (keys.length > 1) context = keys[1]; + } else { + keys = map(flatten$1(keys, false, false), String); + iteratee = function(value, key) { + return !contains(keys, key); + }; + } + return pick(obj, iteratee, context); + }); + + // Returns everything but the last entry of the array. Especially useful on + // the arguments object. Passing **n** will return all the values in + // the array, excluding the last N. + function initial(array, n, guard) { + return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n))); + } + + // Get the first element of an array. Passing **n** will return the first N + // values in the array. The **guard** check allows it to work with `_.map`. + function first(array, n, guard) { + if (array == null || array.length < 1) return n == null || guard ? void 0 : []; + if (n == null || guard) return array[0]; + return initial(array, array.length - n); + } + + // Returns everything but the first entry of the `array`. Especially useful on + // the `arguments` object. Passing an **n** will return the rest N values in the + // `array`. + function rest(array, n, guard) { + return slice.call(array, n == null || guard ? 1 : n); + } + + // Get the last element of an array. Passing **n** will return the last N + // values in the array. + function last(array, n, guard) { + if (array == null || array.length < 1) return n == null || guard ? void 0 : []; + if (n == null || guard) return array[array.length - 1]; + return rest(array, Math.max(0, array.length - n)); + } + + // Trim out all falsy values from an array. + function compact(array) { + return filter(array, Boolean); + } + + // Flatten out an array, either recursively (by default), or up to `depth`. + // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively. + function flatten(array, depth) { + return flatten$1(array, depth, false); + } + + // Take the difference between one array and a number of other arrays. + // Only the elements present in just the first array will remain. + var difference = restArguments(function(array, rest) { + rest = flatten$1(rest, true, true); + return filter(array, function(value){ + return !contains(rest, value); + }); + }); + + // Return a version of the array that does not contain the specified value(s). + var without = restArguments(function(array, otherArrays) { + return difference(array, otherArrays); + }); + + // Produce a duplicate-free version of the array. If the array has already + // been sorted, you have the option of using a faster algorithm. + // The faster algorithm will not work with an iteratee if the iteratee + // is not a one-to-one function, so providing an iteratee will disable + // the faster algorithm. + function uniq(array, isSorted, iteratee, context) { + if (!isBoolean(isSorted)) { + context = iteratee; + iteratee = isSorted; + isSorted = false; + } + if (iteratee != null) iteratee = cb(iteratee, context); + var result = []; + var seen = []; + for (var i = 0, length = getLength(array); i < length; i++) { + var value = array[i], + computed = iteratee ? iteratee(value, i, array) : value; + if (isSorted && !iteratee) { + if (!i || seen !== computed) result.push(value); + seen = computed; + } else if (iteratee) { + if (!contains(seen, computed)) { + seen.push(computed); + result.push(value); + } + } else if (!contains(result, value)) { + result.push(value); + } + } + return result; + } + + // Produce an array that contains the union: each distinct element from all of + // the passed-in arrays. + var union = restArguments(function(arrays) { + return uniq(flatten$1(arrays, true, true)); + }); + + // Produce an array that contains every item shared between all the + // passed-in arrays. + function intersection(array) { + var result = []; + var argsLength = arguments.length; + for (var i = 0, length = getLength(array); i < length; i++) { + var item = array[i]; + if (contains(result, item)) continue; + var j; + for (j = 1; j < argsLength; j++) { + if (!contains(arguments[j], item)) break; + } + if (j === argsLength) result.push(item); + } + return result; + } + + // Complement of zip. Unzip accepts an array of arrays and groups + // each array's elements on shared indices. + function unzip(array) { + var length = array && max(array, getLength).length || 0; + var result = Array(length); + + for (var index = 0; index < length; index++) { + result[index] = pluck(array, index); + } + return result; + } + + // Zip together multiple lists into a single array -- elements that share + // an index go together. + var zip = restArguments(unzip); + + // Converts lists into objects. Pass either a single array of `[key, value]` + // pairs, or two parallel arrays of the same length -- one of keys, and one of + // the corresponding values. Passing by pairs is the reverse of `_.pairs`. + function object(list, values) { + var result = {}; + for (var i = 0, length = getLength(list); i < length; i++) { + if (values) { + result[list[i]] = values[i]; + } else { + result[list[i][0]] = list[i][1]; + } + } + return result; + } + + // Generate an integer Array containing an arithmetic progression. A port of + // the native Python `range()` function. See + // [the Python documentation](https://docs.python.org/library/functions.html#range). + function range(start, stop, step) { + if (stop == null) { + stop = start || 0; + start = 0; + } + if (!step) { + step = stop < start ? -1 : 1; + } + + var length = Math.max(Math.ceil((stop - start) / step), 0); + var range = Array(length); + + for (var idx = 0; idx < length; idx++, start += step) { + range[idx] = start; + } + + return range; + } + + // Chunk a single array into multiple arrays, each containing `count` or fewer + // items. + function chunk(array, count) { + if (count == null || count < 1) return []; + var result = []; + var i = 0, length = array.length; + while (i < length) { + result.push(slice.call(array, i, i += count)); + } + return result; + } + + // Helper function to continue chaining intermediate results. + function chainResult(instance, obj) { + return instance._chain ? _$1(obj).chain() : obj; + } + + // Add your own custom functions to the Underscore object. + function mixin(obj) { + each(functions(obj), function(name) { + var func = _$1[name] = obj[name]; + _$1.prototype[name] = function() { + var args = [this._wrapped]; + push.apply(args, arguments); + return chainResult(this, func.apply(_$1, args)); + }; + }); + return _$1; + } + + // Add all mutator `Array` functions to the wrapper. + each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) { + var method = ArrayProto[name]; + _$1.prototype[name] = function() { + var obj = this._wrapped; + if (obj != null) { + method.apply(obj, arguments); + if ((name === 'shift' || name === 'splice') && obj.length === 0) { + delete obj[0]; + } + } + return chainResult(this, obj); + }; + }); + + // Add all accessor `Array` functions to the wrapper. + each(['concat', 'join', 'slice'], function(name) { + var method = ArrayProto[name]; + _$1.prototype[name] = function() { + var obj = this._wrapped; + if (obj != null) obj = method.apply(obj, arguments); + return chainResult(this, obj); + }; + }); + + // Named Exports + + var allExports = { + __proto__: null, + VERSION: VERSION, + restArguments: restArguments, + isObject: isObject, + isNull: isNull, + isUndefined: isUndefined, + isBoolean: isBoolean, + isElement: isElement, + isString: isString, + isNumber: isNumber, + isDate: isDate, + isRegExp: isRegExp, + isError: isError, + isSymbol: isSymbol, + isArrayBuffer: isArrayBuffer, + isDataView: isDataView$1, + isArray: isArray, + isFunction: isFunction$1, + isArguments: isArguments$1, + isFinite: isFinite$1, + isNaN: isNaN$1, + isTypedArray: isTypedArray$1, + isEmpty: isEmpty, + isMatch: isMatch, + isEqual: isEqual, + isMap: isMap, + isWeakMap: isWeakMap, + isSet: isSet, + isWeakSet: isWeakSet, + keys: keys, + allKeys: allKeys, + values: values, + pairs: pairs, + invert: invert, + functions: functions, + methods: functions, + extend: extend, + extendOwn: extendOwn, + assign: extendOwn, + defaults: defaults, + create: create, + clone: clone, + tap: tap, + get: get, + has: has, + mapObject: mapObject, + identity: identity, + constant: constant, + noop: noop, + toPath: toPath$1, + property: property, + propertyOf: propertyOf, + matcher: matcher, + matches: matcher, + times: times, + random: random, + now: now, + escape: _escape, + unescape: _unescape, + templateSettings: templateSettings, + template: template, + result: result, + uniqueId: uniqueId, + chain: chain, + iteratee: iteratee, + partial: partial, + bind: bind, + bindAll: bindAll, + memoize: memoize, + delay: delay, + defer: defer, + throttle: throttle, + debounce: debounce, + wrap: wrap, + negate: negate, + compose: compose, + after: after, + before: before, + once: once, + findKey: findKey, + findIndex: findIndex, + findLastIndex: findLastIndex, + sortedIndex: sortedIndex, + indexOf: indexOf, + lastIndexOf: lastIndexOf, + find: find, + detect: find, + findWhere: findWhere, + each: each, + forEach: each, + map: map, + collect: map, + reduce: reduce, + foldl: reduce, + inject: reduce, + reduceRight: reduceRight, + foldr: reduceRight, + filter: filter, + select: filter, + reject: reject, + every: every, + all: every, + some: some, + any: some, + contains: contains, + includes: contains, + include: contains, + invoke: invoke, + pluck: pluck, + where: where, + max: max, + min: min, + shuffle: shuffle, + sample: sample, + sortBy: sortBy, + groupBy: groupBy, + indexBy: indexBy, + countBy: countBy, + partition: partition, + toArray: toArray, + size: size, + pick: pick, + omit: omit, + first: first, + head: first, + take: first, + initial: initial, + last: last, + rest: rest, + tail: rest, + drop: rest, + compact: compact, + flatten: flatten, + without: without, + uniq: uniq, + unique: uniq, + union: union, + intersection: intersection, + difference: difference, + unzip: unzip, + transpose: unzip, + zip: zip, + object: object, + range: range, + chunk: chunk, + mixin: mixin, + 'default': _$1 + }; + + // Default Export + + // Add all of the Underscore functions to the wrapper 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(https://fontawesome.com) + +-------------------------------------------------------------------------------- + +Font Awesome Free License + +Font Awesome Free is free, open source, and GPL friendly. You can use it for +commercial projects, open source projects, or really almost whatever you want. +Full Font Awesome Free license: https://fontawesome.com/license/free. + +-------------------------------------------------------------------------------- + +# Icons: CC BY 4.0 License (https://creativecommons.org/licenses/by/4.0/) + +The Font Awesome Free download is licensed under a Creative Commons +Attribution 4.0 International License and applies to all icons packaged +as SVG and JS file types. + +-------------------------------------------------------------------------------- + +# Fonts: SIL OFL 1.1 License + +In the Font Awesome Free download, the SIL OFL license applies to all icons +packaged as web and desktop font files. + +Copyright (c) 2022 Fonticons, Inc. (https://fontawesome.com) +with Reserved Font Name: "Font Awesome". + +This Font Software is licensed under the SIL Open Font License, Version 1.1. +This license is copied below, and is also available with a FAQ at: +http://scripts.sil.org/OFL + +SIL OPEN FONT LICENSE +Version 1.1 - 26 February 2007 + +PREAMBLE +The goals of the Open Font License (OFL) are to stimulate worldwide +development of collaborative font projects, to support the font creation +efforts of academic and linguistic communities, and to provide a free and +open framework in which fonts may be shared and improved in partnership +with others. + +The OFL allows the licensed fonts to be used, studied, modified and +redistributed freely as long as they are not sold by themselves. The +fonts, including any derivative works, can be bundled, embedded, +redistributed and/or sold with any software provided that any reserved +names are not used by derivative works. The fonts and derivatives, +however, cannot be released under any other type of license. The +requirement for fonts to remain under this license does not apply +to any document created using the fonts or their derivatives. + +DEFINITIONS +"Font Software" refers to the set of files released by the Copyright +Holder(s) under this license and clearly marked as such. This may +include source files, build scripts and documentation. + +"Reserved Font Name" refers to any names specified as such after the +copyright statement(s). + +"Original Version" refers to the collection of Font Software components as +distributed by the Copyright Holder(s). + +"Modified Version" refers to any derivative made by adding to, deleting, +or substituting — in part or in whole — any of the components of the +Original Version, by changing formats or by porting the Font Software to a +new environment. + +"Author" refers to any designer, engineer, programmer, technical +writer or other person who contributed to the Font Software. + +PERMISSION & CONDITIONS +Permission is hereby granted, free of charge, to any person obtaining +a copy of the Font Software, to use, study, copy, merge, embed, modify, +redistribute, and sell modified and unmodified copies of the Font +Software, subject to the following conditions: + +1) Neither the Font Software nor any of its individual components, +in Original or Modified Versions, may be sold by itself. + +2) Original or Modified Versions of the Font Software may be bundled, +redistributed and/or sold with any software, provided that each copy +contains the above copyright notice and this license. These can be +included either as stand-alone text files, human-readable headers or +in the appropriate machine-readable metadata fields within text or +binary files as long as those fields can be easily viewed by the user. + +3) No Modified Version of the Font Software may use the Reserved Font +Name(s) unless explicit written permission is granted by the corresponding +Copyright Holder. This restriction only applies to the primary font name as +presented to the users. + +4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font +Software shall not be used to promote, endorse or advertise any +Modified Version, except to acknowledge the contribution(s) of the +Copyright Holder(s) and the Author(s) or with their explicit written +permission. + +5) The Font Software, modified or unmodified, in part or in whole, +must be distributed entirely under this license, and must not be +distributed under any other license. The requirement for fonts to +remain under this license does not apply to any document created +using the Font Software. + +TERMINATION +This license becomes null and void if any of the above conditions are +not met. + +DISCLAIMER +THE FONT SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT +OF COPYRIGHT, PATENT, TRADEMARK, OR OTHER RIGHT. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT +HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION +OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +-------------------------------------------------------------------------------- + +# Attribution + +Attribution is required by MIT, SIL OFL, and CC BY licenses. Downloaded Font +Awesome Free files already contain embedded comments with sufficient +attribution, so you shouldn't need to do anything additional when using these +files normally. + +We've kept attribution comments terse, so we ask that you do not actively work +to remove them from files, especially code. They're a great way for folks to +learn about Font Awesome. + +-------------------------------------------------------------------------------- + +# Brand Icons + +All brand icons are trademarks of their respective owners. The use of these +trademarks does not indicate endorsement of the trademark holder by Font +Awesome, nor vice versa. **Please do not use brand logos for any purpose except +to represent the company, product, or service to which they refer.** diff --git a/_static/vendor/fontawesome/6.1.2/css/all.min.css b/_static/vendor/fontawesome/6.1.2/css/all.min.css new file mode 100644 index 000000000..b9a01fbb7 --- /dev/null +++ b/_static/vendor/fontawesome/6.1.2/css/all.min.css @@ -0,0 +1,6 @@ +/*! + * Font Awesome Free 6.1.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2022 Fonticons, Inc. + */ +.fa{font-family:var(--fa-style-family,"Font Awesome 6 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href="{{ pathto('_static/vendor/fontawesome/6.1.2/css/all.min.css', 1) }}?digest=12da95d707ffb74b382d" rel="stylesheet" /> + <link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2', 1) }}" /> +<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2', 1) }}" /> +<link rel="preload" as="font" type="font/woff2" crossorigin href="{{ pathto('_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2', 1) }}" /> +{% endmacro %} + +{% macro head_pre_assets() %} + <!-- Loaded before other Sphinx assets --> + <link href="{{ pathto('_static/styles/theme.css', 1) }}?digest=12da95d707ffb74b382d" rel="stylesheet" /> +<link href="{{ pathto('_static/styles/bootstrap.css', 1) }}?digest=12da95d707ffb74b382d" rel="stylesheet" /> +<link href="{{ pathto('_static/styles/pydata-sphinx-theme.css', 1) }}?digest=12da95d707ffb74b382d" rel="stylesheet" /> +{% endmacro %} + +{% macro head_js_preload() %} + <!-- Pre-loaded scripts that we'll load fully later --> + <link rel="preload" as="script" href="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=12da95d707ffb74b382d" /> +<link rel="preload" as="script" href="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=12da95d707ffb74b382d" /> +{% endmacro %} + +{% macro body_post() %} + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="{{ pathto('_static/scripts/bootstrap.js', 1) }}?digest=12da95d707ffb74b382d"></script> +<script src="{{ pathto('_static/scripts/pydata-sphinx-theme.js', 1) }}?digest=12da95d707ffb74b382d"></script> +{% endmacro %} \ No newline at end of file diff --git a/about/authors.html b/about/authors.html new file mode 100644 index 000000000..f9fb10bc1 --- /dev/null +++ b/about/authors.html @@ -0,0 +1,479 @@ + + +<!DOCTYPE html> + + +<html lang="en" > + + <head> + <meta charset="utf-8" /> + <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" /> + + <title>Contributors &#8212; LMFlow documentation</title> + + + + <script data-cfasync="false"> + document.documentElement.dataset.mode = localStorage.getItem("mode") || ""; + document.documentElement.dataset.theme = localStorage.getItem("theme") || "light"; + </script> + + <!-- Loaded before other Sphinx assets --> + <link href="../_static/styles/theme.css?digest=12da95d707ffb74b382d" rel="stylesheet" /> +<link href="../_static/styles/bootstrap.css?digest=12da95d707ffb74b382d" rel="stylesheet" /> +<link href="../_static/styles/pydata-sphinx-theme.css?digest=12da95d707ffb74b382d" rel="stylesheet" /> + + + <link href="../_static/vendor/fontawesome/6.1.2/css/all.min.css?digest=12da95d707ffb74b382d" rel="stylesheet" /> + <link rel="preload" as="font" type="font/woff2" crossorigin href="../_static/vendor/fontawesome/6.1.2/webfonts/fa-solid-900.woff2" /> +<link rel="preload" as="font" type="font/woff2" crossorigin href="../_static/vendor/fontawesome/6.1.2/webfonts/fa-brands-400.woff2" /> +<link rel="preload" as="font" type="font/woff2" crossorigin href="../_static/vendor/fontawesome/6.1.2/webfonts/fa-regular-400.woff2" /> + + <link rel="stylesheet" type="text/css" href="../_static/pygments.css" /> + <link rel="stylesheet" type="text/css" href="../_static/graphviz.css" /> + <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" /> + <link rel="stylesheet" type="text/css" href="../_static/design-style.4045f2051d55cab465a707391d5b2007.min.css" /> + + <!-- Pre-loaded scripts that we'll load fully later --> + <link rel="preload" as="script" href="../_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d" /> +<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d" /> + + <script 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class="breadcrumb-item active" aria-current="page">lmflow.args</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.args"> +<span id="lmflow-args"></span><h1>lmflow.args<a class="headerlink" href="#module-lmflow.args" title="Permalink to this heading">#</a></h1> +<p>This script defines dataclasses: ModelArguments and DatasetArguments, +that contain the arguments for the model and dataset used in training.</p> +<p>It imports several modules, including dataclasses, field from typing, Optional from typing, +require_version from transformers.utils.versions, MODEL_FOR_CAUSAL_LM_MAPPING, +and TrainingArguments from transformers.</p> +<p>MODEL_CONFIG_CLASSES is assigned a list of the model config classes from +MODEL_FOR_CAUSAL_LM_MAPPING. MODEL_TYPES is assigned a tuple of the model types +extracted from the MODEL_CONFIG_CLASSES.</p> +<p class="rubric">Classes</p> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="../../autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments" title="lmflow.args.DatasetArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DatasetArguments</span></code></a>([dataset_path, ...])</p></td> +<td><p>Define a class DatasetArguments using the dataclass decorator.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="../../autoapi/lmflow/args/index.html#lmflow.args.FinetunerArguments" title="lmflow.args.FinetunerArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FinetunerArguments</span></code></a>(output_dir[, ...])</p></td> +<td><p>Adapt transformers.TrainingArguments</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="../../autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments" title="lmflow.args.InferencerArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InferencerArguments</span></code></a>([device, local_rank, ...])</p></td> +<td><p>Define a class InferencerArguments using the dataclass decorator.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="../../autoapi/lmflow/args/index.html#lmflow.args.ModelArguments" title="lmflow.args.ModelArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ModelArguments</span></code></a>([model_name_or_path, ...])</p></td> +<td><p>Define a class ModelArguments using the dataclass decorator.</p></td> +</tr> +</tbody> +</table> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + <div 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href="../utils/flash_attention/triton_flash_attention/index.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention</span></code></a></li> +</ul> +</li> +<li class="toctree-l3 has-children"><a class="reference internal" href="../utils/position_interpolation/index.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.position_interpolation</span></code></a><input class="toctree-checkbox" id="toctree-checkbox-9" name="toctree-checkbox-9" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-9"><i class="fa-solid fa-chevron-down"></i></label><ul> +<li class="toctree-l4"><a class="reference internal" href="../utils/position_interpolation/llama_rope_scaled_monkey_patch/index.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch</span></code></a></li> +</ul> +</li> +<li class="toctree-l3"><a class="reference internal" href="../utils/constants/index.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.constants</span></code></a></li> +<li class="toctree-l3"><a class="reference internal" href="../utils/data_utils/index.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.data_utils</span></code></a></li> +</ul> +</li> +<li class="toctree-l2 current active"><a class="current reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.args</span></code></a></li> +<li class="toctree-l2"><a class="reference internal" href="../version/index.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.version</span></code></a></li> +</ul> +</li> +</ul> +</div> +</nav></div> + </div> + + + <div class="sidebar-primary-items__end sidebar-primary__section"> + </div> + + <div id="rtd-footer-container"></div> + + + </div> + + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"> +<div class="header-article-items header-article__inner"> + + <div class="header-article-items__start"> + + <div class="header-article-item"> + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../index.html" class="nav-link">API Reference</a></li> + + + <li class="breadcrumb-item"><a href="../index.html" class="nav-link"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow</span></code></a></li> + + <li class="breadcrumb-item active" aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.args</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.args"> +<span id="lmflow-args"></span><h1><a class="reference internal" href="#module-lmflow.args" title="lmflow.args"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.args</span></code></a><a class="headerlink" href="#module-lmflow.args" title="Permalink to this heading">#</a></h1> +<p>This script defines dataclasses: ModelArguments and DatasetArguments, +that contain the arguments for the model and dataset used in training.</p> +<p>It imports several modules, including dataclasses, field from typing, Optional from typing, +require_version from transformers.utils.versions, MODEL_FOR_CAUSAL_LM_MAPPING, +and TrainingArguments from transformers.</p> +<p>MODEL_CONFIG_CLASSES is assigned a list of the model config classes from +MODEL_FOR_CAUSAL_LM_MAPPING. MODEL_TYPES is assigned a tuple of the model types +extracted from the MODEL_CONFIG_CLASSES.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.args.ModelArguments" title="lmflow.args.ModelArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ModelArguments</span></code></a></p></td> +<td><p>Define a class ModelArguments using the dataclass decorator.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.args.VisModelArguments" title="lmflow.args.VisModelArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">VisModelArguments</span></code></a></p></td> +<td><p>Define a class ModelArguments using the dataclass decorator.</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.args.DatasetArguments" title="lmflow.args.DatasetArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DatasetArguments</span></code></a></p></td> +<td><p>Define a class DatasetArguments using the dataclass decorator.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.args.FinetunerArguments" title="lmflow.args.FinetunerArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FinetunerArguments</span></code></a></p></td> +<td><p>Adapt transformers.TrainingArguments</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.args.EvaluatorArguments" title="lmflow.args.EvaluatorArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EvaluatorArguments</span></code></a></p></td> +<td><p>Define a class EvaluatorArguments using the dataclass decorator. The class contains several optional</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.args.InferencerArguments" title="lmflow.args.InferencerArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">InferencerArguments</span></code></a></p></td> +<td><p>Define a class InferencerArguments using the dataclass decorator. The class contains several optional</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.args.RaftAlignerArguments" title="lmflow.args.RaftAlignerArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RaftAlignerArguments</span></code></a></p></td> +<td><p>Define a class RaftAlignerArguments to configure raft aligner.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.args.BenchmarkingArguments" title="lmflow.args.BenchmarkingArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BenchmarkingArguments</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.args.AutoArguments" title="lmflow.args.AutoArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AutoArguments</span></code></a></p></td> +<td><p>Automatically choose arguments from FinetunerArguments or EvaluatorArguments.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.args.MODEL_CONFIG_CLASSES" title="lmflow.args.MODEL_CONFIG_CLASSES"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MODEL_CONFIG_CLASSES</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.args.MODEL_TYPES" title="lmflow.args.MODEL_TYPES"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MODEL_TYPES</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.args.PIPELINE_ARGUMENT_MAPPING" title="lmflow.args.PIPELINE_ARGUMENT_MAPPING"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PIPELINE_ARGUMENT_MAPPING</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.args.MODEL_CONFIG_CLASSES"> +<span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">MODEL_CONFIG_CLASSES</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#MODEL_CONFIG_CLASSES"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.MODEL_CONFIG_CLASSES" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.args.MODEL_TYPES"> +<span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">MODEL_TYPES</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#MODEL_TYPES"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.MODEL_TYPES" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">ModelArguments</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments" title="Permalink to this definition">#</a></dt> +<dd><p>Define a class ModelArguments using the dataclass decorator. +The class contains several optional parameters that can be used to configure a model.</p> +<dl class="simple"> +<dt>model_name_or_path<span class="classifier">str</span></dt><dd><p>a string representing the path or name of a pretrained +model checkpoint for weights initialization. If None, a model will be trained from scratch.</p> +</dd> +<dt>model_type<span class="classifier">str</span></dt><dd><p>a string representing the type of model to use if training from +scratch. If not provided, a pretrained model will be used.</p> +</dd> +<dt>config_overrides<span class="classifier">str</span></dt><dd><p>a string representing the default config settings to override +when training a model from scratch.</p> +</dd> +<dt>config_name<span class="classifier">str</span></dt><dd><p>a string representing the name or path of the pretrained config to +use, if different from the model_name_or_path.</p> +</dd> +<dt>tokenizer_name<span class="classifier">str</span></dt><dd><p>a string representing the name or path of the pretrained tokenizer +to use, if different from the model_name_or_path.</p> +</dd> +<dt>cache_dir<span class="classifier">str</span></dt><dd><p>a string representing the path to the directory where pretrained models +downloaded from huggingface.co will be stored.</p> +</dd> +<dt>use_fast_tokenizer<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to use a fast tokenizer (backed by the +tokenizers library) or not.</p> +</dd> +<dt>model_revision<span class="classifier">str</span></dt><dd><p>a string representing the specific model version to use (can be a +branch name, tag name, or commit id).</p> +</dd> +<dt>use_auth_token<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to use the token generated when running +huggingface-cli login (necessary to use this script with private models).</p> +</dd> +<dt>torch_dtype<span class="classifier">str</span></dt><dd><p>a string representing the dtype to load the model under. If auto is +passed, the dtype will be automatically derived from the model’s weights.</p> +</dd> +<dt>use_ram_optimized_load<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to use disk mapping when memory is not +enough.</p> +</dd> +<dt>use_int8<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to load int8 quantization for inference.</p> +</dd> +</dl> +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.model_name_or_path"> +<span class="sig-name descname"><span class="pre">model_name_or_path</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.model_name_or_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.model_name_or_path" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.lora_model_path"> +<span class="sig-name descname"><span class="pre">lora_model_path</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.lora_model_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.lora_model_path" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.model_type"> +<span class="sig-name descname"><span class="pre">model_type</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.model_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.model_type" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.arch_type"> +<span class="sig-name descname"><span class="pre">arch_type</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.arch_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.arch_type" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.config_overrides"> +<span class="sig-name descname"><span class="pre">config_overrides</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.config_overrides"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.config_overrides" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="id0"> +<span class="sig-name descname"><span class="pre">arch_type</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.arch_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.config_name"> +<span class="sig-name descname"><span class="pre">config_name</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.config_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.config_name" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.tokenizer_name"> +<span class="sig-name descname"><span class="pre">tokenizer_name</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.tokenizer_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.tokenizer_name" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.cache_dir"> +<span class="sig-name descname"><span class="pre">cache_dir</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.cache_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.cache_dir" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.use_fast_tokenizer"> +<span class="sig-name descname"><span class="pre">use_fast_tokenizer</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.use_fast_tokenizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.use_fast_tokenizer" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.model_revision"> +<span class="sig-name descname"><span class="pre">model_revision</span></span><em class="property"><span class="w"> </span><span class="pre">:str</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.model_revision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.model_revision" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.use_auth_token"> +<span class="sig-name descname"><span class="pre">use_auth_token</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.use_auth_token"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.use_auth_token" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.torch_dtype"> +<span class="sig-name descname"><span class="pre">torch_dtype</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.torch_dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.torch_dtype" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.use_lora"> +<span class="sig-name descname"><span class="pre">use_lora</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.use_lora"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.use_lora" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.lora_r"> +<span class="sig-name descname"><span class="pre">lora_r</span></span><em class="property"><span class="w"> </span><span class="pre">:int</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.lora_r"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.lora_r" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.lora_alpha"> +<span class="sig-name descname"><span class="pre">lora_alpha</span></span><em class="property"><span class="w"> </span><span class="pre">:int</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.lora_alpha"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.lora_alpha" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.lora_target_modules"> +<span class="sig-name descname"><span class="pre">lora_target_modules</span></span><em class="property"><span class="w"> </span><span class="pre">:List[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.lora_target_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.lora_target_modules" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.lora_dropout"> +<span class="sig-name descname"><span class="pre">lora_dropout</span></span><em class="property"><span class="w"> </span><span class="pre">:float</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.lora_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.lora_dropout" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.save_aggregated_lora"> +<span class="sig-name descname"><span class="pre">save_aggregated_lora</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.save_aggregated_lora"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.save_aggregated_lora" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.use_ram_optimized_load"> +<span class="sig-name descname"><span class="pre">use_ram_optimized_load</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.use_ram_optimized_load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.use_ram_optimized_load" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.use_flash_attention"> +<span class="sig-name descname"><span class="pre">use_flash_attention</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.use_flash_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.use_flash_attention" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.truncate_to_model_max_length"> +<span class="sig-name descname"><span class="pre">truncate_to_model_max_length</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.truncate_to_model_max_length"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.truncate_to_model_max_length" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.do_rope_scaling"> +<span class="sig-name descname"><span class="pre">do_rope_scaling</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.do_rope_scaling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.do_rope_scaling" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.rope_pi_ratio"> +<span class="sig-name descname"><span class="pre">rope_pi_ratio</span></span><em class="property"><span class="w"> </span><span class="pre">:int</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.rope_pi_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.rope_pi_ratio" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.rope_ntk_ratio"> +<span class="sig-name descname"><span class="pre">rope_ntk_ratio</span></span><em class="property"><span class="w"> </span><span class="pre">:int</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.rope_ntk_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.rope_ntk_ratio" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.use_int8"> +<span class="sig-name descname"><span class="pre">use_int8</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.use_int8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.use_int8" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.args.ModelArguments.__post_init__"> +<span class="sig-name descname"><span class="pre">__post_init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/lmflow/args.html#ModelArguments.__post_init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.ModelArguments.__post_init__" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.args.VisModelArguments"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">VisModelArguments</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#VisModelArguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.VisModelArguments" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="#lmflow.args.ModelArguments" title="lmflow.args.ModelArguments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ModelArguments</span></code></a></p> +<p>Define a class ModelArguments using the dataclass decorator. +The class contains several optional parameters that can be used to configure a model.</p> +<dl class="simple"> +<dt>model_name_or_path<span class="classifier">str</span></dt><dd><p>a string representing the path or name of a pretrained +model checkpoint for weights initialization. If None, a model will be trained from scratch.</p> +</dd> +<dt>model_type<span class="classifier">str</span></dt><dd><p>a string representing the type of model to use if training from +scratch. If not provided, a pretrained model will be used.</p> +</dd> +<dt>config_overrides<span class="classifier">str</span></dt><dd><p>a string representing the default config settings to override +when training a model from scratch.</p> +</dd> +<dt>config_name<span class="classifier">str</span></dt><dd><p>a string representing the name or path of the pretrained config to +use, if different from the model_name_or_path.</p> +</dd> +<dt>tokenizer_name<span class="classifier">str</span></dt><dd><p>a string representing the name or path of the pretrained tokenizer +to use, if different from the model_name_or_path.</p> +</dd> +<dt>cache_dir<span class="classifier">str</span></dt><dd><p>a string representing the path to the directory where pretrained models +downloaded from huggingface.co will be stored.</p> +</dd> +<dt>use_fast_tokenizer<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to use a fast tokenizer (backed by the +tokenizers library) or not.</p> +</dd> +<dt>model_revision<span class="classifier">str</span></dt><dd><p>a string representing the specific model version to use (can be a +branch name, tag name, or commit id).</p> +</dd> +<dt>use_auth_token<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to use the token generated when running +huggingface-cli login (necessary to use this script with private models).</p> +</dd> +<dt>torch_dtype<span class="classifier">str</span></dt><dd><p>a string representing the dtype to load the model under. If auto is +passed, the dtype will be automatically derived from the model’s weights.</p> +</dd> +<dt>use_ram_optimized_load<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to use disk mapping when memory is not +enough.</p> +</dd> +<dt>use_int8<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to load int8 quantization for inference.</p> +</dd> +</dl> +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.VisModelArguments.low_resource"> +<span class="sig-name descname"><span class="pre">low_resource</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[bool]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#VisModelArguments.low_resource"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.VisModelArguments.low_resource" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.VisModelArguments.custom_model"> +<span class="sig-name descname"><span class="pre">custom_model</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#VisModelArguments.custom_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.VisModelArguments.custom_model" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.VisModelArguments.checkpoint_path"> +<span class="sig-name descname"><span class="pre">checkpoint_path</span></span><em class="property"><span class="w"> </span><span class="pre">:str</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#VisModelArguments.checkpoint_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.VisModelArguments.checkpoint_path" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.VisModelArguments.llm_model_name_or_path"> +<span class="sig-name descname"><span class="pre">llm_model_name_or_path</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#VisModelArguments.llm_model_name_or_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.VisModelArguments.llm_model_name_or_path" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.VisModelArguments.use_prompt_cache"> +<span class="sig-name descname"><span class="pre">use_prompt_cache</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#VisModelArguments.use_prompt_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.VisModelArguments.use_prompt_cache" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.VisModelArguments.prompt_cache_path"> +<span class="sig-name descname"><span class="pre">prompt_cache_path</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#VisModelArguments.prompt_cache_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.VisModelArguments.prompt_cache_path" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">DatasetArguments</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments" title="Permalink to this definition">#</a></dt> +<dd><p>Define a class DatasetArguments using the dataclass decorator. +The class contains several optional parameters that can be used to configure a dataset for a language model.</p> +<dl class="simple"> +<dt>dataset_path<span class="classifier">str</span></dt><dd><p>a string representing the path of the dataset to use.</p> +</dd> +<dt>dataset_name<span class="classifier">str</span></dt><dd><p>a string representing the name of the dataset to use. The default value is “customized”.</p> +</dd> +<dt>is_custom_dataset<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to use custom data. The default value is False.</p> +</dd> +<dt>customized_cache_dir<span class="classifier">str</span></dt><dd><p>a string representing the path to the directory where customized dataset caches will be stored.</p> +</dd> +<dt>dataset_config_name<span class="classifier">str</span></dt><dd><p>a string representing the configuration name of the dataset to use (via the datasets library).</p> +</dd> +<dt>train_file<span class="classifier">str</span></dt><dd><p>a string representing the path to the input training data file (a text file).</p> +</dd> +<dt>validation_file<span class="classifier">str</span></dt><dd><p>a string representing the path to the input evaluation data file to evaluate the perplexity on (a text file).</p> +</dd> +<dt>max_train_samples<span class="classifier">int</span></dt><dd><p>an integer indicating the maximum number of training examples to use for debugging or quicker training. +If set, the training dataset will be truncated to this number.</p> +</dd> +<dt>max_eval_samples: int</dt><dd><p>an integer indicating the maximum number of evaluation examples to use for debugging or quicker training. +If set, the evaluation dataset will be truncated to this number.</p> +</dd> +<dt>streaming<span class="classifier">bool</span></dt><dd><p>a boolean indicating whether to enable streaming mode.</p> +</dd> +<dt>block_size: int</dt><dd><p>an integer indicating the optional input sequence length after tokenization. The training dataset will be +truncated in blocks of this size for training.</p> +</dd> +</dl> +<p>The class also includes some additional parameters that can be used to configure the dataset further, such as <cite>overwrite_cache</cite>, +<cite>validation_split_percentage</cite>, <cite>preprocessing_num_workers</cite>, <cite>disable_group_texts</cite>, <cite>demo_example_in_prompt</cite>, <cite>explanation_in_prompt</cite>, +<cite>keep_linebreaks</cite>, and <cite>prompt_structure</cite>.</p> +<p>The field function is used to set default values and provide help messages for each parameter. The Optional type hint is +used to indicate that a parameter is optional. The metadata argument is used to provide additional information about +each parameter, such as a help message.</p> +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.dataset_path"> +<span class="sig-name descname"><span class="pre">dataset_path</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.dataset_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.dataset_path" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.dataset_name"> +<span class="sig-name descname"><span class="pre">dataset_name</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.dataset_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.dataset_name" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.is_custom_dataset"> +<span class="sig-name descname"><span class="pre">is_custom_dataset</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[bool]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.is_custom_dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.is_custom_dataset" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.customized_cache_dir"> +<span class="sig-name descname"><span class="pre">customized_cache_dir</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.customized_cache_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.customized_cache_dir" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.dataset_config_name"> +<span class="sig-name descname"><span class="pre">dataset_config_name</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.dataset_config_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.dataset_config_name" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.train_file"> +<span class="sig-name descname"><span class="pre">train_file</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.train_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.train_file" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.validation_file"> +<span class="sig-name descname"><span class="pre">validation_file</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.validation_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.validation_file" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.max_train_samples"> +<span class="sig-name descname"><span class="pre">max_train_samples</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.max_train_samples"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.max_train_samples" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.max_eval_samples"> +<span class="sig-name descname"><span class="pre">max_eval_samples</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.max_eval_samples"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.max_eval_samples" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.streaming"> +<span class="sig-name descname"><span class="pre">streaming</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.streaming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.streaming" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.block_size"> +<span class="sig-name descname"><span class="pre">block_size</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.block_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.block_size" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.overwrite_cache"> +<span class="sig-name descname"><span class="pre">overwrite_cache</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.overwrite_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.overwrite_cache" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.validation_split_percentage"> +<span class="sig-name descname"><span class="pre">validation_split_percentage</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.validation_split_percentage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.validation_split_percentage" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.preprocessing_num_workers"> +<span class="sig-name descname"><span class="pre">preprocessing_num_workers</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.preprocessing_num_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.preprocessing_num_workers" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.group_texts_batch_size"> +<span class="sig-name descname"><span class="pre">group_texts_batch_size</span></span><em class="property"><span class="w"> </span><span class="pre">:int</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.group_texts_batch_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.group_texts_batch_size" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.disable_group_texts"> +<span class="sig-name descname"><span class="pre">disable_group_texts</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.disable_group_texts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.disable_group_texts" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.keep_linebreaks"> +<span class="sig-name descname"><span class="pre">keep_linebreaks</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.keep_linebreaks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.keep_linebreaks" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.test_file"> +<span class="sig-name descname"><span class="pre">test_file</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.test_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.test_file" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.args.DatasetArguments.__post_init__"> +<span class="sig-name descname"><span class="pre">__post_init__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/lmflow/args.html#DatasetArguments.__post_init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.DatasetArguments.__post_init__" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.args.FinetunerArguments"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">FinetunerArguments</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#FinetunerArguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.FinetunerArguments" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">transformers.TrainingArguments</span></code></p> +<p>Adapt transformers.TrainingArguments</p> +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.FinetunerArguments.eval_dataset_path"> +<span class="sig-name descname"><span class="pre">eval_dataset_path</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#FinetunerArguments.eval_dataset_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.FinetunerArguments.eval_dataset_path" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">EvaluatorArguments</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments" title="Permalink to this definition">#</a></dt> +<dd><p>Define a class EvaluatorArguments using the dataclass decorator. The class contains several optional +parameters that can be used to configure a evaluator.</p> +<dl class="simple"> +<dt>local_rank<span class="classifier">str</span></dt><dd><p>For distributed training: local_rank</p> +</dd> +</dl> +<p>random_shuffle : bool</p> +<p>use_wandb : bool</p> +<p>random_seed : int, default = 1</p> +<p>output_dir : str, default = ‘./output_dir’,</p> +<dl class="simple"> +<dt>mixed_precision<span class="classifier">str, choice from [“bf16”,”fp16”].</span></dt><dd><p>mixed precision mode, whether to use bf16 or fp16</p> +</dd> +<dt>deepspeed :</dt><dd><p>Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already +loaded json file as a dict</p> +</dd> +<dt>temperature<span class="classifier">float</span></dt><dd><p>An argument of model.generate in huggingface to control the diversity of generation.</p> +</dd> +<dt>repetition_penalty<span class="classifier">float</span></dt><dd><p>An argument of model.generate in huggingface to penalize repetitions.</p> +</dd> +</dl> +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.local_rank"> +<span class="sig-name descname"><span class="pre">local_rank</span></span><em class="property"><span class="w"> </span><span class="pre">:int</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.local_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.local_rank" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.random_shuffle"> +<span class="sig-name descname"><span class="pre">random_shuffle</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[bool]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.random_shuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.random_shuffle" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.use_wandb"> +<span class="sig-name descname"><span class="pre">use_wandb</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[bool]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.use_wandb"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.use_wandb" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.random_seed"> +<span class="sig-name descname"><span class="pre">random_seed</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.random_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.random_seed" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.output_dir"> +<span class="sig-name descname"><span class="pre">output_dir</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.output_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.output_dir" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.mixed_precision"> +<span class="sig-name descname"><span class="pre">mixed_precision</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.mixed_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.mixed_precision" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.deepspeed"> +<span class="sig-name descname"><span class="pre">deepspeed</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.deepspeed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.deepspeed" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.answer_type"> +<span class="sig-name descname"><span class="pre">answer_type</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.answer_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.answer_type" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.prompt_structure"> +<span class="sig-name descname"><span class="pre">prompt_structure</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.prompt_structure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.prompt_structure" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.evaluate_block_size"> +<span class="sig-name descname"><span class="pre">evaluate_block_size</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.evaluate_block_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.evaluate_block_size" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.metric"> +<span class="sig-name descname"><span class="pre">metric</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.metric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.metric" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.inference_batch_size_per_device"> +<span class="sig-name descname"><span class="pre">inference_batch_size_per_device</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.inference_batch_size_per_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.inference_batch_size_per_device" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.use_accelerator_for_evaluator"> +<span class="sig-name descname"><span class="pre">use_accelerator_for_evaluator</span></span><em class="property"><span class="w"> </span><span class="pre">:bool</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.use_accelerator_for_evaluator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.use_accelerator_for_evaluator" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.temperature"> +<span class="sig-name descname"><span class="pre">temperature</span></span><em class="property"><span class="w"> </span><span class="pre">:float</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.temperature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.temperature" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.repetition_penalty"> +<span class="sig-name descname"><span class="pre">repetition_penalty</span></span><em class="property"><span class="w"> </span><span class="pre">:float</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.repetition_penalty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.repetition_penalty" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.EvaluatorArguments.max_new_tokens"> +<span class="sig-name descname"><span class="pre">max_new_tokens</span></span><em class="property"><span class="w"> </span><span class="pre">:int</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#EvaluatorArguments.max_new_tokens"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.EvaluatorArguments.max_new_tokens" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">InferencerArguments</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments" title="Permalink to this definition">#</a></dt> +<dd><p>Define a class InferencerArguments using the dataclass decorator. The class contains several optional +parameters that can be used to configure a inferencer.</p> +<dl class="simple"> +<dt>local_rank<span class="classifier">str</span></dt><dd><p>For distributed training: local_rank</p> +</dd> +</dl> +<p>random_seed : int, default = 1</p> +<dl class="simple"> +<dt>deepspeed :</dt><dd><p>Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already +loaded json file as a dict</p> +</dd> +<dt>mixed_precision<span class="classifier">str, choice from [“bf16”,”fp16”].</span></dt><dd><p>mixed precision mode, whether to use bf16 or fp16</p> +</dd> +<dt>temperature<span class="classifier">float</span></dt><dd><p>An argument of model.generate in huggingface to control the diversity of generation.</p> +</dd> +<dt>repetition_penalty<span class="classifier">float</span></dt><dd><p>An argument of model.generate in huggingface to penalize repetitions.</p> +</dd> +</dl> +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments.device"> +<span class="sig-name descname"><span class="pre">device</span></span><em class="property"><span class="w"> </span><span class="pre">:str</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments.device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments.device" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments.local_rank"> +<span class="sig-name descname"><span class="pre">local_rank</span></span><em class="property"><span class="w"> </span><span class="pre">:int</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments.local_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments.local_rank" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments.temperature"> +<span class="sig-name descname"><span class="pre">temperature</span></span><em class="property"><span class="w"> </span><span class="pre">:float</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments.temperature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments.temperature" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments.repetition_penalty"> +<span class="sig-name descname"><span class="pre">repetition_penalty</span></span><em class="property"><span class="w"> </span><span class="pre">:float</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments.repetition_penalty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments.repetition_penalty" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments.max_new_tokens"> +<span class="sig-name descname"><span class="pre">max_new_tokens</span></span><em class="property"><span class="w"> </span><span class="pre">:int</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments.max_new_tokens"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments.max_new_tokens" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments.random_seed"> +<span class="sig-name descname"><span class="pre">random_seed</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments.random_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments.random_seed" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments.deepspeed"> +<span class="sig-name descname"><span class="pre">deepspeed</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments.deepspeed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments.deepspeed" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments.mixed_precision"> +<span class="sig-name descname"><span class="pre">mixed_precision</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments.mixed_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments.mixed_precision" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.InferencerArguments.do_sample"> +<span class="sig-name descname"><span class="pre">do_sample</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[bool]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#InferencerArguments.do_sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.InferencerArguments.do_sample" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.args.RaftAlignerArguments"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">RaftAlignerArguments</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#RaftAlignerArguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.RaftAlignerArguments" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">transformers.TrainingArguments</span></code></p> +<p>Define a class RaftAlignerArguments to configure raft aligner.</p> +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.RaftAlignerArguments.output_reward_path"> +<span class="sig-name descname"><span class="pre">output_reward_path</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#RaftAlignerArguments.output_reward_path"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.RaftAlignerArguments.output_reward_path" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.RaftAlignerArguments.output_min_length"> +<span class="sig-name descname"><span class="pre">output_min_length</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#RaftAlignerArguments.output_min_length"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.RaftAlignerArguments.output_min_length" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.RaftAlignerArguments.output_max_length"> +<span class="sig-name descname"><span class="pre">output_max_length</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#RaftAlignerArguments.output_max_length"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.RaftAlignerArguments.output_max_length" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.RaftAlignerArguments.num_raft_iteration"> +<span class="sig-name descname"><span class="pre">num_raft_iteration</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#RaftAlignerArguments.num_raft_iteration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.RaftAlignerArguments.num_raft_iteration" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.RaftAlignerArguments.raft_batch_size"> +<span class="sig-name descname"><span class="pre">raft_batch_size</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#RaftAlignerArguments.raft_batch_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.RaftAlignerArguments.raft_batch_size" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.RaftAlignerArguments.top_reward_percentage"> +<span class="sig-name descname"><span class="pre">top_reward_percentage</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[float]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#RaftAlignerArguments.top_reward_percentage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.RaftAlignerArguments.top_reward_percentage" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.RaftAlignerArguments.inference_batch_size_per_device"> +<span class="sig-name descname"><span class="pre">inference_batch_size_per_device</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[int]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#RaftAlignerArguments.inference_batch_size_per_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.RaftAlignerArguments.inference_batch_size_per_device" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.RaftAlignerArguments.collection_strategy"> +<span class="sig-name descname"><span class="pre">collection_strategy</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#RaftAlignerArguments.collection_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.RaftAlignerArguments.collection_strategy" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.args.BenchmarkingArguments"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">BenchmarkingArguments</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#BenchmarkingArguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.BenchmarkingArguments" title="Permalink to this definition">#</a></dt> +<dd><dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.BenchmarkingArguments.dataset_name"> +<span class="sig-name descname"><span class="pre">dataset_name</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#BenchmarkingArguments.dataset_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.BenchmarkingArguments.dataset_name" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py attribute"> +<dt class="sig sig-object py" id="lmflow.args.BenchmarkingArguments.lm_evaluation_metric"> +<span class="sig-name descname"><span class="pre">lm_evaluation_metric</span></span><em class="property"><span class="w"> </span><span class="pre">:Optional[str]</span></em><a class="reference internal" href="../../../_modules/lmflow/args.html#BenchmarkingArguments.lm_evaluation_metric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.BenchmarkingArguments.lm_evaluation_metric" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.args.PIPELINE_ARGUMENT_MAPPING"> +<span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">PIPELINE_ARGUMENT_MAPPING</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#PIPELINE_ARGUMENT_MAPPING"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.PIPELINE_ARGUMENT_MAPPING" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.args.AutoArguments"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.args.</span></span><span class="sig-name descname"><span class="pre">AutoArguments</span></span><a class="reference internal" href="../../../_modules/lmflow/args.html#AutoArguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.AutoArguments" title="Permalink to this definition">#</a></dt> +<dd><p>Automatically choose arguments from FinetunerArguments or EvaluatorArguments.</p> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.args.AutoArguments.get_pipeline_args_class"> +<span class="sig-name descname"><span class="pre">get_pipeline_args_class</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/lmflow/args.html#AutoArguments.get_pipeline_args_class"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.args.AutoArguments.get_pipeline_args_class" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../utils/data_utils/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.data_utils</span></code></p> + </div> + </a> + <a class="right-next" + href="../version/index.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.version</span></code></p> + </div> + <i class="fa-solid fa-angle-right"></i> + </a> +</div> + </footer> + + </div> + + + + <div 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nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.MODEL_TYPES"><code class="docutils literal notranslate"><span class="pre">MODEL_TYPES</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments"><code class="docutils literal notranslate"><span class="pre">ModelArguments</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.model_name_or_path"><code class="docutils literal notranslate"><span class="pre">ModelArguments.model_name_or_path</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_model_path"><code class="docutils literal notranslate"><span class="pre">ModelArguments.lora_model_path</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal 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notranslate"><span class="pre">ModelArguments.config_name</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.tokenizer_name"><code class="docutils literal notranslate"><span class="pre">ModelArguments.tokenizer_name</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.cache_dir"><code class="docutils literal notranslate"><span class="pre">ModelArguments.cache_dir</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_fast_tokenizer"><code class="docutils literal notranslate"><span class="pre">ModelArguments.use_fast_tokenizer</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.model_revision"><code class="docutils literal notranslate"><span class="pre">ModelArguments.model_revision</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_auth_token"><code class="docutils literal notranslate"><span class="pre">ModelArguments.use_auth_token</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.torch_dtype"><code class="docutils literal notranslate"><span class="pre">ModelArguments.torch_dtype</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_lora"><code class="docutils literal notranslate"><span class="pre">ModelArguments.use_lora</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_r"><code class="docutils literal notranslate"><span class="pre">ModelArguments.lora_r</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_alpha"><code class="docutils literal notranslate"><span class="pre">ModelArguments.lora_alpha</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_target_modules"><code class="docutils literal notranslate"><span class="pre">ModelArguments.lora_target_modules</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_dropout"><code class="docutils literal notranslate"><span class="pre">ModelArguments.lora_dropout</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.save_aggregated_lora"><code class="docutils literal notranslate"><span class="pre">ModelArguments.save_aggregated_lora</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_ram_optimized_load"><code class="docutils literal notranslate"><span class="pre">ModelArguments.use_ram_optimized_load</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_flash_attention"><code class="docutils literal notranslate"><span class="pre">ModelArguments.use_flash_attention</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.truncate_to_model_max_length"><code class="docutils literal notranslate"><span class="pre">ModelArguments.truncate_to_model_max_length</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.do_rope_scaling"><code class="docutils literal notranslate"><span class="pre">ModelArguments.do_rope_scaling</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.rope_pi_ratio"><code class="docutils literal notranslate"><span class="pre">ModelArguments.rope_pi_ratio</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.rope_ntk_ratio"><code class="docutils literal notranslate"><span class="pre">ModelArguments.rope_ntk_ratio</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_int8"><code class="docutils literal notranslate"><span class="pre">ModelArguments.use_int8</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.__post_init__"><code class="docutils literal notranslate"><span class="pre">ModelArguments.__post_init__()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments"><code class="docutils literal notranslate"><span class="pre">VisModelArguments</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.low_resource"><code class="docutils literal notranslate"><span class="pre">VisModelArguments.low_resource</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.custom_model"><code class="docutils literal notranslate"><span class="pre">VisModelArguments.custom_model</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.checkpoint_path"><code class="docutils literal notranslate"><span class="pre">VisModelArguments.checkpoint_path</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.llm_model_name_or_path"><code class="docutils literal notranslate"><span class="pre">VisModelArguments.llm_model_name_or_path</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.use_prompt_cache"><code class="docutils literal notranslate"><span class="pre">VisModelArguments.use_prompt_cache</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.prompt_cache_path"><code class="docutils literal notranslate"><span class="pre">VisModelArguments.prompt_cache_path</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments"><code class="docutils literal notranslate"><span class="pre">DatasetArguments</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.dataset_path"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.dataset_path</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.dataset_name"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.dataset_name</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.is_custom_dataset"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.is_custom_dataset</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.customized_cache_dir"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.customized_cache_dir</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.dataset_config_name"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.dataset_config_name</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.train_file"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.train_file</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.validation_file"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.validation_file</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.max_train_samples"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.max_train_samples</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.max_eval_samples"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.max_eval_samples</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.streaming"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.streaming</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.block_size"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.block_size</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.overwrite_cache"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.overwrite_cache</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.validation_split_percentage"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.validation_split_percentage</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.preprocessing_num_workers"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.preprocessing_num_workers</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.group_texts_batch_size"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.group_texts_batch_size</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.disable_group_texts"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.disable_group_texts</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.keep_linebreaks"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.keep_linebreaks</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.test_file"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.test_file</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.__post_init__"><code class="docutils literal notranslate"><span class="pre">DatasetArguments.__post_init__()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.FinetunerArguments"><code class="docutils literal notranslate"><span class="pre">FinetunerArguments</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.FinetunerArguments.eval_dataset_path"><code class="docutils literal notranslate"><span class="pre">FinetunerArguments.eval_dataset_path</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.local_rank"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.local_rank</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.random_shuffle"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.random_shuffle</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.use_wandb"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.use_wandb</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.random_seed"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.random_seed</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.output_dir"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.output_dir</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.mixed_precision"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.mixed_precision</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.deepspeed"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.deepspeed</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.answer_type"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.answer_type</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.prompt_structure"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.prompt_structure</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.evaluate_block_size"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.evaluate_block_size</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.metric"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.metric</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.inference_batch_size_per_device"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.inference_batch_size_per_device</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.use_accelerator_for_evaluator"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.use_accelerator_for_evaluator</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.temperature"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.temperature</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.repetition_penalty"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.repetition_penalty</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.max_new_tokens"><code class="docutils literal notranslate"><span class="pre">EvaluatorArguments.max_new_tokens</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments"><code class="docutils literal notranslate"><span class="pre">InferencerArguments</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.device"><code class="docutils literal notranslate"><span class="pre">InferencerArguments.device</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.local_rank"><code class="docutils literal notranslate"><span class="pre">InferencerArguments.local_rank</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.temperature"><code class="docutils literal notranslate"><span class="pre">InferencerArguments.temperature</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.repetition_penalty"><code class="docutils literal notranslate"><span class="pre">InferencerArguments.repetition_penalty</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.max_new_tokens"><code class="docutils literal notranslate"><span class="pre">InferencerArguments.max_new_tokens</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.random_seed"><code class="docutils literal notranslate"><span class="pre">InferencerArguments.random_seed</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.deepspeed"><code class="docutils literal notranslate"><span class="pre">InferencerArguments.deepspeed</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.mixed_precision"><code class="docutils literal notranslate"><span class="pre">InferencerArguments.mixed_precision</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.do_sample"><code class="docutils literal notranslate"><span class="pre">InferencerArguments.do_sample</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments"><code class="docutils literal notranslate"><span class="pre">RaftAlignerArguments</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.output_reward_path"><code class="docutils literal notranslate"><span class="pre">RaftAlignerArguments.output_reward_path</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.output_min_length"><code class="docutils literal notranslate"><span class="pre">RaftAlignerArguments.output_min_length</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.output_max_length"><code class="docutils literal notranslate"><span class="pre">RaftAlignerArguments.output_max_length</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.num_raft_iteration"><code class="docutils literal notranslate"><span class="pre">RaftAlignerArguments.num_raft_iteration</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.raft_batch_size"><code class="docutils literal notranslate"><span class="pre">RaftAlignerArguments.raft_batch_size</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.top_reward_percentage"><code class="docutils literal notranslate"><span class="pre">RaftAlignerArguments.top_reward_percentage</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.inference_batch_size_per_device"><code class="docutils literal notranslate"><span class="pre">RaftAlignerArguments.inference_batch_size_per_device</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.collection_strategy"><code class="docutils literal notranslate"><span class="pre">RaftAlignerArguments.collection_strategy</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.BenchmarkingArguments"><code class="docutils literal notranslate"><span class="pre">BenchmarkingArguments</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.BenchmarkingArguments.dataset_name"><code class="docutils literal notranslate"><span class="pre">BenchmarkingArguments.dataset_name</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.BenchmarkingArguments.lm_evaluation_metric"><code class="docutils literal notranslate"><span class="pre">BenchmarkingArguments.lm_evaluation_metric</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.PIPELINE_ARGUMENT_MAPPING"><code class="docutils literal notranslate"><span class="pre">PIPELINE_ARGUMENT_MAPPING</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.AutoArguments"><code class="docutils literal notranslate"><span class="pre">AutoArguments</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.AutoArguments.get_pipeline_args_class"><code class="docutils literal notranslate"><span class="pre">AutoArguments.get_pipeline_args_class()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.MODEL_CONFIG_CLASSES">MODEL_CONFIG_CLASSES</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.MODEL_TYPES">MODEL_TYPES</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments">ModelArguments</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.model_name_or_path">model_name_or_path</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_model_path">lora_model_path</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.model_type">model_type</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.arch_type">arch_type</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.config_overrides">config_overrides</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#id0">arch_type</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.config_name">config_name</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.tokenizer_name">tokenizer_name</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.cache_dir">cache_dir</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_fast_tokenizer">use_fast_tokenizer</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.model_revision">model_revision</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_auth_token">use_auth_token</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.torch_dtype">torch_dtype</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_lora">use_lora</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_r">lora_r</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_alpha">lora_alpha</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_target_modules">lora_target_modules</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.lora_dropout">lora_dropout</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.save_aggregated_lora">save_aggregated_lora</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_ram_optimized_load">use_ram_optimized_load</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_flash_attention">use_flash_attention</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.truncate_to_model_max_length">truncate_to_model_max_length</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.do_rope_scaling">do_rope_scaling</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.rope_pi_ratio">rope_pi_ratio</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.rope_ntk_ratio">rope_ntk_ratio</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.use_int8">use_int8</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.ModelArguments.__post_init__">__post_init__</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments">VisModelArguments</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.low_resource">low_resource</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.custom_model">custom_model</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.checkpoint_path">checkpoint_path</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.llm_model_name_or_path">llm_model_name_or_path</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.use_prompt_cache">use_prompt_cache</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.VisModelArguments.prompt_cache_path">prompt_cache_path</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments">DatasetArguments</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.dataset_path">dataset_path</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.dataset_name">dataset_name</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.is_custom_dataset">is_custom_dataset</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.customized_cache_dir">customized_cache_dir</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.dataset_config_name">dataset_config_name</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.train_file">train_file</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.validation_file">validation_file</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.max_train_samples">max_train_samples</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.max_eval_samples">max_eval_samples</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.streaming">streaming</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.block_size">block_size</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.overwrite_cache">overwrite_cache</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.validation_split_percentage">validation_split_percentage</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.preprocessing_num_workers">preprocessing_num_workers</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.group_texts_batch_size">group_texts_batch_size</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.disable_group_texts">disable_group_texts</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.keep_linebreaks">keep_linebreaks</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.test_file">test_file</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.DatasetArguments.__post_init__">__post_init__</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.FinetunerArguments">FinetunerArguments</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.FinetunerArguments.eval_dataset_path">eval_dataset_path</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments">EvaluatorArguments</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.local_rank">local_rank</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.random_shuffle">random_shuffle</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.use_wandb">use_wandb</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.random_seed">random_seed</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.output_dir">output_dir</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.mixed_precision">mixed_precision</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.deepspeed">deepspeed</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.answer_type">answer_type</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.prompt_structure">prompt_structure</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.evaluate_block_size">evaluate_block_size</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.metric">metric</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.inference_batch_size_per_device">inference_batch_size_per_device</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.use_accelerator_for_evaluator">use_accelerator_for_evaluator</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.temperature">temperature</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.repetition_penalty">repetition_penalty</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.EvaluatorArguments.max_new_tokens">max_new_tokens</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments">InferencerArguments</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.device">device</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.local_rank">local_rank</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.temperature">temperature</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.repetition_penalty">repetition_penalty</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.max_new_tokens">max_new_tokens</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.random_seed">random_seed</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.deepspeed">deepspeed</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.mixed_precision">mixed_precision</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.InferencerArguments.do_sample">do_sample</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments">RaftAlignerArguments</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.output_reward_path">output_reward_path</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.output_min_length">output_min_length</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.output_max_length">output_max_length</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.num_raft_iteration">num_raft_iteration</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.raft_batch_size">raft_batch_size</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.top_reward_percentage">top_reward_percentage</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.inference_batch_size_per_device">inference_batch_size_per_device</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.RaftAlignerArguments.collection_strategy">collection_strategy</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.BenchmarkingArguments">BenchmarkingArguments</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.BenchmarkingArguments.dataset_name">dataset_name</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.BenchmarkingArguments.lm_evaluation_metric">lm_evaluation_metric</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.PIPELINE_ARGUMENT_MAPPING">PIPELINE_ARGUMENT_MAPPING</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.AutoArguments">AutoArguments</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.args.AutoArguments.get_pipeline_args_class">get_pipeline_args_class</a></li> +</ul> +</li> 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Python code defines a class Dataset with methods for initializing, loading, +and manipulating datasets from different backends such as Hugging Face and JSON.</p> +<p>The <cite>Dataset</cite> class includes methods for loading datasets from a dictionary and a Hugging +Face dataset, mapping datasets, and retrieving the backend dataset and arguments.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.datasets.dataset.Dataset" title="lmflow.datasets.dataset.Dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dataset</span></code></a></p></td> +<td><p>Initializes the Dataset object with the given parameters.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.datasets.dataset.DATASET_TYPES" title="lmflow.datasets.dataset.DATASET_TYPES"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DATASET_TYPES</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.datasets.dataset.KEY_TYPE" title="lmflow.datasets.dataset.KEY_TYPE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KEY_TYPE</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.datasets.dataset.KEY_INSTANCES" title="lmflow.datasets.dataset.KEY_INSTANCES"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KEY_INSTANCES</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.DATASET_TYPES"> +<span class="sig-prename descclassname"><span class="pre">lmflow.datasets.dataset.</span></span><span class="sig-name descname"><span class="pre">DATASET_TYPES</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">['text_only',</span> <span class="pre">'text2text',</span> <span class="pre">'float_only',</span> <span class="pre">'image_text']</span></em><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#DATASET_TYPES"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.DATASET_TYPES" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.KEY_TYPE"> +<span class="sig-prename descclassname"><span class="pre">lmflow.datasets.dataset.</span></span><span class="sig-name descname"><span class="pre">KEY_TYPE</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">type</span></em><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#KEY_TYPE"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.KEY_TYPE" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.KEY_INSTANCES"> +<span class="sig-prename descclassname"><span class="pre">lmflow.datasets.dataset.</span></span><span class="sig-name descname"><span class="pre">KEY_INSTANCES</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">instances</span></em><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#KEY_INSTANCES"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.KEY_INSTANCES" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.datasets.dataset.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'huggingface'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset" title="Permalink to this definition">#</a></dt> +<dd><p>Initializes the Dataset object with the given parameters.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>data_args</strong><span class="classifier">DatasetArguments object.</span></dt><dd><p>Contains the arguments required to load the dataset.</p> +</dd> +<dt><strong>backend</strong><span class="classifier">str, default=”huggingface”</span></dt><dd><p>A string representing the dataset backend. Defaults to “huggingface”.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset._check_data_format"> +<span class="sig-name descname"><span class="pre">_check_data_format</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset._check_data_format"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset._check_data_format" title="Permalink to this definition">#</a></dt> +<dd><p>Checks if data type and data structure matches</p> +<p>Raise messages with hints if not matched.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.from_dict"> +<span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict_obj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.from_dict" title="Permalink to this definition">#</a></dt> +<dd><p>Create a Dataset object from a dictionary.</p> +<dl> +<dt>Return a Dataset given a dict with format:</dt><dd><dl> +<dt>{</dt><dd><p>“type”: TYPE, +“instances”: [</p> +<blockquote> +<div><dl class="simple"> +<dt>{</dt><dd><p>“key_1”: VALUE_1.1, +“key_2”: VALUE_1.2, +…</p> +</dd> +</dl> +<p>}, +{</p> +<blockquote> +<div><p>“key_1”: VALUE_2.1, +“key_2”: VALUE_2.2, +…</p> +</div></blockquote> +</div></blockquote> +<p>]</p> +</dd> +</dl> +<p>}</p> +</dd> +</dl> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>dict_obj</strong><span class="classifier">dict.</span></dt><dd><p>A dictionary containing the dataset information.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt><strong>self</strong><span class="classifier">Dataset object.</span></dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.create_from_dict"> +<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict_obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.create_from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.create_from_dict" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>Returns a Dataset object given a dict.</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.to_dict"> +<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.to_dict" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl> +<dt>Return a dict represents the dataset:</dt><dd><dl> +<dt>{</dt><dd><p>“type”: TYPE, +“instances”: [</p> +<blockquote> +<div><dl class="simple"> +<dt>{</dt><dd><p>“key_1”: VALUE_1.1, +“key_2”: VALUE_1.2, +…</p> +</dd> +</dl> +<p>}, +{</p> +<blockquote> +<div><p>“key_1”: VALUE_2.1, +“key_2”: VALUE_2.2, +…</p> +</div></blockquote> +</div></blockquote> +<p>]</p> +</dd> +</dl> +<p>}</p> +</dd> +<dt>A python dict object represents the content of this dataset.</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.to_list"> +<span class="sig-name descname"><span class="pre">to_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.to_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.to_list" title="Permalink to this definition">#</a></dt> +<dd><p>Returns a list of instances.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.map"> +<span class="sig-name descname"><span class="pre">map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.map" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt><strong>self</strong><span class="classifier">Dataset object.</span></dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.get_backend"> +<span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.get_backend" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>self.backend</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.get_backend_dataset"> +<span class="sig-name descname"><span class="pre">get_backend_dataset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.get_backend_dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.get_backend_dataset" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>self.backend_dataset</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.get_fingerprint"> +<span class="sig-name descname"><span class="pre">get_fingerprint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.get_fingerprint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.get_fingerprint" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>Fingerprint of the backend_dataset which controls the cache</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.get_data_args"> +<span class="sig-name descname"><span class="pre">get_data_args</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.get_data_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.get_data_args" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>self.data_args</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.dataset.Dataset.get_type"> +<span class="sig-name descname"><span class="pre">get_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/datasets/dataset.html#Dataset.get_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.dataset.Dataset.get_type" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>self.type</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer 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onthispage"> + <i class="fa-solid fa-list"></i> On this page + </div> + <nav class="bd-toc-nav page-toc"> + <ul class="visible nav section-nav flex-column"> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-contents">Module Contents</a><ul class="nav section-nav flex-column"> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#classes">Classes</a></li> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#attributes">Attributes</a><ul class="nav section-nav flex-column"> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.DATASET_TYPES"><code class="docutils literal notranslate"><span class="pre">DATASET_TYPES</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.KEY_TYPE"><code class="docutils literal notranslate"><span class="pre">KEY_TYPE</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.KEY_INSTANCES"><code class="docutils literal notranslate"><span class="pre">KEY_INSTANCES</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset"><code class="docutils literal notranslate"><span class="pre">Dataset</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset._check_data_format"><code class="docutils literal notranslate"><span class="pre">Dataset._check_data_format()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.from_dict"><code class="docutils literal notranslate"><span class="pre">Dataset.from_dict()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.create_from_dict"><code class="docutils literal notranslate"><span class="pre">Dataset.create_from_dict()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.to_dict"><code class="docutils literal notranslate"><span class="pre">Dataset.to_dict()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.to_list"><code class="docutils literal notranslate"><span class="pre">Dataset.to_list()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.map"><code class="docutils literal notranslate"><span class="pre">Dataset.map()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_backend"><code class="docutils literal notranslate"><span class="pre">Dataset.get_backend()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_backend_dataset"><code class="docutils literal notranslate"><span class="pre">Dataset.get_backend_dataset()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_fingerprint"><code class="docutils literal notranslate"><span class="pre">Dataset.get_fingerprint()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_data_args"><code class="docutils literal notranslate"><span class="pre">Dataset.get_data_args()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_type"><code class="docutils literal notranslate"><span class="pre">Dataset.get_type()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.DATASET_TYPES">DATASET_TYPES</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.KEY_TYPE">KEY_TYPE</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.KEY_INSTANCES">KEY_INSTANCES</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset">Dataset</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset._check_data_format">_check_data_format</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.from_dict">from_dict</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.create_from_dict">create_from_dict</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.to_dict">to_dict</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.to_list">to_list</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.map">map</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_backend">get_backend</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_backend_dataset">get_backend_dataset</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_fingerprint">get_fingerprint</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_data_args">get_data_args</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.dataset.Dataset.get_type">get_type</a></li> +</ul> +</li> +</ul> +</li> +</ul> +</li> +</ul> + </nav></div> + + <div class="sidebar-secondary-item"> + <div class="tocsection sourcelink"> + <a href="../../../../_sources/autoapi/lmflow/datasets/dataset/index.rst.txt"> + <i class="fa-solid fa-file-lines"></i> Show Source + </a> + </div> +</div> + +</div></div> + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script 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literal notranslate"><span class="pre">lmflow.args</span></code></a></li> +<li class="toctree-l2"><a class="reference internal" href="../version/index.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.version</span></code></a></li> +</ul> +</li> +</ul> +</div> +</nav></div> + </div> + + + <div class="sidebar-primary-items__end sidebar-primary__section"> + </div> + + <div id="rtd-footer-container"></div> + + + </div> + + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"> +<div class="header-article-items header-article__inner"> + + <div class="header-article-items__start"> + + <div class="header-article-item"> + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../index.html" class="nav-link">API Reference</a></li> + + + <li class="breadcrumb-item"><a href="../index.html" class="nav-link"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow</span></code></a></li> + + <li class="breadcrumb-item active" aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.datasets</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.datasets"> +<span id="lmflow-datasets"></span><h1><a class="reference internal" href="#module-lmflow.datasets" title="lmflow.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.datasets</span></code></a><a class="headerlink" href="#module-lmflow.datasets" title="Permalink to this heading">#</a></h1> +<p>This Python code defines a class Dataset with methods for initializing, loading, +and manipulating datasets from different backends such as Hugging Face and JSON.</p> +<p>The <cite>Dataset</cite> class includes methods for loading datasets from a dictionary and a Hugging +Face dataset, mapping datasets, and retrieving the backend dataset and arguments.</p> +<section id="submodules"> +<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading">#</a></h2> +<div class="toctree-wrapper compound"> +<ul> +<li class="toctree-l1"><a class="reference internal" href="dataset/index.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.datasets.dataset</span></code></a></li> +</ul> +</div> +</section> +<section id="package-contents"> +<h2>Package Contents<a class="headerlink" href="#package-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.datasets.Dataset" title="lmflow.datasets.Dataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Dataset</span></code></a></p></td> +<td><p>Initializes the Dataset object with the given parameters.</p></td> +</tr> +</tbody> +</table> +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.datasets.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'huggingface'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/lmflow/datasets/dataset.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.datasets.Dataset" title="Permalink to this definition">#</a></dt> +<dd><p>Initializes the Dataset object with the given parameters.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>data_args</strong><span class="classifier">DatasetArguments object.</span></dt><dd><p>Contains the arguments required to load the dataset.</p> +</dd> +<dt><strong>backend</strong><span class="classifier">str, default=”huggingface”</span></dt><dd><p>A string representing the dataset backend. Defaults to “huggingface”.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset._check_data_format"> +<span class="sig-name descname"><span class="pre">_check_data_format</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset._check_data_format" title="Permalink to this definition">#</a></dt> +<dd><p>Checks if data type and data structure matches</p> +<p>Raise messages with hints if not matched.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.from_dict"> +<span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict_obj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset.from_dict" title="Permalink to this definition">#</a></dt> +<dd><p>Create a Dataset object from a dictionary.</p> +<dl> +<dt>Return a Dataset given a dict with format:</dt><dd><dl> +<dt>{</dt><dd><p>“type”: TYPE, +“instances”: [</p> +<blockquote> +<div><dl class="simple"> +<dt>{</dt><dd><p>“key_1”: VALUE_1.1, +“key_2”: VALUE_1.2, +…</p> +</dd> +</dl> +<p>}, +{</p> +<blockquote> +<div><p>“key_1”: VALUE_2.1, +“key_2”: VALUE_2.2, +…</p> +</div></blockquote> +</div></blockquote> +<p>]</p> +</dd> +</dl> +<p>}</p> +</dd> +</dl> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>dict_obj</strong><span class="classifier">dict.</span></dt><dd><p>A dictionary containing the dataset information.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt><strong>self</strong><span class="classifier">Dataset object.</span></dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.create_from_dict"> +<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dict_obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset.create_from_dict" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>Returns a Dataset object given a dict.</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.to_dict"> +<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset.to_dict" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl> +<dt>Return a dict represents the dataset:</dt><dd><dl> +<dt>{</dt><dd><p>“type”: TYPE, +“instances”: [</p> +<blockquote> +<div><dl class="simple"> +<dt>{</dt><dd><p>“key_1”: VALUE_1.1, +“key_2”: VALUE_1.2, +…</p> +</dd> +</dl> +<p>}, +{</p> +<blockquote> +<div><p>“key_1”: VALUE_2.1, +“key_2”: VALUE_2.2, +…</p> +</div></blockquote> +</div></blockquote> +<p>]</p> +</dd> +</dl> +<p>}</p> +</dd> +<dt>A python dict object represents the content of this dataset.</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.to_list"> +<span class="sig-name descname"><span class="pre">to_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset.to_list" title="Permalink to this definition">#</a></dt> +<dd><p>Returns a list of instances.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.map"> +<span class="sig-name descname"><span class="pre">map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset.map" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt><strong>self</strong><span class="classifier">Dataset object.</span></dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.get_backend"> +<span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></span><a class="headerlink" href="#lmflow.datasets.Dataset.get_backend" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>self.backend</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.get_backend_dataset"> +<span class="sig-name descname"><span class="pre">get_backend_dataset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset.get_backend_dataset" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>self.backend_dataset</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.get_fingerprint"> +<span class="sig-name descname"><span class="pre">get_fingerprint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset.get_fingerprint" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>Fingerprint of the backend_dataset which controls the cache</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.get_data_args"> +<span class="sig-name descname"><span class="pre">get_data_args</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset.get_data_args" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>self.data_args</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.datasets.Dataset.get_type"> +<span class="sig-name descname"><span class="pre">get_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#lmflow.datasets.Dataset.get_type" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Returns<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>self.type</dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../index.html" + title="previous page"> + <i class="fa-solid 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toc-entry"><a class="reference internal nav-link" href="#submodules">Submodules</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#package-contents">Package Contents</a><ul class="nav section-nav flex-column"> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#classes">Classes</a><ul class="nav section-nav flex-column"> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset"><code class="docutils literal notranslate"><span class="pre">Dataset</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset._check_data_format"><code class="docutils literal notranslate"><span class="pre">Dataset._check_data_format()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.from_dict"><code class="docutils literal notranslate"><span class="pre">Dataset.from_dict()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.create_from_dict"><code class="docutils literal notranslate"><span class="pre">Dataset.create_from_dict()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.to_dict"><code class="docutils literal notranslate"><span class="pre">Dataset.to_dict()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.to_list"><code class="docutils literal notranslate"><span class="pre">Dataset.to_list()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.map"><code class="docutils literal notranslate"><span class="pre">Dataset.map()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_backend"><code class="docutils literal notranslate"><span class="pre">Dataset.get_backend()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_backend_dataset"><code class="docutils literal notranslate"><span class="pre">Dataset.get_backend_dataset()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_fingerprint"><code class="docutils literal notranslate"><span class="pre">Dataset.get_fingerprint()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_data_args"><code class="docutils literal notranslate"><span class="pre">Dataset.get_data_args()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_type"><code class="docutils literal notranslate"><span class="pre">Dataset.get_type()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset">Dataset</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset._check_data_format">_check_data_format</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.from_dict">from_dict</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.create_from_dict">create_from_dict</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.to_dict">to_dict</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.to_list">to_list</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.map">map</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_backend">get_backend</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_backend_dataset">get_backend_dataset</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_fingerprint">get_fingerprint</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_data_args">get_data_args</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.datasets.Dataset.get_type">get_type</a></li> +</ul> +</li> +</ul> 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<article class="bd-article" role="main"> + + <section id="module-lmflow.models.decoder_model"> +<span id="lmflow-models-decoder-model"></span><h1><a class="reference internal" href="#module-lmflow.models.decoder_model" title="lmflow.models.decoder_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.models.decoder_model</span></code></a><a class="headerlink" href="#module-lmflow.models.decoder_model" title="Permalink to this heading">#</a></h1> +<p>A one-line summary of the module or program, terminated by a period.</p> +<p>Leave one blank line. The rest of this docstring should contain an +overall description of the module or program. 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id="lmflow.models.decoder_model.DecoderModel"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.models.decoder_model.</span></span><span class="sig-name descname"><span class="pre">DecoderModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/decoder_model.html#DecoderModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.decoder_model.DecoderModel" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" 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id="lmflow-models-encoder-decoder-model"></span><h1><a class="reference internal" href="#module-lmflow.models.encoder_decoder_model" title="lmflow.models.encoder_decoder_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.models.encoder_decoder_model</span></code></a><a class="headerlink" href="#module-lmflow.models.encoder_decoder_model" title="Permalink to this heading">#</a></h1> +<p>A one-line summary of the module or program, terminated by a period.</p> +<p>Leave one blank line. 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class="pre">lmflow.models.hf_decoder_model</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.models.hf_decoder_model"> +<span id="lmflow-models-hf-decoder-model"></span><h1><a class="reference internal" href="#module-lmflow.models.hf_decoder_model" title="lmflow.models.hf_decoder_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.models.hf_decoder_model</span></code></a><a class="headerlink" href="#module-lmflow.models.hf_decoder_model" title="Permalink to this heading">#</a></h1> +<p>This is a class called HFDecoderModel which is a wrapper around transformers model and +tokenizer classes. It has several methods such as __init__, tokenize, and train that are +used for training and fine-tuning the model. The __init__ method takes in several arguments +such as model_args, tune_strategy, and ds_config, which are used to load the pretrained +model and tokenizer, and initialize the training settings.</p> +<p>The tokenize method is used to tokenize the input text and return the input IDs and attention +masks that can be fed to the model for training or inference.</p> +<p>This class supports different tune_strategy options such as ‘normal’, ‘none’, ‘lora’, and +‘adapter’, which allow for different fine-tuning settings of the model. However, the ‘lora’ +and ‘adapter’ strategies are not yet implemented.</p> +<p>Overall, this class provides a convenient interface for loading and fine-tuning transformer +models and can be used for various NLP tasks such as language modeling, text classification, +and question answering.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.models.hf_decoder_model.HFDecoderModel" title="lmflow.models.hf_decoder_model.HFDecoderModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HFDecoderModel</span></code></a></p></td> +<td><p>Initializes a HFDecoderModel instance.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.models.hf_decoder_model.logger" title="lmflow.models.hf_decoder_model.logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logger</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.models.hf_decoder_model.MODELS_SUPPORT_FLASH_ATTENTION" title="lmflow.models.hf_decoder_model.MODELS_SUPPORT_FLASH_ATTENTION"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MODELS_SUPPORT_FLASH_ATTENTION</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#id0" title="lmflow.models.hf_decoder_model.GPU_SUPPORT_FLASH_ATTENTION"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GPU_SUPPORT_FLASH_ATTENTION</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#id0" title="lmflow.models.hf_decoder_model.GPU_SUPPORT_FLASH_ATTENTION"><code class="xref py py-obj docutils literal notranslate"><span class="pre">GPU_SUPPORT_FLASH_ATTENTION</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.logger"> +<span class="sig-prename descclassname"><span class="pre">lmflow.models.hf_decoder_model.</span></span><span class="sig-name descname"><span class="pre">logger</span></span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.logger" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.MODELS_SUPPORT_FLASH_ATTENTION"> +<span class="sig-prename descclassname"><span class="pre">lmflow.models.hf_decoder_model.</span></span><span class="sig-name descname"><span class="pre">MODELS_SUPPORT_FLASH_ATTENTION</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">['LlamaForCausalLM',</span> <span class="pre">'GPTNeoForCausalLM',</span> <span class="pre">'GPT2ForCausalLM',</span> <span class="pre">'BloomForCausalLM']</span></em><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#MODELS_SUPPORT_FLASH_ATTENTION"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.MODELS_SUPPORT_FLASH_ATTENTION" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.GPU_SUPPORT_FLASH_ATTENTION"> +<span class="sig-prename descclassname"><span class="pre">lmflow.models.hf_decoder_model.</span></span><span class="sig-name descname"><span class="pre">GPU_SUPPORT_FLASH_ATTENTION</span></span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#GPU_SUPPORT_FLASH_ATTENTION"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.GPU_SUPPORT_FLASH_ATTENTION" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="id0"> +<span class="sig-prename descclassname"><span class="pre">lmflow.models.hf_decoder_model.</span></span><span class="sig-name descname"><span class="pre">GPU_SUPPORT_FLASH_ATTENTION</span></span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#GPU_SUPPORT_FLASH_ATTENTION"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.models.hf_decoder_model.</span></span><span class="sig-name descname"><span class="pre">HFDecoderModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tune_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'normal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ds_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gpu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_accelerator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../decoder_model/index.html#lmflow.models.decoder_model.DecoderModel" title="lmflow.models.decoder_model.DecoderModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.models.decoder_model.DecoderModel</span></code></a>, <a class="reference internal" href="../interfaces/tunable/index.html#lmflow.models.interfaces.tunable.Tunable" title="lmflow.models.interfaces.tunable.Tunable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.models.interfaces.tunable.Tunable</span></code></a></p> +<p>Initializes a HFDecoderModel instance.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model_args :</strong></dt><dd><p>Model arguments such as model name, path, revision, etc.</p> +</dd> +<dt><strong>tune_strategy</strong><span class="classifier">str or none, default=”normal”.</span></dt><dd><p>A string representing the dataset backend. Defaults to “huggingface”.</p> +</dd> +<dt><strong>ds_config :</strong></dt><dd><p>Deepspeed configuations.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel.tokenize"> +<span class="sig-name descname"><span class="pre">tokenize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_special_tokens</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel.tokenize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel.tokenize" title="Permalink to this definition">#</a></dt> +<dd><p>Tokenize the full dataset.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>dataset</strong><span class="classifier">lmflow.datasets.Dataset.</span></dt><dd></dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>tokenized_datasets :</dt><dd><p>The tokenized dataset, without any leading or trailing special +tokens (normally they are Begin-Of-Sentence or End-Of-Sentence +tokens).</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel.encode"> +<span class="sig-name descname"><span class="pre">encode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel.encode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel.encode" title="Permalink to this definition">#</a></dt> +<dd><p>Perform encoding process of the tokenizer.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>inputs</strong><span class="classifier">str or list.</span></dt><dd><p>The text sequence.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>outputs :</dt><dd><p>if string input,return the tokenized inputs. +“Hello,world!”-&gt; [101, 7592, 1010, 2088, 102] +if batch input,return {input_ids,attention_mask,token_type_ids} +[“Hello,world!”,”Hello!”]-&gt; {‘input_ids’: tensor([[ 101, 7592, 1010, 2088, 102],…),’attention_mask’: tensor([[1, 1, 1, 1, 1],[0,0,1,1,1]])}</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel.decode"> +<span class="sig-name descname"><span class="pre">decode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel.decode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel.decode" title="Permalink to this definition">#</a></dt> +<dd><p>Perform decoding process of the tokenizer.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>inputs</strong><span class="classifier">list or tensor.</span></dt><dd><p>The token sequence.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>outputs :</dt><dd><p>The text decoded from the token inputs. +if batch input,return the list of text +[[101, 7592, 1010, 2088, 102],[101, 7592, 1010, 2088, 102]]-&gt; [“Hello,world!”,”Hello,world!” +if single input,return the text +[101, 7592, 1010, 2088, 102]-&gt; “Hello,world!”</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel.inference"> +<span class="sig-name descname"><span class="pre">inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_accelerator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel.inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel.inference" title="Permalink to this definition">#</a></dt> +<dd><p>Perform generation process of the model.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>inputs :</strong></dt><dd><p>The sequence used as a prompt for the generation or as model inputs to the model.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>outputs :</dt><dd><p>The generated sequence output</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel.merge_lora_weights"> +<span class="sig-name descname"><span class="pre">merge_lora_weights</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel.merge_lora_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel.merge_lora_weights" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel.save"> +<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_full_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel.save" title="Permalink to this definition">#</a></dt> +<dd><p>Perform generation process of the model.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>dir :</strong></dt><dd><p>The directory to save model and tokenizer</p> +</dd> +<dt><strong>save_full_model</strong><span class="classifier">Optional.</span></dt><dd><p>Whether to save full model.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>outputs :</dt><dd><p>The generated sequence output</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel.get_max_length"> +<span class="sig-name descname"><span class="pre">get_max_length</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel.get_max_length"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel.get_max_length" title="Permalink to this definition">#</a></dt> +<dd><p>Return max acceptable input length in terms of tokens.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel.get_tokenizer"> +<span class="sig-name descname"><span class="pre">get_tokenizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel.get_tokenizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel.get_tokenizer" title="Permalink to this definition">#</a></dt> +<dd><p>Return the tokenizer of the model.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_decoder_model.HFDecoderModel.get_backend_model"> +<span class="sig-name descname"><span class="pre">get_backend_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_decoder_model.html#HFDecoderModel.get_backend_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_decoder_model.HFDecoderModel.get_backend_model" title="Permalink to this definition">#</a></dt> +<dd><p>Return the backend model.</p> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../encoder_decoder_model/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p 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href="../../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../index.html" class="nav-link">API Reference</a></li> + + + <li class="breadcrumb-item"><i class="fa-solid fa-ellipsis"></i></li> + + + <li class="breadcrumb-item"><a href="../index.html" class="nav-link"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.models</span></code></a></li> + + <li class="breadcrumb-item active" aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.models.hf_encoder_decoder_model</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.models.hf_encoder_decoder_model"> +<span id="lmflow-models-hf-encoder-decoder-model"></span><h1><a class="reference internal" href="#module-lmflow.models.hf_encoder_decoder_model" title="lmflow.models.hf_encoder_decoder_model"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.models.hf_encoder_decoder_model</span></code></a><a class="headerlink" href="#module-lmflow.models.hf_encoder_decoder_model" title="Permalink to this heading">#</a></h1> +<p>This is a class called HFDecoderModel which is a wrapper around transformers model and +tokenizer classes. It has several methods such as __init__, tokenize, and train that are +used for training and fine-tuning the model. The __init__ method takes in several arguments +such as model_args, tune_strategy, and ds_config, which are used to load the pretrained +model and tokenizer, and initialize the training settings.</p> +<p>The tokenize method is used to tokenize the input text and return the input IDs and attention +masks that can be fed to the model for training or inference.</p> +<p>This class supports different tune_strategy options such as ‘normal’, ‘none’, ‘lora’, and +‘adapter’, which allow for different fine-tuning settings of the model. However, the ‘lora’ +and ‘adapter’ strategies are not yet implemented.</p> +<p>Overall, this class provides a convenient interface for loading and fine-tuning transformer +models and can be used for various NLP tasks such as language modeling, text classification, +and question answering.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel" title="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">HFEncoderDecoderModel</span></code></a></p></td> +<td><p>Initializes a HFEncoderDecoderModel instance.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.models.hf_encoder_decoder_model.logger" title="lmflow.models.hf_encoder_decoder_model.logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logger</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.logger"> +<span class="sig-prename descclassname"><span class="pre">lmflow.models.hf_encoder_decoder_model.</span></span><span class="sig-name descname"><span class="pre">logger</span></span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.logger" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.models.hf_encoder_decoder_model.</span></span><span class="sig-name descname"><span class="pre">HFEncoderDecoderModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tune_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'normal'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ds_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gpu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_accelerator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../encoder_decoder_model/index.html#lmflow.models.encoder_decoder_model.EncoderDecoderModel" title="lmflow.models.encoder_decoder_model.EncoderDecoderModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.models.encoder_decoder_model.EncoderDecoderModel</span></code></a>, <a class="reference internal" href="../interfaces/tunable/index.html#lmflow.models.interfaces.tunable.Tunable" title="lmflow.models.interfaces.tunable.Tunable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.models.interfaces.tunable.Tunable</span></code></a></p> +<p>Initializes a HFEncoderDecoderModel instance.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model_args :</strong></dt><dd><p>Model arguments such as model name, path, revision, etc.</p> +</dd> +<dt><strong>tune_strategy</strong><span class="classifier">str or none, default=”normal”.</span></dt><dd><p>A string representing the dataset backend. Defaults to “huggingface”.</p> +</dd> +<dt><strong>ds_config :</strong></dt><dd><p>Deepspeed configuations.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.tokenize"> +<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">tokenize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel.tokenize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.tokenize" title="Permalink to this definition">#</a></dt> +<dd><p>Tokenize the full dataset.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>dataset :</strong></dt><dd><p>Text dataset.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>tokenized_datasets :</dt><dd><p>The tokenized dataset.</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.encode"> +<span class="sig-name descname"><span class="pre">encode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel.encode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.encode" title="Permalink to this definition">#</a></dt> +<dd><p>Perform encoding process of the tokenizer.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>inputs</strong><span class="classifier">str or list.</span></dt><dd><p>The text sequence.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>outputs :</dt><dd><p>The tokenized inputs.</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.decode"> +<span class="sig-name descname"><span class="pre">decode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel.decode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.decode" title="Permalink to this definition">#</a></dt> +<dd><p>Perform decoding process of the tokenizer.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>inputs</strong><span class="classifier">list.</span></dt><dd><p>The token sequence.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>outputs :</dt><dd><p>The text decoded from the token inputs.</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.inference"> +<span class="sig-name descname"><span class="pre">inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel.inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.inference" title="Permalink to this definition">#</a></dt> +<dd><p>Perform generation process of the model.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>inputs :</strong></dt><dd><p>The sequence used as a prompt for the generation or as model inputs to the model.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>outputs :</dt><dd><p>The generated sequence output</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.merge_lora_weights"> +<span class="sig-name descname"><span class="pre">merge_lora_weights</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel.merge_lora_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.merge_lora_weights" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.save"> +<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_full_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.save" title="Permalink to this definition">#</a></dt> +<dd><p>Perform generation process of the model.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>dir :</strong></dt><dd><p>The directory to save model and tokenizer</p> +</dd> +<dt><strong>save_full_model</strong><span class="classifier">Optional.</span></dt><dd><p>Whether to save full model.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>outputs :</dt><dd><p>The generated sequence output</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_max_length"> +<span class="sig-name descname"><span class="pre">get_max_length</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel.get_max_length"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_max_length" title="Permalink to this definition">#</a></dt> +<dd><p>Return max acceptable input length in terms of tokens.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_tokenizer"> +<span class="sig-name descname"><span class="pre">get_tokenizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel.get_tokenizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_tokenizer" title="Permalink to this definition">#</a></dt> +<dd><p>Return the tokenizer of the model.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_backend_model"> +<span class="sig-name descname"><span class="pre">get_backend_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/hf_encoder_decoder_model.html#HFEncoderDecoderModel.get_backend_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_backend_model" title="Permalink to this definition">#</a></dt> +<dd><p>Return the backend model.</p> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer 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class="reference internal nav-link" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel">HFEncoderDecoderModel</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.tokenize">tokenize</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.encode">encode</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.decode">decode</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.inference">inference</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" 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class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.models.interfaces.tunable.Tunable" title="lmflow.models.interfaces.tunable.Tunable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Tunable</span></code></a></p></td> +<td><p>Helper class that provides a standard way to create an ABC using</p></td> +</tr> +</tbody> +</table> +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.models.interfaces.tunable.Tunable"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.models.interfaces.tunable.</span></span><span class="sig-name descname"><span 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class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/regression_model.html#RegressionModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.regression_model.RegressionModel" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../base_model/index.html#lmflow.models.base_model.BaseModel" title="lmflow.models.base_model.BaseModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.models.base_model.BaseModel</span></code></a></p> +<p>Helper class that provides a standard way to create an ABC using +inheritance.</p> +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + 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href="#module-lmflow.models.text_regression_model" title="Permalink to this heading">#</a></h1> +<p>A model maps “text_only” data to float.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.models.text_regression_model.TextRegressionModel" title="lmflow.models.text_regression_model.TextRegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TextRegressionModel</span></code></a></p></td> +<td><p>Initializes a TextRegressionModel instance.</p></td> +</tr> +</tbody> +</table> +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.models.text_regression_model.TextRegressionModel"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.models.text_regression_model.</span></span><span class="sig-name descname"><span class="pre">TextRegressionModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/text_regression_model.html#TextRegressionModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.text_regression_model.TextRegressionModel" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../regression_model/index.html#lmflow.models.regression_model.RegressionModel" title="lmflow.models.regression_model.RegressionModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.models.regression_model.RegressionModel</span></code></a></p> +<p>Initializes a TextRegressionModel instance.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model_args :</strong></dt><dd><p>Model arguments such as model name, path, revision, etc.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.text_regression_model.TextRegressionModel.register_inference_function"> +<span class="sig-name descname"><span class="pre">register_inference_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inference_func</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/text_regression_model.html#TextRegressionModel.register_inference_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.text_regression_model.TextRegressionModel.register_inference_function" title="Permalink to this definition">#</a></dt> +<dd><p>Registers a regression function.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.text_regression_model.TextRegressionModel.inference"> +<span class="sig-name descname"><span class="pre">inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../../datasets/dataset/index.html#lmflow.datasets.dataset.Dataset" title="lmflow.datasets.dataset.Dataset"><span class="pre">lmflow.datasets.dataset.Dataset</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/text_regression_model.html#TextRegressionModel.inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.text_regression_model.TextRegressionModel.inference" title="Permalink to this definition">#</a></dt> +<dd><p>Gets regression results of a given dataset.</p> +<dl class="field-list simple"> +<dt class="field-odd">Inputs<span class="colon">:</span></dt> +<dd class="field-odd"><p>Dataset object, only accept type 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class="pre">lmflow.models.vision2seq_model</span></code></a><a class="headerlink" href="#module-lmflow.models.vision2seq_model" title="Permalink to this heading">#</a></h1> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel" title="lmflow.models.vision2seq_model.CustomAutoVision2SeqModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CustomAutoVision2SeqModel</span></code></a></p></td> +<td><p>An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained</p></td> +</tr> +</tbody> +</table> +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.models.vision2seq_model.CustomAutoVision2SeqModel"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.models.vision2seq_model.</span></span><span class="sig-name descname"><span class="pre">CustomAutoVision2SeqModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.Blip2Config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/vision2seq_model.html#CustomAutoVision2SeqModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">transformers.Blip2ForConditionalGeneration</span></code>, <a class="reference internal" href="../base_model/index.html#lmflow.models.base_model.BaseModel" title="lmflow.models.base_model.BaseModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.models.base_model.BaseModel</span></code></a></p> +<p>An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained +models.</p> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.vision_model_from_pretrained"> +<span class="sig-name descname"><span class="pre">vision_model_from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pretrained_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/vision2seq_model.html#CustomAutoVision2SeqModel.vision_model_from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.vision_model_from_pretrained" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.qformer_from_pretrained"> +<span class="sig-name descname"><span class="pre">qformer_from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pretrained_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/vision2seq_model.html#CustomAutoVision2SeqModel.qformer_from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.qformer_from_pretrained" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.language_model_from_pretrained"> +<span class="sig-name descname"><span class="pre">language_model_from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pretrained_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low_resource</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_prompt_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/vision2seq_model.html#CustomAutoVision2SeqModel.language_model_from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.language_model_from_pretrained" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.register_prompt_cache"> +<span class="sig-name descname"><span class="pre">register_prompt_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prompt_ids</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prompt_keys_values</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/vision2seq_model.html#CustomAutoVision2SeqModel.register_prompt_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.register_prompt_cache" title="Permalink to this definition">#</a></dt> +<dd><p>Udpate the prompt id and embedding for reuse in the future</p> +<dl class="simple"> +<dt>Args:</dt><dd><p>prompt_ids (torch.LongTensor): The id of the prompt. +prompt_keys_values (torch.FloatTensor): The embedding of the prompt.</p> +</dd> +<dt>Returns:</dt><dd><p>None</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.save_prompt_cache"> +<span class="sig-name descname"><span class="pre">save_prompt_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/vision2seq_model.html#CustomAutoVision2SeqModel.save_prompt_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.save_prompt_cache" title="Permalink to this definition">#</a></dt> +<dd><p>Save prompt embedding and id.</p> +<dl class="simple"> +<dt>Args:</dt><dd><p>path: The path to save the prompt embedding and id.</p> +</dd> +<dt>Returns:</dt><dd><p>None</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.load_prompt_cache"> +<span class="sig-name descname"><span class="pre">load_prompt_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/models/vision2seq_model.html#CustomAutoVision2SeqModel.load_prompt_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.load_prompt_cache" title="Permalink to this definition">#</a></dt> +<dd><p>Load prompt embedding and id. +Args:</p> +<blockquote> +<div><p>path: The path to load the prompt embedding and id.</p> +</div></blockquote> +<dl class="simple"> +<dt>Returns:</dt><dd><p>None</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.generate"> +<span class="sig-name descname"><span class="pre">generate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pixel_values</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.FloatTensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.LongTensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.LongTensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_token_indexes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[0]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">one_sample_multiple_images</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">generate_kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.LongTensor</span></span></span><a class="reference internal" href="../../../../_modules/lmflow/models/vision2seq_model.html#CustomAutoVision2SeqModel.generate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.generate" title="Permalink to this definition">#</a></dt> +<dd><p>Overrides <cite>generate</cite> function to be able to use the model as a conditional generator.</p> +<dl class="simple"> +<dt>Args:</dt><dd><dl class="simple"> +<dt>pixel_values (<cite>torch.FloatTensor</cite> of shape (batch_size, num_channels, height, width)):</dt><dd><p>Input images to be processed.</p> +</dd> +<dt>input_ids (<cite>torch.LongTensor</cite> of shape (batch_size, sequence_length), <em>optional</em>):</dt><dd><p>The sequence used as a prompt for the generation.</p> +</dd> +<dt>attention_mask (<cite>torch.LongTensor</cite> of shape (batch_size, sequence_length), <em>optional</em>):</dt><dd><p>Mask to avoid performing attention on padding token indices</p> +</dd> +<dt>image_token_indexes (bool, <em>optional</em>):</dt><dd><p>The index for inserting the image tokens.</p> +</dd> +<dt>one_sample_multiple_images: (bool, <em>optional</em>):</dt><dd><p>The flag for inference that the input batch size is 1 and contain multiple images.</p> +</dd> +</dl> +</dd> +<dt>Returns:</dt><dd><p>captions (list): A list of strings of length batch_size * num_captions.</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + 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header-article__inner"> + + <div class="header-article-items__start"> + + <div class="header-article-item"> + + + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../index.html" class="nav-link">API Reference</a></li> + + + <li class="breadcrumb-item"><i class="fa-solid fa-ellipsis"></i></li> + + + <li class="breadcrumb-item"><a href="../index.html" class="nav-link"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline</span></code></a></li> + + <li class="breadcrumb-item active" aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.auto_pipeline</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.pipeline.auto_pipeline"> +<span id="lmflow-pipeline-auto-pipeline"></span><h1><a class="reference internal" href="#module-lmflow.pipeline.auto_pipeline" title="lmflow.pipeline.auto_pipeline"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.auto_pipeline</span></code></a><a class="headerlink" href="#module-lmflow.pipeline.auto_pipeline" title="Permalink to this heading">#</a></h1> +<p>Return a pipeline automatically based on its name.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.auto_pipeline.AutoPipeline" title="lmflow.pipeline.auto_pipeline.AutoPipeline"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AutoPipeline</span></code></a></p></td> +<td><p>The class designed to return a pipeline automatically based on its name.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.auto_pipeline.PIPELINE_MAPPING" title="lmflow.pipeline.auto_pipeline.PIPELINE_MAPPING"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PIPELINE_MAPPING</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.auto_pipeline.PIPELINE_MAPPING"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.auto_pipeline.</span></span><span class="sig-name descname"><span class="pre">PIPELINE_MAPPING</span></span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/auto_pipeline.html#PIPELINE_MAPPING"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.auto_pipeline.PIPELINE_MAPPING" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.auto_pipeline.AutoPipeline"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.auto_pipeline.</span></span><span class="sig-name descname"><span class="pre">AutoPipeline</span></span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/auto_pipeline.html#AutoPipeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.auto_pipeline.AutoPipeline" title="Permalink to this definition">#</a></dt> +<dd><p>The class designed to return a pipeline automatically based on its name.</p> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.auto_pipeline.AutoPipeline.get_pipeline"> +<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pipeline_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipeline_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/auto_pipeline.html#AutoPipeline.get_pipeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.auto_pipeline.AutoPipeline.get_pipeline" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../utils/raft_trainer/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p 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class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.base_aligner.BaseAligner" title="lmflow.pipeline.base_aligner.BaseAligner"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseAligner</span></code></a></p></td> +<td><p>A subclass of BasePipeline which is alignable.</p></td> +</tr> +</tbody> +</table> +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.base_aligner.BaseAligner"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.base_aligner.</span></span><span class="sig-name descname"><span class="pre">BaseAligner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/base_aligner.html#BaseAligner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.base_aligner.BaseAligner" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../base_pipeline/index.html#lmflow.pipeline.base_pipeline.BasePipeline" title="lmflow.pipeline.base_pipeline.BasePipeline"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.pipeline.base_pipeline.BasePipeline</span></code></a></p> +<p>A subclass of BasePipeline which is alignable.</p> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.base_aligner.BaseAligner._check_if_alignable"> +<span class="sig-name descname"><span class="pre">_check_if_alignable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reward_model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/base_aligner.html#BaseAligner._check_if_alignable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.base_aligner.BaseAligner._check_if_alignable" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.base_aligner.BaseAligner.align"> +<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">align</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reward_model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/base_aligner.html#BaseAligner.align"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.base_aligner.BaseAligner.align" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../auto_pipeline/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div 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id="lmflow-pipeline-base-tuner"></span><h1><a class="reference internal" href="#module-lmflow.pipeline.base_tuner" title="lmflow.pipeline.base_tuner"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.base_tuner</span></code></a><a class="headerlink" href="#module-lmflow.pipeline.base_tuner" title="Permalink to this heading">#</a></h1> +<p>BaseTuner: a subclass of BasePipeline.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.base_tuner.BaseTuner" title="lmflow.pipeline.base_tuner.BaseTuner"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseTuner</span></code></a></p></td> +<td><p>A subclass of BasePipeline which is tunable.</p></td> +</tr> +</tbody> +</table> +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.base_tuner.BaseTuner"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.base_tuner.</span></span><span class="sig-name descname"><span class="pre">BaseTuner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/base_tuner.html#BaseTuner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.base_tuner.BaseTuner" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../base_pipeline/index.html#lmflow.pipeline.base_pipeline.BasePipeline" title="lmflow.pipeline.base_pipeline.BasePipeline"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.pipeline.base_pipeline.BasePipeline</span></code></a></p> +<p>A subclass of BasePipeline which is tunable.</p> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.base_tuner.BaseTuner._check_if_tunable"> +<span class="sig-name descname"><span class="pre">_check_if_tunable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/base_tuner.html#BaseTuner._check_if_tunable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.base_tuner.BaseTuner._check_if_tunable" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.base_tuner.BaseTuner.tune"> +<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">tune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/base_tuner.html#BaseTuner.tune"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" 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class="breadcrumb-item"><i class="fa-solid fa-ellipsis"></i></li> + + + <li class="breadcrumb-item"><a href="../index.html" class="nav-link"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline</span></code></a></li> + + <li class="breadcrumb-item active" aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.evaluator</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.pipeline.evaluator"> +<span id="lmflow-pipeline-evaluator"></span><h1><a class="reference internal" href="#module-lmflow.pipeline.evaluator" title="lmflow.pipeline.evaluator"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.evaluator</span></code></a><a class="headerlink" href="#module-lmflow.pipeline.evaluator" title="Permalink to this heading">#</a></h1> +<p>The Evaluator class simplifies the process of running evaluation on a language model provided by a HFDecoderModel instance imported from the lmflow package. The class constructor takes three dictionaries as arguments: model_args containing arguments related to the language model, data_args containing arguments related to the data used for evaluation, and evaluator_args containing other arguments for the evaluation process.</p> +<p>The class has two methods: create_dataloader() that loads the data from the test file, creates a data loader, and returns it with the size of the data, and evaluate(model) that generates output text given input text. It uses the create_dataloader() method to load the data, iterates over the data in mini-batches, and encodes the input text with the encode() method of the HFDecoderModel class. Then, it generates output text using the evaluate() method of the HFDecoderModel class, decodes the generated output text using the decode() method of the HFDecoderModel class, and writes the output to a file in the output directory. The method also logs some information to the console and Weights and Biases if the use_wandb argument is True.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.evaluator.Evaluator" title="lmflow.pipeline.evaluator.Evaluator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Evaluator</span></code></a></p></td> +<td><p>Initializes the <cite>Evaluator</cite> class with given arguments.</p></td> +</tr> +</tbody> +</table> +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.evaluator.Evaluator"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.evaluator.</span></span><span class="sig-name descname"><span class="pre">Evaluator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evaluator_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/evaluator.html#Evaluator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.evaluator.Evaluator" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../base_pipeline/index.html#lmflow.pipeline.base_pipeline.BasePipeline" title="lmflow.pipeline.base_pipeline.BasePipeline"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.pipeline.base_pipeline.BasePipeline</span></code></a></p> +<p>Initializes the <cite>Evaluator</cite> class with given arguments.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model_args</strong><span class="classifier">ModelArguments object.</span></dt><dd><p>Contains the arguments required to load the model.</p> +</dd> +<dt><strong>data_args</strong><span class="classifier">DatasetArguments object.</span></dt><dd><p>Contains the arguments required to load the dataset.</p> +</dd> +<dt><strong>evaluator_args</strong><span class="classifier">EvaluatorArguments object.</span></dt><dd><p>Contains the arguments required to perform evaluation.</p> +</dd> +</dl> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.evaluator.Evaluator.create_dataloader"> +<span class="sig-name descname"><span class="pre">create_dataloader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../../datasets/dataset/index.html#lmflow.datasets.dataset.Dataset" title="lmflow.datasets.dataset.Dataset"><span class="pre">lmflow.datasets.dataset.Dataset</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/evaluator.html#Evaluator.create_dataloader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.evaluator.Evaluator.create_dataloader" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.evaluator.Evaluator._match"> +<span class="sig-name descname"><span class="pre">_match</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">predicted_answer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groundtruth</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">answer_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/evaluator.html#Evaluator._match"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.evaluator.Evaluator._match" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.evaluator.Evaluator.evaluate"> +<span class="sig-name descname"><span class="pre">evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../../datasets/dataset/index.html#lmflow.datasets.dataset.Dataset" title="lmflow.datasets.dataset.Dataset"><span class="pre">lmflow.datasets.dataset.Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'accuracy'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/evaluator.html#Evaluator.evaluate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.evaluator.Evaluator.evaluate" title="Permalink to this definition">#</a></dt> +<dd><p>Perform Evaluation for a model</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model</strong><span class="classifier">TunableModel object.</span></dt><dd><p>TunableModel to perform inference</p> +</dd> +<dt><strong>dataset</strong><span class="classifier">Dataset object.</span></dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.evaluator.Evaluator._evaluate_acc_with_accelerator"> +<span class="sig-name descname"><span class="pre">_evaluate_acc_with_accelerator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/evaluator.html#Evaluator._evaluate_acc_with_accelerator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.evaluator.Evaluator._evaluate_acc_with_accelerator" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.evaluator.Evaluator._evaluate_acc_with_deepspeed"> +<span class="sig-name descname"><span class="pre">_evaluate_acc_with_deepspeed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/evaluator.html#Evaluator._evaluate_acc_with_deepspeed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.evaluator.Evaluator._evaluate_acc_with_deepspeed" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.evaluator.Evaluator._evaluate_ppl"> +<span class="sig-name descname"><span class="pre">_evaluate_ppl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../../datasets/dataset/index.html#lmflow.datasets.dataset.Dataset" title="lmflow.datasets.dataset.Dataset"><span class="pre">lmflow.datasets.dataset.Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/evaluator.html#Evaluator._evaluate_ppl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.evaluator.Evaluator._evaluate_ppl" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.evaluator.Evaluator._evaluate_nll"> +<span class="sig-name descname"><span class="pre">_evaluate_nll</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../../datasets/dataset/index.html#lmflow.datasets.dataset.Dataset" title="lmflow.datasets.dataset.Dataset"><span class="pre">lmflow.datasets.dataset.Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/evaluator.html#Evaluator._evaluate_nll"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.evaluator.Evaluator._evaluate_nll" title="Permalink to this definition">#</a></dt> +<dd><p>Evaluates negative log likelihood of the model over a dataset.</p> +<p>NLL = -1/N sum_{i=1}^N sum_{j=1}^|w_i| ln(p(w_{i,j}|context_window)),</p> +<p>where N is the number of data samples, w_{i,j} is the j-th token in +i-th sample. Here “context_window” = p(w_{i,start}, w_{i,start+1}, …, +p_{i,j-1} with start = max(0, j - window_length + 1). “window_length” +is normally the maximum length accepted by the model.</p> +<dl class="simple"> +<dt>Returns:</dt><dd><p>A float which represents the negative log likelihood.</p> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../base_tuner/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.base_tuner</span></code></p> + </div> + </a> + <a class="right-next" + href="../finetuner/index.html" + title="next page"> + <div class="prev-next-info"> + <p 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<article class="bd-article" role="main"> + + <section id="module-lmflow.pipeline.finetuner"> +<span id="lmflow-pipeline-finetuner"></span><h1><a class="reference internal" href="#module-lmflow.pipeline.finetuner" title="lmflow.pipeline.finetuner"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.finetuner</span></code></a><a class="headerlink" href="#module-lmflow.pipeline.finetuner" title="Permalink to this heading">#</a></h1> +<p>The Finetuner class simplifies the process of running finetuning process on a language model for a TunableModel instance with given dataset.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.finetuner.Finetuner" title="lmflow.pipeline.finetuner.Finetuner"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Finetuner</span></code></a></p></td> +<td><p>Initializes the <cite>Finetuner</cite> class with given arguments.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.finetuner.logger" title="lmflow.pipeline.finetuner.logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logger</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.finetuner.logger"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.finetuner.</span></span><span class="sig-name descname"><span class="pre">logger</span></span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/finetuner.html#logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.finetuner.logger" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.finetuner.Finetuner"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.finetuner.</span></span><span class="sig-name descname"><span class="pre">Finetuner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">finetuner_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/finetuner.html#Finetuner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.finetuner.Finetuner" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../base_tuner/index.html#lmflow.pipeline.base_tuner.BaseTuner" title="lmflow.pipeline.base_tuner.BaseTuner"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.pipeline.base_tuner.BaseTuner</span></code></a></p> +<p>Initializes the <cite>Finetuner</cite> class with given arguments.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model_args</strong><span class="classifier">ModelArguments object.</span></dt><dd><p>Contains the arguments required to load the model.</p> +</dd> +<dt><strong>data_args</strong><span class="classifier">DatasetArguments object.</span></dt><dd><p>Contains the arguments required to load the dataset.</p> +</dd> +<dt><strong>finetuner_args</strong><span class="classifier">FinetunerArguments object.</span></dt><dd><p>Contains the arguments required to perform finetuning.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.finetuner.Finetuner.group_text"> +<span class="sig-name descname"><span class="pre">group_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tokenized_datasets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_max_length</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/finetuner.html#Finetuner.group_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.finetuner.Finetuner.group_text" title="Permalink to this definition">#</a></dt> +<dd><p>Groups texts together to form blocks of maximum length <cite>model_max_length</cite> and returns the processed data as +a dictionary.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.finetuner.Finetuner.tune"> +<span class="sig-name descname"><span class="pre">tune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform_dataset_in_place</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/finetuner.html#Finetuner.tune"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.finetuner.Finetuner.tune" title="Permalink to this definition">#</a></dt> +<dd><p>Perform tuning for a model</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model</strong><span class="classifier">TunableModel object.</span></dt><dd><p>TunableModel to perform tuning.</p> +</dd> +<dt><strong>dataset:</strong></dt><dd><p>dataset to train model.</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../evaluator/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.evaluator</span></code></p> + </div> + </a> + <a class="right-next" + href="../inferencer/index.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span 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literal notranslate"><span class="pre">lmflow.pipeline.inferencer</span></code></a><a class="headerlink" href="#module-lmflow.pipeline.inferencer" title="Permalink to this heading">#</a></h1> +<p>The Inferencer class simplifies the process of model inferencing.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.inferencer.Inferencer" title="lmflow.pipeline.inferencer.Inferencer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Inferencer</span></code></a></p></td> +<td><p>Initializes the <cite>Inferencer</cite> class with given arguments.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="functions"> +<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.inferencer.rstrip_partial_utf8" title="lmflow.pipeline.inferencer.rstrip_partial_utf8"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rstrip_partial_utf8</span></code></a>(string)</p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.inferencer.supported_dataset_type" title="lmflow.pipeline.inferencer.supported_dataset_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">supported_dataset_type</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.pipeline.inferencer.rstrip_partial_utf8"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.inferencer.</span></span><span class="sig-name descname"><span class="pre">rstrip_partial_utf8</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">string</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/inferencer.html#rstrip_partial_utf8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.inferencer.rstrip_partial_utf8" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.inferencer.supported_dataset_type"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.inferencer.</span></span><span class="sig-name descname"><span class="pre">supported_dataset_type</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">['text_only',</span> <span class="pre">'image_text']</span></em><a class="reference internal" href="../../../../_modules/lmflow/pipeline/inferencer.html#supported_dataset_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.inferencer.supported_dataset_type" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.inferencer.Inferencer"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.inferencer.</span></span><span class="sig-name descname"><span class="pre">Inferencer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inferencer_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/inferencer.html#Inferencer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.inferencer.Inferencer" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../base_pipeline/index.html#lmflow.pipeline.base_pipeline.BasePipeline" title="lmflow.pipeline.base_pipeline.BasePipeline"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.pipeline.base_pipeline.BasePipeline</span></code></a></p> +<p>Initializes the <cite>Inferencer</cite> class with given arguments.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model_args</strong><span class="classifier">ModelArguments object.</span></dt><dd><p>Contains the arguments required to load the model.</p> +</dd> +<dt><strong>data_args</strong><span class="classifier">DatasetArguments object.</span></dt><dd><p>Contains the arguments required to load the dataset.</p> +</dd> +<dt><strong>inferencer_args</strong><span class="classifier">InferencerArguments object.</span></dt><dd><p>Contains the arguments required to perform inference.</p> +</dd> +</dl> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.inferencer.Inferencer.create_dataloader"> +<span class="sig-name descname"><span class="pre">create_dataloader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../../datasets/dataset/index.html#lmflow.datasets.dataset.Dataset" title="lmflow.datasets.dataset.Dataset"><span class="pre">lmflow.datasets.dataset.Dataset</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/inferencer.html#Inferencer.create_dataloader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.inferencer.Inferencer.create_dataloader" title="Permalink to this definition">#</a></dt> +<dd><p>Batchlize dataset and format it to dataloader.</p> +<dl class="simple"> +<dt>Args:</dt><dd><p>dataset (Dataset): the dataset object</p> +</dd> +<dt>Output:</dt><dd><p>dataloader (batchlize): the dataloader object +dataset_size (int): the length of the dataset</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.inferencer.Inferencer.inference"> +<span class="sig-name descname"><span class="pre">inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../../datasets/dataset/index.html#lmflow.datasets.dataset.Dataset" title="lmflow.datasets.dataset.Dataset"><span class="pre">lmflow.datasets.dataset.Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_new_tokens</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temperature</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prompt_structure</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'{input}'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_image_flag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/inferencer.html#Inferencer.inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.inferencer.Inferencer.inference" title="Permalink to this definition">#</a></dt> +<dd><p>Perform inference for a model</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model</strong><span class="classifier">TunableModel object.</span></dt><dd><p>TunableModel to perform inference</p> +</dd> +<dt><strong>dataset</strong><span class="classifier">Dataset object.</span></dt><dd></dd> +<dt><strong>Returns:</strong></dt><dd></dd> +<dt><strong>output_dataset: Dataset object.</strong></dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.inferencer.Inferencer.stream_inference"> +<span class="sig-name descname"><span class="pre">stream_inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">context</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_new_tokens</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_per_step</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_image_flag</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/inferencer.html#Inferencer.stream_inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.inferencer.Inferencer.stream_inference" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../finetuner/index.html" + title="previous 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section-nav flex-column"> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-contents">Module Contents</a><ul class="nav section-nav flex-column"> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#classes">Classes</a></li> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#functions">Functions</a></li> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#attributes">Attributes</a><ul class="nav section-nav flex-column"> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.rstrip_partial_utf8"><code class="docutils literal notranslate"><span class="pre">rstrip_partial_utf8()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.supported_dataset_type"><code class="docutils literal notranslate"><span class="pre">supported_dataset_type</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.Inferencer"><code class="docutils literal notranslate"><span class="pre">Inferencer</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.Inferencer.create_dataloader"><code class="docutils literal notranslate"><span class="pre">Inferencer.create_dataloader()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.Inferencer.inference"><code class="docutils literal notranslate"><span class="pre">Inferencer.inference()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.Inferencer.stream_inference"><code class="docutils literal notranslate"><span class="pre">Inferencer.stream_inference()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.rstrip_partial_utf8">rstrip_partial_utf8</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.supported_dataset_type">supported_dataset_type</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.Inferencer">Inferencer</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.Inferencer.create_dataloader">create_dataloader</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.inferencer.Inferencer.inference">inference</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" 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class="breadcrumb-item"><i class="fa-solid fa-ellipsis"></i></li> + + + <li class="breadcrumb-item"><a href="../index.html" class="nav-link"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline</span></code></a></li> + + <li class="breadcrumb-item active" aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.raft_aligner</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.pipeline.raft_aligner"> +<span id="lmflow-pipeline-raft-aligner"></span><h1><a class="reference internal" href="#module-lmflow.pipeline.raft_aligner" title="lmflow.pipeline.raft_aligner"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.raft_aligner</span></code></a><a class="headerlink" href="#module-lmflow.pipeline.raft_aligner" title="Permalink to this heading">#</a></h1> +<p>The Aligner class simplifies the process of running alignment.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.raft_aligner.RaftAligner" title="lmflow.pipeline.raft_aligner.RaftAligner"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RaftAligner</span></code></a></p></td> +<td><p>Initializes the <cite>RaftAligner</cite> class with given arguments.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.raft_aligner.logger" title="lmflow.pipeline.raft_aligner.logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logger</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.logger"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.raft_aligner.</span></span><span class="sig-name descname"><span class="pre">logger</span></span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.logger" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.RaftAligner"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.raft_aligner.</span></span><span class="sig-name descname"><span class="pre">RaftAligner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aligner_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#RaftAligner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.RaftAligner" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <a class="reference internal" href="../base_aligner/index.html#lmflow.pipeline.base_aligner.BaseAligner" title="lmflow.pipeline.base_aligner.BaseAligner"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lmflow.pipeline.base_aligner.BaseAligner</span></code></a></p> +<p>Initializes the <cite>RaftAligner</cite> class with given arguments.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model_args</strong><span class="classifier">ModelArguments object.</span></dt><dd><p>Contains the arguments required to load the model.</p> +</dd> +<dt><strong>data_args</strong><span class="classifier">DatasetArguments object.</span></dt><dd><p>Contains the arguments required to load the dataset.</p> +</dd> +<dt><strong>raft_aligner_args</strong><span class="classifier">RaftAlignerArguments object.</span></dt><dd><p>Contains the arguments required to perform alignment.</p> +</dd> +<dt><strong>args</strong><span class="classifier">Optional.</span></dt><dd><p>Positional arguments.</p> +</dd> +<dt><strong>kwargs</strong><span class="classifier">Optional.</span></dt><dd><p>Keyword arguments.</p> +</dd> +</dl> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.RaftAligner._initialize_trainer"> +<span class="sig-name descname"><span class="pre">_initialize_trainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#RaftAligner._initialize_trainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.RaftAligner._initialize_trainer" title="Permalink to this definition">#</a></dt> +<dd><p>This function takes the model and tokenizer as the input and initialize the trainer.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.RaftAligner._load_dataset"> +<span class="sig-name descname"><span class="pre">_load_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">selected_dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#RaftAligner._load_dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.RaftAligner._load_dataset" title="Permalink to this definition">#</a></dt> +<dd><p>This function prepares the dataset for every iteration.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.RaftAligner._load_input_dataset"> +<span class="sig-name descname"><span class="pre">_load_input_dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#RaftAligner._load_input_dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.RaftAligner._load_input_dataset" title="Permalink to this definition">#</a></dt> +<dd><p>Load input dataset (i.e. prompt/question dataset) for training.</p> +<dl class="simple"> +<dt>Args:</dt><dd><dl class="simple"> +<dt>dataset: A Dataset object.</dt><dd><p>The dataset to be loaded.</p> +</dd> +</dl> +</dd> +<dt>Returns:</dt><dd><dl class="simple"> +<dt>dataloader (<cite>torch.utils.data.DataLoader</cite>):</dt><dd><p>The dataloader for the dataset.</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.RaftAligner._clean_text"> +<span class="sig-name descname"><span class="pre">_clean_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#RaftAligner._clean_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.RaftAligner._clean_text" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.RaftAligner._discard_sample"> +<span class="sig-name descname"><span class="pre">_discard_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#RaftAligner._discard_sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.RaftAligner._discard_sample" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_top"> +<span class="sig-name descname"><span class="pre">_get_batch_dataset_top</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_min_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_max_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">48</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">infer_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generation_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reward_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_reward_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#RaftAligner._get_batch_dataset_top"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_top" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><p><strong>batch_input</strong> – input prompts</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_local"> +<span class="sig-name descname"><span class="pre">_get_batch_dataset_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">K</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iter_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_min_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_max_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">48</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">infer_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generation_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reward_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_reward_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#RaftAligner._get_batch_dataset_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_local" title="Permalink to this definition">#</a></dt> +<dd><dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><p><strong>batch_input</strong> – input prompts</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.raft_aligner.RaftAligner.align"> +<span class="sig-name descname"><span class="pre">align</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reward_model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/pipeline/raft_aligner.html#RaftAligner.align"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.raft_aligner.RaftAligner.align" title="Permalink to this definition">#</a></dt> +<dd><p>Perform alignment for a model</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>model</strong><span class="classifier">BaseModel object.</span></dt><dd></dd> +<dt><strong>dataset: Dataset object.</strong></dt><dd><dl class="simple"> +<dt>Input dataset for model to generate outputs. The input and output</dt><dd><p>will then be feed into reward model to get the reward for +alignment.</p> +</dd> +</dl> +</dd> +<dt><strong>reward_model: RegressionModel object.</strong></dt><dd></dd> +</dl> +</dd> +</dl> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../inferencer/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.inferencer</span></code></p> + </div> + </a> + <a class="right-next" + href="../../utils/index.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal 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href="#lmflow.pipeline.raft_aligner.logger"><code class="docutils literal notranslate"><span class="pre">logger</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner"><code class="docutils literal notranslate"><span class="pre">RaftAligner</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._initialize_trainer"><code class="docutils literal notranslate"><span class="pre">RaftAligner._initialize_trainer()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._load_dataset"><code class="docutils literal notranslate"><span class="pre">RaftAligner._load_dataset()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._load_input_dataset"><code class="docutils literal notranslate"><span class="pre">RaftAligner._load_input_dataset()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._clean_text"><code class="docutils literal notranslate"><span class="pre">RaftAligner._clean_text()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._discard_sample"><code class="docutils literal notranslate"><span class="pre">RaftAligner._discard_sample()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_top"><code class="docutils literal notranslate"><span class="pre">RaftAligner._get_batch_dataset_top()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_local"><code class="docutils literal notranslate"><span class="pre">RaftAligner._get_batch_dataset_local()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner.align"><code class="docutils literal notranslate"><span class="pre">RaftAligner.align()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.logger">logger</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner">RaftAligner</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._initialize_trainer">_initialize_trainer</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._load_dataset">_load_dataset</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._load_input_dataset">_load_input_dataset</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._clean_text">_clean_text</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._discard_sample">_discard_sample</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_top">_get_batch_dataset_top</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" 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+<span id="lmflow-pipeline-utils-peft-trainer"></span><h1><a class="reference internal" href="#module-lmflow.pipeline.utils.peft_trainer" title="lmflow.pipeline.utils.peft_trainer"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.utils.peft_trainer</span></code></a><a class="headerlink" href="#module-lmflow.pipeline.utils.peft_trainer" title="Permalink to this heading">#</a></h1> +<p>Trainer for Peft models</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.peft_trainer.PeftTrainer" title="lmflow.pipeline.utils.peft_trainer.PeftTrainer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PeftTrainer</span></code></a></p></td> +<td><p>Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback" title="lmflow.pipeline.utils.peft_trainer.PeftSavingCallback"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PeftSavingCallback</span></code></a></p></td> +<td><p>Correctly save PEFT model and not full model</p></td> +</tr> +</tbody> +</table> +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.peft_trainer.PeftTrainer"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.peft_trainer.</span></span><span class="sig-name descname"><span class="pre">PeftTrainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/peft_trainer.html#PeftTrainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.peft_trainer.PeftTrainer" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">transformers.Trainer</span></code></p> +<p>Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.</p> +<dl> +<dt>Args:</dt><dd><dl> +<dt>model ([<cite>PreTrainedModel</cite>] or <cite>torch.nn.Module</cite>, <em>optional</em>):</dt><dd><p>The model to train, evaluate or use for predictions. If not provided, a <cite>model_init</cite> must be passed.</p> +<p>&lt;Tip&gt;</p> +<p>[<cite>Trainer</cite>] is optimized to work with the [<cite>PreTrainedModel</cite>] provided by the library. You can still use +your own models defined as <cite>torch.nn.Module</cite> as long as they work the same way as the 🤗 Transformers +models.</p> +<p>&lt;/Tip&gt;</p> +</dd> +<dt>args ([<cite>TrainingArguments</cite>], <em>optional</em>):</dt><dd><p>The arguments to tweak for training. Will default to a basic instance of [<cite>TrainingArguments</cite>] with the +<cite>output_dir</cite> set to a directory named <em>tmp_trainer</em> in the current directory if not provided.</p> +</dd> +<dt>data_collator (<cite>DataCollator</cite>, <em>optional</em>):</dt><dd><p>The function to use to form a batch from a list of elements of <cite>train_dataset</cite> or <cite>eval_dataset</cite>. Will +default to [<cite>default_data_collator</cite>] if no <cite>tokenizer</cite> is provided, an instance of +[<cite>DataCollatorWithPadding</cite>] otherwise.</p> +</dd> +<dt>train_dataset (<cite>torch.utils.data.Dataset</cite> or <cite>torch.utils.data.IterableDataset</cite>, <em>optional</em>):</dt><dd><p>The dataset to use for training. If it is a [<cite>~datasets.Dataset</cite>], columns not accepted by the +<cite>model.forward()</cite> method are automatically removed.</p> +<p>Note that if it’s a <cite>torch.utils.data.IterableDataset</cite> with some randomization and you are training in a +distributed fashion, your iterable dataset should either use a internal attribute <cite>generator</cite> that is a +<cite>torch.Generator</cite> for the randomization that must be identical on all processes (and the Trainer will +manually set the seed of this <cite>generator</cite> at each epoch) or have a <cite>set_epoch()</cite> method that internally +sets the seed of the RNGs used.</p> +</dd> +<dt>eval_dataset (Union[<cite>torch.utils.data.Dataset</cite>, Dict[str, <cite>torch.utils.data.Dataset</cite>]), <em>optional</em>):</dt><dd><p>The dataset to use for evaluation. If it is a [<cite>~datasets.Dataset</cite>], columns not accepted by the +<cite>model.forward()</cite> method are automatically removed. If it is a dictionary, it will evaluate on each +dataset prepending the dictionary key to the metric name.</p> +</dd> +<dt>tokenizer ([<cite>PreTrainedTokenizerBase</cite>], <em>optional</em>):</dt><dd><p>The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the +maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an +interrupted training or reuse the fine-tuned model.</p> +</dd> +<dt>model_init (<cite>Callable[[], PreTrainedModel]</cite>, <em>optional</em>):</dt><dd><p>A function that instantiates the model to be used. If provided, each call to [<cite>~Trainer.train</cite>] will start +from a new instance of the model as given by this function.</p> +<p>The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to +be able to choose different architectures according to hyper parameters (such as layer count, sizes of +inner layers, dropout probabilities etc).</p> +</dd> +<dt>compute_metrics (<cite>Callable[[EvalPrediction], Dict]</cite>, <em>optional</em>):</dt><dd><p>The function that will be used to compute metrics at evaluation. Must take a [<cite>EvalPrediction</cite>] and return +a dictionary string to metric values.</p> +</dd> +<dt>callbacks (List of [<cite>TrainerCallback</cite>], <em>optional</em>):</dt><dd><p>A list of callbacks to customize the training loop. Will add those to the list of default callbacks +detailed in [here](callback).</p> +<p>If you want to remove one of the default callbacks used, use the [<cite>Trainer.remove_callback</cite>] method.</p> +</dd> +<dt>optimizers (<cite>Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]</cite>, <em>optional</em>): A tuple</dt><dd><p>containing the optimizer and the scheduler to use. Will default to an instance of [<cite>AdamW</cite>] on your model +and a scheduler given by [<cite>get_linear_schedule_with_warmup</cite>] controlled by <cite>args</cite>.</p> +</dd> +<dt>preprocess_logits_for_metrics (<cite>Callable[[torch.Tensor, torch.Tensor], torch.Tensor]</cite>, <em>optional</em>):</dt><dd><p>A function that preprocess the logits right before caching them at each evaluation step. Must take two +tensors, the logits and the labels, and return the logits once processed as desired. The modifications made +by this function will be reflected in the predictions received by <cite>compute_metrics</cite>.</p> +<p>Note that the labels (second parameter) will be <cite>None</cite> if the dataset does not have them.</p> +</dd> +</dl> +</dd> +</dl> +<p>Important attributes:</p> +<blockquote> +<div><ul class="simple"> +<li><p><strong>model</strong> – Always points to the core model. If using a transformers model, it will be a [<cite>PreTrainedModel</cite>] +subclass.</p></li> +<li><p><strong>model_wrapped</strong> – Always points to the most external model in case one or more other modules wrap the +original model. This is the model that should be used for the forward pass. For example, under <cite>DeepSpeed</cite>, +the inner model is wrapped in <cite>DeepSpeed</cite> and then again in <cite>torch.nn.DistributedDataParallel</cite>. If the inner +model hasn’t been wrapped, then <cite>self.model_wrapped</cite> is the same as <cite>self.model</cite>.</p></li> +<li><p><strong>is_model_parallel</strong> – Whether or not a model has been switched to a model parallel mode (different from +data parallelism, this means some of the model layers are split on different GPUs).</p></li> +<li><p><strong>place_model_on_device</strong> – Whether or not to automatically place the model on the device - it will be set +to <cite>False</cite> if model parallel or deepspeed is used, or if the default +<cite>TrainingArguments.place_model_on_device</cite> is overridden to return <cite>False</cite> .</p></li> +<li><p><strong>is_in_train</strong> – Whether or not a model is currently running <cite>train</cite> (e.g. when <cite>evaluate</cite> is called while +in <cite>train</cite>)</p></li> +</ul> +</div></blockquote> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.peft_trainer.PeftTrainer._save_checkpoint"> +<span class="sig-name descname"><span class="pre">_save_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metrics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/peft_trainer.html#PeftTrainer._save_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.peft_trainer.PeftTrainer._save_checkpoint" title="Permalink to this definition">#</a></dt> +<dd><p>Don’t save base model, optimizer etc. +but create checkpoint folder (needed for saving adapter)</p> +</dd></dl> + +</dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.peft_trainer.PeftSavingCallback"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.peft_trainer.</span></span><span class="sig-name descname"><span class="pre">PeftSavingCallback</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/peft_trainer.html#PeftSavingCallback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">transformers.trainer_callback.TrainerCallback</span></code></p> +<p>Correctly save PEFT model and not full model</p> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.peft_trainer.PeftSavingCallback._save"> +<span class="sig-name descname"><span class="pre">_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">folder</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/peft_trainer.html#PeftSavingCallback._save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback._save" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_train_end"> +<span class="sig-name descname"><span class="pre">on_train_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.training_args.TrainingArguments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.trainer_callback.TrainerState</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">control</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.trainer_callback.TrainerControl</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/peft_trainer.html#PeftSavingCallback.on_train_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_train_end" title="Permalink to this definition">#</a></dt> +<dd><p>Save final best model adapter</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_epoch_end"> +<span class="sig-name descname"><span class="pre">on_epoch_end</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.training_args.TrainingArguments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.trainer_callback.TrainerState</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">control</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.trainer_callback.TrainerControl</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/peft_trainer.html#PeftSavingCallback.on_epoch_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_epoch_end" title="Permalink to this definition">#</a></dt> +<dd><p>Save intermediate model adapters in case of interrupted training</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_save"> +<span class="sig-name descname"><span class="pre">on_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.training_args.TrainingArguments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.trainer_callback.TrainerState</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">control</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.trainer_callback.TrainerControl</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/peft_trainer.html#PeftSavingCallback.on_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_save" title="Permalink to this definition">#</a></dt> +<dd><p>Event called after a checkpoint save.</p> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span 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class="reference internal nav-link" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback._save"><code class="docutils literal notranslate"><span class="pre">PeftSavingCallback._save()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_train_end"><code class="docutils literal notranslate"><span class="pre">PeftSavingCallback.on_train_end()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_epoch_end"><code class="docutils literal notranslate"><span class="pre">PeftSavingCallback.on_epoch_end()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_save"><code class="docutils literal notranslate"><span 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href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer" title="lmflow.pipeline.utils.raft_trainer.RaftTrainer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RaftTrainer</span></code></a></p></td> +<td><p>Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.is_torch_greater_or_equal_than_1_10" title="lmflow.pipeline.utils.raft_trainer.is_torch_greater_or_equal_than_1_10"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_greater_or_equal_than_1_10</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.is_torch_less_than_1_11" title="lmflow.pipeline.utils.raft_trainer.is_torch_less_than_1_11"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_torch_less_than_1_11</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer._is_native_cpu_amp_available" title="lmflow.pipeline.utils.raft_trainer._is_native_cpu_amp_available"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_is_native_cpu_amp_available</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.DEFAULT_CALLBACKS" title="lmflow.pipeline.utils.raft_trainer.DEFAULT_CALLBACKS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DEFAULT_CALLBACKS</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#id0" title="lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DEFAULT_PROGRESS_CALLBACK</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#id0" title="lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DEFAULT_PROGRESS_CALLBACK</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.IS_SAGEMAKER_MP_POST_1_10" title="lmflow.pipeline.utils.raft_trainer.IS_SAGEMAKER_MP_POST_1_10"><code class="xref py py-obj docutils literal notranslate"><span class="pre">IS_SAGEMAKER_MP_POST_1_10</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.skip_first_batches" title="lmflow.pipeline.utils.raft_trainer.skip_first_batches"><code class="xref py py-obj docutils literal notranslate"><span class="pre">skip_first_batches</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.logger" title="lmflow.pipeline.utils.raft_trainer.logger"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logger</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.TRAINING_ARGS_NAME" title="lmflow.pipeline.utils.raft_trainer.TRAINING_ARGS_NAME"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TRAINING_ARGS_NAME</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.TRAINER_STATE_NAME" title="lmflow.pipeline.utils.raft_trainer.TRAINER_STATE_NAME"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TRAINER_STATE_NAME</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.OPTIMIZER_NAME" title="lmflow.pipeline.utils.raft_trainer.OPTIMIZER_NAME"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OPTIMIZER_NAME</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.SCHEDULER_NAME" title="lmflow.pipeline.utils.raft_trainer.SCHEDULER_NAME"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SCHEDULER_NAME</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.pipeline.utils.raft_trainer.SCALER_NAME" title="lmflow.pipeline.utils.raft_trainer.SCALER_NAME"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SCALER_NAME</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.is_torch_greater_or_equal_than_1_10"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">is_torch_greater_or_equal_than_1_10</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#is_torch_greater_or_equal_than_1_10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.is_torch_greater_or_equal_than_1_10" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.is_torch_less_than_1_11"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">is_torch_less_than_1_11</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#is_torch_less_than_1_11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.is_torch_less_than_1_11" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer._is_native_cpu_amp_available"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">_is_native_cpu_amp_available</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#_is_native_cpu_amp_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer._is_native_cpu_amp_available" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.DEFAULT_CALLBACKS"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">DEFAULT_CALLBACKS</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#DEFAULT_CALLBACKS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.DEFAULT_CALLBACKS" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">DEFAULT_PROGRESS_CALLBACK</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#DEFAULT_PROGRESS_CALLBACK"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="id0"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">DEFAULT_PROGRESS_CALLBACK</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#DEFAULT_PROGRESS_CALLBACK"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.IS_SAGEMAKER_MP_POST_1_10"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">IS_SAGEMAKER_MP_POST_1_10</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#IS_SAGEMAKER_MP_POST_1_10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.IS_SAGEMAKER_MP_POST_1_10" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.skip_first_batches"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">skip_first_batches</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#skip_first_batches"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.skip_first_batches" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.logger"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">logger</span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.logger" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.TRAINING_ARGS_NAME"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">TRAINING_ARGS_NAME</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">training_args.bin</span></em><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#TRAINING_ARGS_NAME"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.TRAINING_ARGS_NAME" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.TRAINER_STATE_NAME"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">TRAINER_STATE_NAME</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">trainer_state.json</span></em><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#TRAINER_STATE_NAME"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.TRAINER_STATE_NAME" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.OPTIMIZER_NAME"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">OPTIMIZER_NAME</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">optimizer.pt</span></em><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#OPTIMIZER_NAME"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.OPTIMIZER_NAME" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.SCHEDULER_NAME"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">SCHEDULER_NAME</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">scheduler.pt</span></em><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#SCHEDULER_NAME"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.SCHEDULER_NAME" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.SCALER_NAME"> +<span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">SCALER_NAME</span></span><em class="property"><span class="w"> </span><span class="pre">=</span> <span class="pre">scaler.pt</span></em><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#SCALER_NAME"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.SCALER_NAME" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.pipeline.utils.raft_trainer.</span></span><span class="sig-name descname"><span class="pre">RaftTrainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">transformers.modeling_utils.PreTrainedModel</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.nn.Module</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.training_args.TrainingArguments</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_collator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">transformers.data.data_collator.DataCollator</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.utils.data.Dataset</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.utils.data.Dataset</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.utils.data.Dataset</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokenizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">transformers.tokenization_utils_base.PreTrainedTokenizerBase</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">transformers.modeling_utils.PreTrainedModel</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_metrics</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">transformers.trainer_utils.EvalPrediction</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callbacks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">transformers.trainer_callback.TrainerCallback</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.optim.Optimizer</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.optim.lr_scheduler.LambdaLR</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">(None,</span> <span class="pre">None)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocess_logits_for_metrics</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer" title="Permalink to this definition">#</a></dt> +<dd><p>Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>model ([<cite>PreTrainedModel</cite>] or <cite>torch.nn.Module</cite>, <em>optional</em>):</dt><dd><p>The model to train, evaluate or use for predictions. If not provided, a <cite>model_init</cite> must be passed. +&lt;Tip&gt; +[<cite>Trainer</cite>] is optimized to work with the [<cite>PreTrainedModel</cite>] provided by the library. You can still use +your own models defined as <cite>torch.nn.Module</cite> as long as they work the same way as the 🤗 Transformers +models. +&lt;/Tip&gt;</p> +</dd> +<dt>args ([<cite>TrainingArguments</cite>], <em>optional</em>):</dt><dd><p>The arguments to tweak for training. Will default to a basic instance of [<cite>TrainingArguments</cite>] with the +<cite>output_dir</cite> set to a directory named <em>tmp_trainer</em> in the current directory if not provided.</p> +</dd> +<dt>data_collator (<cite>DataCollator</cite>, <em>optional</em>):</dt><dd><p>The function to use to form a batch from a list of elements of <cite>train_dataset</cite> or <cite>eval_dataset</cite>. Will +default to [<cite>default_data_collator</cite>] if no <cite>tokenizer</cite> is provided, an instance of +[<cite>DataCollatorWithPadding</cite>] otherwise.</p> +</dd> +<dt>train_dataset (<cite>torch.utils.data.Dataset</cite> or <cite>torch.utils.data.IterableDataset</cite>, <em>optional</em>):</dt><dd><p>The dataset to use for training. If it is a [<cite>~datasets.Dataset</cite>], columns not accepted by the +<cite>model.forward()</cite> method are automatically removed. +Note that if it’s a <cite>torch.utils.data.IterableDataset</cite> with some randomization and you are training in a +distributed fashion, your iterable dataset should either use a internal attribute <cite>generator</cite> that is a +<cite>torch.Generator</cite> for the randomization that must be identical on all processes (and the Trainer will +manually set the seed of this <cite>generator</cite> at each epoch) or have a <cite>set_epoch()</cite> method that internally +sets the seed of the RNGs used.</p> +</dd> +<dt>eval_dataset (Union[<cite>torch.utils.data.Dataset</cite>, Dict[str, <cite>torch.utils.data.Dataset</cite>]), <em>optional</em>):</dt><dd><p>The dataset to use for evaluation. If it is a [<cite>~datasets.Dataset</cite>], columns not accepted by the +<cite>model.forward()</cite> method are automatically removed. If it is a dictionary, it will evaluate on each +dataset prepending the dictionary key to the metric name.</p> +</dd> +<dt>tokenizer ([<cite>PreTrainedTokenizerBase</cite>], <em>optional</em>):</dt><dd><p>The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to the +maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an +interrupted training or reuse the fine-tuned model.</p> +</dd> +<dt>model_init (<cite>Callable[[], PreTrainedModel]</cite>, <em>optional</em>):</dt><dd><p>A function that instantiates the model to be used. If provided, each call to [<cite>~Trainer.train</cite>] will start +from a new instance of the model as given by this function. +The function may have zero argument, or a single one containing the optuna/Ray Tune/SigOpt trial object, to +be able to choose different architectures according to hyper parameters (such as layer count, sizes of +inner layers, dropout probabilities etc).</p> +</dd> +<dt>compute_metrics (<cite>Callable[[EvalPrediction], Dict]</cite>, <em>optional</em>):</dt><dd><p>The function that will be used to compute metrics at evaluation. Must take a [<cite>EvalPrediction</cite>] and return +a dictionary string to metric values.</p> +</dd> +<dt>callbacks (List of [<cite>TrainerCallback</cite>], <em>optional</em>):</dt><dd><p>A list of callbacks to customize the training loop. Will add those to the list of default callbacks +detailed in [here](callback). +If you want to remove one of the default callbacks used, use the [<cite>Trainer.remove_callback</cite>] method.</p> +</dd> +<dt>optimizers (<cite>Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]</cite>, <em>optional</em>): A tuple</dt><dd><p>containing the optimizer and the scheduler to use. Will default to an instance of [<cite>AdamW</cite>] on your model +and a scheduler given by [<cite>get_linear_schedule_with_warmup</cite>] controlled by <cite>args</cite>.</p> +</dd> +<dt>preprocess_logits_for_metrics (<cite>Callable[[torch.Tensor, torch.Tensor], torch.Tensor]</cite>, <em>optional</em>):</dt><dd><p>A function that preprocess the logits right before caching them at each evaluation step. Must take two +tensors, the logits and the labels, and return the logits once processed as desired. The modifications made +by this function will be reflected in the predictions received by <cite>compute_metrics</cite>. +Note that the labels (second parameter) will be <cite>None</cite> if the dataset does not have them.</p> +</dd> +</dl> +</div></blockquote> +<dl class="simple"> +<dt>Important attributes:</dt><dd><ul class="simple"> +<li><p><strong>model</strong> – Always points to the core model. If using a transformers model, it will be a [<cite>PreTrainedModel</cite>] +subclass.</p></li> +<li><p><strong>model_wrapped</strong> – Always points to the most external model in case one or more other modules wrap the +original model. This is the model that should be used for the forward pass. For example, under <cite>DeepSpeed</cite>, +the inner model is wrapped in <cite>DeepSpeed</cite> and then again in <cite>torch.nn.DistributedDataParallel</cite>. If the inner +model hasn’t been wrapped, then <cite>self.model_wrapped</cite> is the same as <cite>self.model</cite>.</p></li> +<li><p><strong>is_model_parallel</strong> – Whether or not a model has been switched to a model parallel mode (different from +data parallelism, this means some of the model layers are split on different GPUs).</p></li> +<li><p><strong>place_model_on_device</strong> – Whether or not to automatically place the model on the device - it will be set +to <cite>False</cite> if model parallel or deepspeed is used, or if the default +<cite>TrainingArguments.place_model_on_device</cite> is overridden to return <cite>False</cite> .</p></li> +<li><p><strong>is_in_train</strong> – Whether or not a model is currently running <cite>train</cite> (e.g. when <cite>evaluate</cite> is called while +in <cite>train</cite>)</p></li> +</ul> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.add_callback"> +<span class="sig-name descname"><span class="pre">add_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.add_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.add_callback" title="Permalink to this definition">#</a></dt> +<dd><p>Add a callback to the current list of [<cite>~transformer.TrainerCallback</cite>]. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>callback (<cite>type</cite> or [<cite>~transformer.TrainerCallback</cite>]):</dt><dd><p>A [<cite>~transformer.TrainerCallback</cite>] class or an instance of a [<cite>~transformer.TrainerCallback</cite>]. In the +first case, will instantiate a member of that class.</p> +</dd> +</dl> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.pop_callback"> +<span class="sig-name descname"><span class="pre">pop_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.pop_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.pop_callback" title="Permalink to this definition">#</a></dt> +<dd><p>Remove a callback from the current list of [<cite>~transformer.TrainerCallback</cite>] and returns it. +If the callback is not found, returns <cite>None</cite> (and no error is raised). +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>callback (<cite>type</cite> or [<cite>~transformer.TrainerCallback</cite>]):</dt><dd><p>A [<cite>~transformer.TrainerCallback</cite>] class or an instance of a [<cite>~transformer.TrainerCallback</cite>]. In the +first case, will pop the first member of that class found in the list of callbacks.</p> +</dd> +</dl> +</div></blockquote> +<dl class="simple"> +<dt>Returns:</dt><dd><p>[<cite>~transformer.TrainerCallback</cite>]: The callback removed, if found.</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.remove_callback"> +<span class="sig-name descname"><span class="pre">remove_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.remove_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.remove_callback" title="Permalink to this definition">#</a></dt> +<dd><p>Remove a callback from the current list of [<cite>~transformer.TrainerCallback</cite>]. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>callback (<cite>type</cite> or [<cite>~transformer.TrainerCallback</cite>]):</dt><dd><p>A [<cite>~transformer.TrainerCallback</cite>] class or an instance of a [<cite>~transformer.TrainerCallback</cite>]. In the +first case, will remove the first member of that class found in the list of callbacks.</p> +</dd> +</dl> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._move_model_to_device"> +<span class="sig-name descname"><span class="pre">_move_model_to_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._move_model_to_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._move_model_to_device" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._set_signature_columns_if_needed"> +<span class="sig-name descname"><span class="pre">_set_signature_columns_if_needed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._set_signature_columns_if_needed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._set_signature_columns_if_needed" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._remove_unused_columns"> +<span class="sig-name descname"><span class="pre">_remove_unused_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../../../datasets/index.html#lmflow.datasets.Dataset" title="lmflow.datasets.Dataset"><span class="pre">datasets.Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._remove_unused_columns"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._remove_unused_columns" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_collator_with_removed_columns"> +<span class="sig-name descname"><span class="pre">_get_collator_with_removed_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_collator</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Callable</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._get_collator_with_removed_columns"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_collator_with_removed_columns" title="Permalink to this definition">#</a></dt> +<dd><p>Wrap the data collator in a callable removing unused columns.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_train_sampler"> +<span class="sig-name descname"><span class="pre">_get_train_sampler</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.utils.data.Sampler</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._get_train_sampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_train_sampler" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_train_dataloader"> +<span class="sig-name descname"><span class="pre">get_train_dataloader</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.utils.data.DataLoader</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.get_train_dataloader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_train_dataloader" title="Permalink to this definition">#</a></dt> +<dd><p>Returns the training [<cite>~torch.utils.data.DataLoader</cite>]. +Will use no sampler if <cite>train_dataset</cite> does not implement <cite>__len__</cite>, a random sampler (adapted to distributed +training if necessary) otherwise. +Subclass and override this method if you want to inject some custom behavior.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_eval_sampler"> +<span class="sig-name descname"><span class="pre">_get_eval_sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eval_dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils.data.Dataset</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.utils.data.Sampler</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._get_eval_sampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_eval_sampler" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_eval_dataloader"> +<span class="sig-name descname"><span class="pre">get_eval_dataloader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eval_dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.utils.data.Dataset</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.utils.data.DataLoader</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.get_eval_dataloader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_eval_dataloader" title="Permalink to this definition">#</a></dt> +<dd><p>Returns the evaluation [<cite>~torch.utils.data.DataLoader</cite>]. +Subclass and override this method if you want to inject some custom behavior. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>eval_dataset (<cite>torch.utils.data.Dataset</cite>, <em>optional</em>):</dt><dd><p>If provided, will override <cite>self.eval_dataset</cite>. If it is a [<cite>~datasets.Dataset</cite>], columns not accepted +by the <cite>model.forward()</cite> method are automatically removed. It must implement <cite>__len__</cite>.</p> +</dd> +</dl> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_test_dataloader"> +<span class="sig-name descname"><span class="pre">get_test_dataloader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">test_dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils.data.Dataset</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.utils.data.DataLoader</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.get_test_dataloader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_test_dataloader" title="Permalink to this definition">#</a></dt> +<dd><p>Returns the test [<cite>~torch.utils.data.DataLoader</cite>]. +Subclass and override this method if you want to inject some custom behavior. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>test_dataset (<cite>torch.utils.data.Dataset</cite>, <em>optional</em>):</dt><dd><p>The test dataset to use. If it is a [<cite>~datasets.Dataset</cite>], columns not accepted by the +<cite>model.forward()</cite> method are automatically removed. It must implement <cite>__len__</cite>.</p> +</dd> +</dl> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer_and_scheduler"> +<span class="sig-name descname"><span class="pre">create_optimizer_and_scheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_training_steps</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.create_optimizer_and_scheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer_and_scheduler" title="Permalink to this definition">#</a></dt> +<dd><p>Setup the optimizer and the learning rate scheduler. +We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the +Trainer’s init through <cite>optimizers</cite>, or subclass and override this method (or <cite>create_optimizer</cite> and/or +<cite>create_scheduler</cite>) in a subclass.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer"> +<span class="sig-name descname"><span class="pre">create_optimizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.create_optimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer" title="Permalink to this definition">#</a></dt> +<dd><p>Setup the optimizer. +We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the +Trainer’s init through <cite>optimizers</cite>, or subclass and override this method in a subclass.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_optimizer_cls_and_kwargs"> +<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_optimizer_cls_and_kwargs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">transformers.training_args.TrainingArguments</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.get_optimizer_cls_and_kwargs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_optimizer_cls_and_kwargs" title="Permalink to this definition">#</a></dt> +<dd><p>Returns the optimizer class and optimizer parameters based on the training arguments. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>args (<cite>transformers.training_args.TrainingArguments</cite>):</dt><dd><p>The training arguments for the training session.</p> +</dd> +</dl> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_scheduler"> +<span class="sig-name descname"><span class="pre">create_scheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_training_steps</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.optim.Optimizer</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.create_scheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_scheduler" title="Permalink to this definition">#</a></dt> +<dd><p>Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or +passed as an argument. +Args:</p> +<blockquote> +<div><p>num_training_steps (int): The number of training steps to do.</p> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.num_examples"> +<span class="sig-name descname"><span class="pre">num_examples</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataloader</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils.data.DataLoader</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">int</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.num_examples"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.num_examples" title="Permalink to this definition">#</a></dt> +<dd><p>Helper to get number of samples in a [<cite>~torch.utils.data.DataLoader</cite>] by accessing its dataset. When +dataloader.dataset does not exist or has no length, estimates as best it can</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._hp_search_setup"> +<span class="sig-name descname"><span class="pre">_hp_search_setup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">optuna.Trial</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._hp_search_setup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._hp_search_setup" title="Permalink to this definition">#</a></dt> +<dd><p>HP search setup code</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._report_to_hp_search"> +<span class="sig-name descname"><span class="pre">_report_to_hp_search</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">optuna.Trial</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metrics</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._report_to_hp_search"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._report_to_hp_search" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._tune_save_checkpoint"> +<span class="sig-name descname"><span class="pre">_tune_save_checkpoint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._tune_save_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._tune_save_checkpoint" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.call_model_init"> +<span class="sig-name descname"><span class="pre">call_model_init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.call_model_init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.call_model_init" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.torch_jit_model_eval"> +<span class="sig-name descname"><span class="pre">torch_jit_model_eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataloader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.torch_jit_model_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.torch_jit_model_eval" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.ipex_optimize_model"> +<span class="sig-name descname"><span class="pre">ipex_optimize_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float32</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.ipex_optimize_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.ipex_optimize_model" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._wrap_model"> +<span class="sig-name descname"><span class="pre">_wrap_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataloader</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._wrap_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._wrap_model" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.train"> +<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">resume_from_checkpoint</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">optuna.Trial</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_keys_for_eval</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_first_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.train" title="Permalink to this definition">#</a></dt> +<dd><p>Main training entry point. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>resume_from_checkpoint (<cite>str</cite> or <cite>bool</cite>, <em>optional</em>):</dt><dd><p>If a <cite>str</cite>, local path to a saved checkpoint as saved by a previous instance of [<cite>Trainer</cite>]. If a +<cite>bool</cite> and equals <cite>True</cite>, load the last checkpoint in <em>args.output_dir</em> as saved by a previous instance +of [<cite>Trainer</cite>]. If present, training will resume from the model/optimizer/scheduler states loaded here.</p> +</dd> +<dt>trial (<cite>optuna.Trial</cite> or <cite>Dict[str, Any]</cite>, <em>optional</em>):</dt><dd><p>The trial run or the hyperparameter dictionary for hyperparameter search.</p> +</dd> +<dt>ignore_keys_for_eval (<cite>List[str]</cite>, <em>optional</em>)</dt><dd><p>A list of keys in the output of your model (if it is a dictionary) that should be ignored when +gathering predictions for evaluation during the training.</p> +</dd> +<dt>kwargs:</dt><dd><p>Additional keyword arguments used to hide deprecated arguments</p> +</dd> +</dl> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._one_train"> +<span class="sig-name descname"><span class="pre">_one_train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resume_from_checkpoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_keys_for_eval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._one_train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._one_train" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._inner_training_loop"> +<span class="sig-name descname"><span class="pre">_inner_training_loop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resume_from_checkpoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_keys_for_eval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._inner_training_loop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._inner_training_loop" title="Permalink to this definition">#</a></dt> +<dd><p>0 This function serves to train one time +1 Update the self.train_dataset before calling this function</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_output_dir"> +<span class="sig-name descname"><span class="pre">_get_output_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._get_output_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_output_dir" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_from_checkpoint"> +<span class="sig-name descname"><span class="pre">_load_from_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">resume_from_checkpoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._load_from_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_from_checkpoint" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_best_model"> +<span class="sig-name descname"><span class="pre">_load_best_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._load_best_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_best_model" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._issue_warnings_after_load"> +<span class="sig-name descname"><span class="pre">_issue_warnings_after_load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">load_result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._issue_warnings_after_load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._issue_warnings_after_load" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._maybe_log_save_evaluate"> +<span class="sig-name descname"><span class="pre">_maybe_log_save_evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tr_loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_keys_for_eval</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._maybe_log_save_evaluate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._maybe_log_save_evaluate" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_rng_state"> +<span class="sig-name descname"><span class="pre">_load_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._load_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_rng_state" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_checkpoint"> +<span class="sig-name descname"><span class="pre">_save_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metrics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._save_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_checkpoint" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_optimizer_and_scheduler"> +<span class="sig-name descname"><span class="pre">_load_optimizer_and_scheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._load_optimizer_and_scheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_optimizer_and_scheduler" title="Permalink to this definition">#</a></dt> +<dd><p>If optimizer and scheduler states exist, load them.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.hyperparameter_search"> +<span class="sig-name descname"><span class="pre">hyperparameter_search</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hp_space</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">optuna.Trial</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_objective</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_trials</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">direction</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'minimize'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">transformers.trainer_utils.HPSearchBackend</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hp_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">optuna.Trial</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">transformers.trainer_utils.BestRun</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.hyperparameter_search"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.hyperparameter_search" title="Permalink to this definition">#</a></dt> +<dd><p>Launch an hyperparameter search using <cite>optuna</cite> or <cite>Ray Tune</cite> or <cite>SigOpt</cite>. The optimized quantity is determined +by <cite>compute_objective</cite>, which defaults to a function returning the evaluation loss when no metric is provided, +the sum of all metrics otherwise. +&lt;Tip warning={true}&gt; +To use this method, you need to have provided a <cite>model_init</cite> when initializing your [<cite>Trainer</cite>]: we need to +reinitialize the model at each new run. This is incompatible with the <cite>optimizers</cite> argument, so you need to +subclass [<cite>Trainer</cite>] and override the method [<cite>~Trainer.create_optimizer_and_scheduler</cite>] for custom +optimizer/scheduler. +&lt;/Tip&gt; +Args:</p> +<blockquote> +<div><dl> +<dt>hp_space (<cite>Callable[[“optuna.Trial”], Dict[str, float]]</cite>, <em>optional</em>):</dt><dd><p>A function that defines the hyperparameter search space. Will default to +[<cite>~trainer_utils.default_hp_space_optuna</cite>] or [<cite>~trainer_utils.default_hp_space_ray</cite>] or +[<cite>~trainer_utils.default_hp_space_sigopt</cite>] depending on your backend.</p> +</dd> +<dt>compute_objective (<cite>Callable[[Dict[str, float]], float]</cite>, <em>optional</em>):</dt><dd><p>A function computing the objective to minimize or maximize from the metrics returned by the <cite>evaluate</cite> +method. Will default to [<cite>~trainer_utils.default_compute_objective</cite>].</p> +</dd> +<dt>n_trials (<cite>int</cite>, <em>optional</em>, defaults to 100):</dt><dd><p>The number of trial runs to test.</p> +</dd> +<dt>direction (<cite>str</cite>, <em>optional</em>, defaults to <cite>“minimize”</cite>):</dt><dd><p>Whether to optimize greater or lower objects. Can be <cite>“minimize”</cite> or <cite>“maximize”</cite>, you should pick +<cite>“minimize”</cite> when optimizing the validation loss, <cite>“maximize”</cite> when optimizing one or several metrics.</p> +</dd> +<dt>backend (<cite>str</cite> or [<cite>~training_utils.HPSearchBackend</cite>], <em>optional</em>):</dt><dd><p>The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending +on which one is installed. If all are installed, will default to optuna.</p> +</dd> +<dt>hp_name (<cite>Callable[[“optuna.Trial”], str]]</cite>, <em>optional</em>):</dt><dd><p>A function that defines the trial/run name. Will default to None.</p> +</dd> +<dt>kwargs (<cite>Dict[str, Any]</cite>, <em>optional</em>):</dt><dd><p>Additional keyword arguments passed along to <cite>optuna.create_study</cite> or <cite>ray.tune.run</cite>. For more +information see: +- the documentation of</p> +<blockquote> +<div><p>[optuna.create_study](<a class="reference external" href="https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html">https://optuna.readthedocs.io/en/stable/reference/generated/optuna.study.create_study.html</a>)</p> +</div></blockquote> +<ul class="simple"> +<li><p>the documentation of [tune.run](<a class="reference external" href="https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run">https://docs.ray.io/en/latest/tune/api_docs/execution.html#tune-run</a>)</p></li> +<li><p>the documentation of [sigopt](<a class="reference external" href="https://app.sigopt.com/docs/endpoints/experiments/create">https://app.sigopt.com/docs/endpoints/experiments/create</a>)</p></li> +</ul> +</dd> +</dl> +</div></blockquote> +<dl class="simple"> +<dt>Returns:</dt><dd><p>[<cite>trainer_utils.BestRun</cite>]: All the information about the best run. Experiment summary can be found in +<cite>run_summary</cite> attribute for Ray backend.</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.log"> +<span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.log"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.log" title="Permalink to this definition">#</a></dt> +<dd><p>Log <cite>logs</cite> on the various objects watching training. +Subclass and override this method to inject custom behavior. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>logs (<cite>Dict[str, float]</cite>):</dt><dd><p>The values to log.</p> +</dd> +</dl> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_input"> +<span class="sig-name descname"><span class="pre">_prepare_input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._prepare_input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_input" title="Permalink to this definition">#</a></dt> +<dd><p>Prepares one <cite>data</cite> before feeding it to the model, be it a tensor or a nested list/dictionary of tensors.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_inputs"> +<span class="sig-name descname"><span class="pre">_prepare_inputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._prepare_inputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_inputs" title="Permalink to this definition">#</a></dt> +<dd><p>Prepare <cite>inputs</cite> before feeding them to the model, converting them to tensors if they are not already and +handling potential state.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss_context_manager"> +<span class="sig-name descname"><span class="pre">compute_loss_context_manager</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.compute_loss_context_manager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss_context_manager" title="Permalink to this definition">#</a></dt> +<dd><p>A helper wrapper to group together context managers.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.autocast_smart_context_manager"> +<span class="sig-name descname"><span class="pre">autocast_smart_context_manager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.autocast_smart_context_manager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.autocast_smart_context_manager" title="Permalink to this definition">#</a></dt> +<dd><p>A helper wrapper that creates an appropriate context manager for <cite>autocast</cite> while feeding it the desired +arguments, depending on the situation.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.training_step"> +<span class="sig-name descname"><span class="pre">training_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.nn.Module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.Tensor</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.training_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.training_step" title="Permalink to this definition">#</a></dt> +<dd><p>Perform a training step on a batch of inputs. +Subclass and override to inject custom behavior. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>model (<cite>nn.Module</cite>):</dt><dd><p>The model to train.</p> +</dd> +<dt>inputs (<cite>Dict[str, Union[torch.Tensor, Any]]</cite>):</dt><dd><p>The inputs and targets of the model. +The dictionary will be unpacked before being fed to the model. Most models expect the targets under the +argument <cite>labels</cite>. Check your model’s documentation for all accepted arguments.</p> +</dd> +</dl> +</div></blockquote> +<dl class="simple"> +<dt>Return:</dt><dd><p><cite>torch.Tensor</cite>: The tensor with training loss on this batch.</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss"> +<span class="sig-name descname"><span class="pre">compute_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.compute_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss" title="Permalink to this definition">#</a></dt> +<dd><p>How the loss is computed by Trainer. By default, all models return the loss in the first element. +Subclass and override for custom behavior.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_local_process_zero"> +<span class="sig-name descname"><span class="pre">is_local_process_zero</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.is_local_process_zero"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_local_process_zero" title="Permalink to this definition">#</a></dt> +<dd><p>Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several +machines) main process.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_world_process_zero"> +<span class="sig-name descname"><span class="pre">is_world_process_zero</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.is_world_process_zero"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_world_process_zero" title="Permalink to this definition">#</a></dt> +<dd><p>Whether or not this process is the global main process (when training in a distributed fashion on several +machines, this is only going to be <cite>True</cite> for one process).</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.save_model"> +<span class="sig-name descname"><span class="pre">save_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal_call</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.save_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.save_model" title="Permalink to this definition">#</a></dt> +<dd><p>Will save the model, so you can reload it using <cite>from_pretrained()</cite>. +Will only save from the main process.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_tpu"> +<span class="sig-name descname"><span class="pre">_save_tpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._save_tpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_tpu" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._save"> +<span class="sig-name descname"><span class="pre">_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.store_flos"> +<span class="sig-name descname"><span class="pre">store_flos</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.store_flos"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.store_flos" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._sorted_checkpoints"> +<span class="sig-name descname"><span class="pre">_sorted_checkpoints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">PREFIX_CHECKPOINT_DIR</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_mtime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._sorted_checkpoints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._sorted_checkpoints" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._rotate_checkpoints"> +<span class="sig-name descname"><span class="pre">_rotate_checkpoints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">use_mtime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._rotate_checkpoints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._rotate_checkpoints" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluate"> +<span class="sig-name descname"><span class="pre">evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eval_dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.utils.data.Dataset</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_keys</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_key_prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'eval'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.evaluate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluate" title="Permalink to this definition">#</a></dt> +<dd><p>Run evaluation and returns metrics. +The calling script will be responsible for providing a method to compute metrics, as they are task-dependent +(pass it to the init <cite>compute_metrics</cite> argument). +You can also subclass and override this method to inject custom behavior. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>eval_dataset (<cite>Dataset</cite>, <em>optional</em>):</dt><dd><p>Pass a dataset if you wish to override <cite>self.eval_dataset</cite>. If it is a [<cite>~datasets.Dataset</cite>], columns +not accepted by the <cite>model.forward()</cite> method are automatically removed. It must implement the <cite>__len__</cite> +method.</p> +</dd> +<dt>ignore_keys (<cite>Lst[str]</cite>, <em>optional</em>):</dt><dd><p>A list of keys in the output of your model (if it is a dictionary) that should be ignored when +gathering predictions.</p> +</dd> +<dt>metric_key_prefix (<cite>str</cite>, <em>optional</em>, defaults to <cite>“eval”</cite>):</dt><dd><p>An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named +“eval_bleu” if the prefix is “eval” (default)</p> +</dd> +</dl> +</div></blockquote> +<dl class="simple"> +<dt>Returns:</dt><dd><p>A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The +dictionary also contains the epoch number which comes from the training state.</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.predict"> +<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">test_dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils.data.Dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_keys</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_key_prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'test'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">transformers.trainer_utils.PredictionOutput</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.predict" title="Permalink to this definition">#</a></dt> +<dd><p>Run prediction and returns predictions and potential metrics. +Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method +will also return metrics, like in <cite>evaluate()</cite>. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>test_dataset (<cite>Dataset</cite>):</dt><dd><p>Dataset to run the predictions on. If it is an <cite>datasets.Dataset</cite>, columns not accepted by the +<cite>model.forward()</cite> method are automatically removed. Has to implement the method <cite>__len__</cite></p> +</dd> +<dt>ignore_keys (<cite>Lst[str]</cite>, <em>optional</em>):</dt><dd><p>A list of keys in the output of your model (if it is a dictionary) that should be ignored when +gathering predictions.</p> +</dd> +<dt>metric_key_prefix (<cite>str</cite>, <em>optional</em>, defaults to <cite>“test”</cite>):</dt><dd><p>An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named +“test_bleu” if the prefix is “test” (default)</p> +</dd> +</dl> +</div></blockquote> +<p>&lt;Tip&gt; +If your predictions or labels have different sequence length (for instance because you’re doing dynamic padding +in a token classification task) the predictions will be padded (on the right) to allow for concatenation into +one array. The padding index is -100. +&lt;/Tip&gt; +Returns: <em>NamedTuple</em> A namedtuple with the following keys:</p> +<blockquote> +<div><ul class="simple"> +<li><p>predictions (<cite>np.ndarray</cite>): The predictions on <cite>test_dataset</cite>.</p></li> +<li><p>label_ids (<cite>np.ndarray</cite>, <em>optional</em>): The labels (if the dataset contained some).</p></li> +<li><p>metrics (<cite>Dict[str, float]</cite>, <em>optional</em>): The potential dictionary of metrics (if the dataset contained +labels).</p></li> +</ul> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluation_loop"> +<span class="sig-name descname"><span class="pre">evaluation_loop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataloader</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils.data.DataLoader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_loss_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_keys</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_key_prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'eval'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">transformers.trainer_utils.EvalLoopOutput</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.evaluation_loop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluation_loop" title="Permalink to this definition">#</a></dt> +<dd><p>Prediction/evaluation loop, shared by <cite>Trainer.evaluate()</cite> and <cite>Trainer.predict()</cite>. +Works both with or without labels.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._nested_gather"> +<span class="sig-name descname"><span class="pre">_nested_gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._nested_gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._nested_gather" title="Permalink to this definition">#</a></dt> +<dd><p>Gather value of <cite>tensors</cite> (tensor or list/tuple of nested tensors) and convert them to numpy before +concatenating them to <cite>gathered</cite></p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._pad_across_processes"> +<span class="sig-name descname"><span class="pre">_pad_across_processes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._pad_across_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._pad_across_processes" title="Permalink to this definition">#</a></dt> +<dd><p>Recursively pad the tensors in a nested list/tuple/dictionary of tensors from all devices to the same size so +they can safely be gathered.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_step"> +<span class="sig-name descname"><span class="pre">prediction_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.nn.Module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_loss_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_keys</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.prediction_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_step" title="Permalink to this definition">#</a></dt> +<dd><p>Perform an evaluation step on <cite>model</cite> using <cite>inputs</cite>. +Subclass and override to inject custom behavior. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>model (<cite>nn.Module</cite>):</dt><dd><p>The model to evaluate.</p> +</dd> +<dt>inputs (<cite>Dict[str, Union[torch.Tensor, Any]]</cite>):</dt><dd><p>The inputs and targets of the model. +The dictionary will be unpacked before being fed to the model. Most models expect the targets under the +argument <cite>labels</cite>. Check your model’s documentation for all accepted arguments.</p> +</dd> +<dt>prediction_loss_only (<cite>bool</cite>):</dt><dd><p>Whether or not to return the loss only.</p> +</dd> +<dt>ignore_keys (<cite>Lst[str]</cite>, <em>optional</em>):</dt><dd><p>A list of keys in the output of your model (if it is a dictionary) that should be ignored when +gathering predictions.</p> +</dd> +</dl> +</div></blockquote> +<dl class="simple"> +<dt>Return:</dt><dd><p>Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, +logits and labels (each being optional).</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.floating_point_ops"> +<span class="sig-name descname"><span class="pre">floating_point_ops</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.floating_point_ops"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.floating_point_ops" title="Permalink to this definition">#</a></dt> +<dd><p>For models that inherit from [<cite>PreTrainedModel</cite>], uses that method to compute the number of floating point +operations for every backward + forward pass. If using another model, either implement such a method in the +model or subclass and override this method. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>inputs (<cite>Dict[str, Union[torch.Tensor, Any]]</cite>):</dt><dd><p>The inputs and targets of the model.</p> +</dd> +</dl> +</div></blockquote> +<dl class="simple"> +<dt>Returns:</dt><dd><p><cite>int</cite>: The number of floating-point operations.</p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.init_git_repo"> +<span class="sig-name descname"><span class="pre">init_git_repo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">at_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.init_git_repo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.init_git_repo" title="Permalink to this definition">#</a></dt> +<dd><p>Initializes a git repo in <cite>self.args.hub_model_id</cite>. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>at_init (<cite>bool</cite>, <em>optional</em>, defaults to <cite>False</cite>):</dt><dd><p>Whether this function is called before any training or not. If <cite>self.args.overwrite_output_dir</cite> is +<cite>True</cite> and <cite>at_init</cite> is <cite>True</cite>, the path to the repo (which is <cite>self.args.output_dir</cite>) might be wiped +out.</p> +</dd> +</dl> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_model_card"> +<span class="sig-name descname"><span class="pre">create_model_card</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">language</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">license</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">finetuned_from</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tasks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_tags</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">None</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.create_model_card"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_model_card" title="Permalink to this definition">#</a></dt> +<dd><p>Creates a draft of a model card using the information available to the <cite>Trainer</cite>. +Args:</p> +<blockquote> +<div><dl class="simple"> +<dt>language (<cite>str</cite>, <em>optional</em>):</dt><dd><p>The language of the model (if applicable)</p> +</dd> +<dt>license (<cite>str</cite>, <em>optional</em>):</dt><dd><p>The license of the model. Will default to the license of the pretrained model used, if the original +model given to the <cite>Trainer</cite> comes from a repo on the Hub.</p> +</dd> +<dt>tags (<cite>str</cite> or <cite>List[str]</cite>, <em>optional</em>):</dt><dd><p>Some tags to be included in the metadata of the model card.</p> +</dd> +<dt>model_name (<cite>str</cite>, <em>optional</em>):</dt><dd><p>The name of the model.</p> +</dd> +<dt>finetuned_from (<cite>str</cite>, <em>optional</em>):</dt><dd><p>The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo +of the original model given to the <cite>Trainer</cite> (if it comes from the Hub).</p> +</dd> +<dt>tasks (<cite>str</cite> or <cite>List[str]</cite>, <em>optional</em>):</dt><dd><p>One or several task identifiers, to be included in the metadata of the model card.</p> +</dd> +<dt>dataset_tags (<cite>str</cite> or <cite>List[str]</cite>, <em>optional</em>):</dt><dd><p>One or several dataset tags, to be included in the metadata of the model card.</p> +</dd> +<dt>dataset (<cite>str</cite> or <cite>List[str]</cite>, <em>optional</em>):</dt><dd><p>One or several dataset identifiers, to be included in the metadata of the model card.</p> +</dd> +<dt>dataset_args (<cite>str</cite> or <cite>List[str]</cite>, <em>optional</em>):</dt><dd><p>One or several dataset arguments, to be included in the metadata of the model card.</p> +</dd> +</dl> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._push_from_checkpoint"> +<span class="sig-name descname"><span class="pre">_push_from_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_folder</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._push_from_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._push_from_checkpoint" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.push_to_hub"> +<span class="sig-name descname"><span class="pre">push_to_hub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">commit_message</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'End</span> <span class="pre">of</span> <span class="pre">training'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.push_to_hub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.push_to_hub" title="Permalink to this definition">#</a></dt> +<dd><p>Upload <em>self.model</em> and <em>self.tokenizer</em> to the 🤗 model hub on the repo <em>self.args.hub_model_id</em>. +Parameters:</p> +<blockquote> +<div><dl class="simple"> +<dt>commit_message (<cite>str</cite>, <em>optional</em>, defaults to <cite>“End of training”</cite>):</dt><dd><p>Message to commit while pushing.</p> +</dd> +<dt>blocking (<cite>bool</cite>, <em>optional</em>, defaults to <cite>True</cite>):</dt><dd><p>Whether the function should return only when the <cite>git push</cite> has finished.</p> +</dd> +<dt>kwargs:</dt><dd><p>Additional keyword arguments passed along to [<cite>~Trainer.create_model_card</cite>].</p> +</dd> +</dl> +</div></blockquote> +<dl class="simple"> +<dt>Returns:</dt><dd><p>The url of the commit of your model in the given repository if <cite>blocking=False</cite>, a tuple with the url of +the commit and an object to track the progress of the commit if <cite>blocking=True</cite></p> +</dd> +</dl> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_loop"> +<span class="sig-name descname"><span class="pre">prediction_loop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataloader</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils.data.DataLoader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prediction_loss_only</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_keys</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_key_prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'eval'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">transformers.trainer_utils.EvalLoopOutput</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer.prediction_loop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_loop" title="Permalink to this definition">#</a></dt> +<dd><p>Prediction/evaluation loop, shared by <cite>Trainer.evaluate()</cite> and <cite>Trainer.predict()</cite>. +Works both with or without labels.</p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._gather_and_numpify"> +<span class="sig-name descname"><span class="pre">_gather_and_numpify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._gather_and_numpify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._gather_and_numpify" title="Permalink to this definition">#</a></dt> +<dd><p>Gather value of <cite>tensors</cite> (tensor or list/tuple of nested tensors) and convert them to numpy before +concatenating them to <cite>gathered</cite></p> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.pipeline.utils.raft_trainer.RaftTrainer._add_sm_patterns_to_gitignore"> +<span class="sig-name descname"><span class="pre">_add_sm_patterns_to_gitignore</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/pipeline/utils/raft_trainer.html#RaftTrainer._add_sm_patterns_to_gitignore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._add_sm_patterns_to_gitignore" title="Permalink to this definition">#</a></dt> +<dd><p>Add SageMaker Checkpointing patterns to .gitignore file.</p> +</dd></dl> + +</dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../peft_trainer/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.utils.peft_trainer</span></code></p> + </div> + </a> + <a class="right-next" + href="../../auto_pipeline/index.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.pipeline.auto_pipeline</span></code></p> + </div> + <i class="fa-solid fa-angle-right"></i> + </a> +</div> + </footer> + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner"> + + <div class="sidebar-secondary-item"> + <div class="page-toc tocsection onthispage"> + <i class="fa-solid fa-list"></i> On this page + </div> + <nav class="bd-toc-nav page-toc"> + <ul class="visible nav section-nav flex-column"> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-contents">Module Contents</a><ul class="nav section-nav flex-column"> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#classes">Classes</a></li> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#attributes">Attributes</a><ul class="nav section-nav flex-column"> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.is_torch_greater_or_equal_than_1_10"><code class="docutils literal notranslate"><span class="pre">is_torch_greater_or_equal_than_1_10</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.is_torch_less_than_1_11"><code class="docutils literal notranslate"><span class="pre">is_torch_less_than_1_11</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer._is_native_cpu_amp_available"><code class="docutils literal notranslate"><span class="pre">_is_native_cpu_amp_available</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.DEFAULT_CALLBACKS"><code class="docutils literal notranslate"><span class="pre">DEFAULT_CALLBACKS</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK"><code class="docutils literal notranslate"><span class="pre">DEFAULT_PROGRESS_CALLBACK</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#id0"><code class="docutils literal notranslate"><span class="pre">DEFAULT_PROGRESS_CALLBACK</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.IS_SAGEMAKER_MP_POST_1_10"><code class="docutils literal notranslate"><span class="pre">IS_SAGEMAKER_MP_POST_1_10</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.skip_first_batches"><code class="docutils literal notranslate"><span class="pre">skip_first_batches</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.logger"><code class="docutils literal notranslate"><span class="pre">logger</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.TRAINING_ARGS_NAME"><code class="docutils literal notranslate"><span class="pre">TRAINING_ARGS_NAME</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.TRAINER_STATE_NAME"><code class="docutils literal notranslate"><span class="pre">TRAINER_STATE_NAME</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.OPTIMIZER_NAME"><code class="docutils literal notranslate"><span class="pre">OPTIMIZER_NAME</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.SCHEDULER_NAME"><code class="docutils literal notranslate"><span class="pre">SCHEDULER_NAME</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.SCALER_NAME"><code class="docutils literal notranslate"><span class="pre">SCALER_NAME</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer"><code class="docutils literal notranslate"><span class="pre">RaftTrainer</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.add_callback"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.add_callback()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.pop_callback"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.pop_callback()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.remove_callback"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.remove_callback()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._move_model_to_device"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._move_model_to_device()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._set_signature_columns_if_needed"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._set_signature_columns_if_needed()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._remove_unused_columns"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._remove_unused_columns()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_collator_with_removed_columns"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._get_collator_with_removed_columns()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_train_sampler"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._get_train_sampler()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_train_dataloader"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.get_train_dataloader()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_eval_sampler"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._get_eval_sampler()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_eval_dataloader"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.get_eval_dataloader()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_test_dataloader"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.get_test_dataloader()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer_and_scheduler"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.create_optimizer_and_scheduler()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.create_optimizer()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_optimizer_cls_and_kwargs"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.get_optimizer_cls_and_kwargs()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_scheduler"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.create_scheduler()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.num_examples"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.num_examples()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._hp_search_setup"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._hp_search_setup()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._report_to_hp_search"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._report_to_hp_search()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._tune_save_checkpoint"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._tune_save_checkpoint()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.call_model_init"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.call_model_init()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.torch_jit_model_eval"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.torch_jit_model_eval()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.ipex_optimize_model"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.ipex_optimize_model()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._wrap_model"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._wrap_model()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.train"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.train()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._one_train"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._one_train()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._inner_training_loop"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._inner_training_loop()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_output_dir"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._get_output_dir()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_from_checkpoint"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._load_from_checkpoint()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_best_model"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._load_best_model()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._issue_warnings_after_load"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._issue_warnings_after_load()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._maybe_log_save_evaluate"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._maybe_log_save_evaluate()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_rng_state"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._load_rng_state()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_checkpoint"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._save_checkpoint()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_optimizer_and_scheduler"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._load_optimizer_and_scheduler()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.hyperparameter_search"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.hyperparameter_search()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.log"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.log()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_input"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._prepare_input()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_inputs"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._prepare_inputs()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss_context_manager"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.compute_loss_context_manager()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.autocast_smart_context_manager"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.autocast_smart_context_manager()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.training_step"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.training_step()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.compute_loss()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_local_process_zero"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.is_local_process_zero()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_world_process_zero"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.is_world_process_zero()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.save_model"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.save_model()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_tpu"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._save_tpu()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._save()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.store_flos"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.store_flos()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._sorted_checkpoints"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._sorted_checkpoints()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._rotate_checkpoints"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._rotate_checkpoints()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluate"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.evaluate()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.predict"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.predict()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluation_loop"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.evaluation_loop()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._nested_gather"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._nested_gather()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._pad_across_processes"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._pad_across_processes()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_step"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.prediction_step()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.floating_point_ops"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.floating_point_ops()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.init_git_repo"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.init_git_repo()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_model_card"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.create_model_card()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._push_from_checkpoint"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._push_from_checkpoint()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.push_to_hub"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.push_to_hub()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_loop"><code class="docutils literal notranslate"><span class="pre">RaftTrainer.prediction_loop()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._gather_and_numpify"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._gather_and_numpify()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._add_sm_patterns_to_gitignore"><code class="docutils literal notranslate"><span class="pre">RaftTrainer._add_sm_patterns_to_gitignore()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.is_torch_greater_or_equal_than_1_10">is_torch_greater_or_equal_than_1_10</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.is_torch_less_than_1_11">is_torch_less_than_1_11</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer._is_native_cpu_amp_available">_is_native_cpu_amp_available</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.DEFAULT_CALLBACKS">DEFAULT_CALLBACKS</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK">DEFAULT_PROGRESS_CALLBACK</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#id0">DEFAULT_PROGRESS_CALLBACK</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.IS_SAGEMAKER_MP_POST_1_10">IS_SAGEMAKER_MP_POST_1_10</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.skip_first_batches">skip_first_batches</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.logger">logger</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.TRAINING_ARGS_NAME">TRAINING_ARGS_NAME</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.TRAINER_STATE_NAME">TRAINER_STATE_NAME</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.OPTIMIZER_NAME">OPTIMIZER_NAME</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.SCHEDULER_NAME">SCHEDULER_NAME</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.SCALER_NAME">SCALER_NAME</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer">RaftTrainer</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.add_callback">add_callback</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.pop_callback">pop_callback</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.remove_callback">remove_callback</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._move_model_to_device">_move_model_to_device</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._set_signature_columns_if_needed">_set_signature_columns_if_needed</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._remove_unused_columns">_remove_unused_columns</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_collator_with_removed_columns">_get_collator_with_removed_columns</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_train_sampler">_get_train_sampler</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_train_dataloader">get_train_dataloader</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_eval_sampler">_get_eval_sampler</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_eval_dataloader">get_eval_dataloader</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_test_dataloader">get_test_dataloader</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer_and_scheduler">create_optimizer_and_scheduler</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer">create_optimizer</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_optimizer_cls_and_kwargs">get_optimizer_cls_and_kwargs</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_scheduler">create_scheduler</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.num_examples">num_examples</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._hp_search_setup">_hp_search_setup</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._report_to_hp_search">_report_to_hp_search</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._tune_save_checkpoint">_tune_save_checkpoint</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.call_model_init">call_model_init</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.torch_jit_model_eval">torch_jit_model_eval</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.ipex_optimize_model">ipex_optimize_model</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._wrap_model">_wrap_model</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.train">train</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._one_train">_one_train</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._inner_training_loop">_inner_training_loop</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_output_dir">_get_output_dir</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_from_checkpoint">_load_from_checkpoint</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_best_model">_load_best_model</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._issue_warnings_after_load">_issue_warnings_after_load</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._maybe_log_save_evaluate">_maybe_log_save_evaluate</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_rng_state">_load_rng_state</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_checkpoint">_save_checkpoint</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_optimizer_and_scheduler">_load_optimizer_and_scheduler</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.hyperparameter_search">hyperparameter_search</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.log">log</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_input">_prepare_input</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_inputs">_prepare_inputs</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss_context_manager">compute_loss_context_manager</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.autocast_smart_context_manager">autocast_smart_context_manager</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.training_step">training_step</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss">compute_loss</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_local_process_zero">is_local_process_zero</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_world_process_zero">is_world_process_zero</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.save_model">save_model</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_tpu">_save_tpu</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save">_save</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.store_flos">store_flos</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._sorted_checkpoints">_sorted_checkpoints</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._rotate_checkpoints">_rotate_checkpoints</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluate">evaluate</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.predict">predict</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluation_loop">evaluation_loop</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._nested_gather">_nested_gather</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._pad_across_processes">_pad_across_processes</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_step">prediction_step</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.floating_point_ops">floating_point_ops</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.init_git_repo">init_git_repo</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_model_card">create_model_card</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._push_from_checkpoint">_push_from_checkpoint</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.push_to_hub">push_to_hub</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_loop">prediction_loop</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._gather_and_numpify">_gather_and_numpify</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.pipeline.utils.raft_trainer.RaftTrainer._add_sm_patterns_to_gitignore">_add_sm_patterns_to_gitignore</a></li> +</ul> +</li> +</ul> +</li> +</ul> +</li> +</ul> + </nav></div> + + <div class="sidebar-secondary-item"> + <div class="tocsection sourcelink"> + <a href="../../../../../_sources/autoapi/lmflow/pipeline/utils/raft_trainer/index.rst.txt"> + <i class="fa-solid fa-file-lines"></i> Show Source + </a> + </div> +</div> + +</div></div> + + + </div> + <footer 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heading">#</a></h2> +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.utils.constants.TEXT_ONLY_DATASET_DESCRIPTION"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.constants.</span></span><span class="sig-name descname"><span class="pre">TEXT_ONLY_DATASET_DESCRIPTION</span></span><a class="reference internal" href="../../../../_modules/lmflow/utils/constants.html#TEXT_ONLY_DATASET_DESCRIPTION"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.constants.TEXT_ONLY_DATASET_DESCRIPTION" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.utils.constants.TEXT_ONLY_DATASET_DETAILS"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.constants.</span></span><span class="sig-name descname"><span class="pre">TEXT_ONLY_DATASET_DETAILS</span></span><a class="reference internal" 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id="lmflow.utils.constants.TEXT2TEXT_DATASET_DETAILS"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.constants.</span></span><span class="sig-name descname"><span class="pre">TEXT2TEXT_DATASET_DETAILS</span></span><a class="reference internal" href="../../../../_modules/lmflow/utils/constants.html#TEXT2TEXT_DATASET_DETAILS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.constants.TEXT2TEXT_DATASET_DETAILS" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.utils.constants.FLOAT_ONLY_DATASET_DESCRIPTION"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.constants.</span></span><span class="sig-name descname"><span class="pre">FLOAT_ONLY_DATASET_DESCRIPTION</span></span><a class="reference internal" href="../../../../_modules/lmflow/utils/constants.html#FLOAT_ONLY_DATASET_DESCRIPTION"><span 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+ + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../index.html" class="nav-link">API Reference</a></li> + + + <li class="breadcrumb-item"><i class="fa-solid fa-ellipsis"></i></li> + + + <li class="breadcrumb-item"><a href="../index.html" class="nav-link"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils</span></code></a></li> + + <li class="breadcrumb-item active" aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.data_utils</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.utils.data_utils"> +<span id="lmflow-utils-data-utils"></span><h1><a class="reference internal" href="#module-lmflow.utils.data_utils" title="lmflow.utils.data_utils"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.data_utils</span></code></a><a class="headerlink" href="#module-lmflow.utils.data_utils" title="Permalink to this heading">#</a></h1> +<p>The program includes several functions: setting a random seed, +loading data from a JSON file, batching data, and extracting answers from generated text.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="functions"> +<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.data_utils.set_random_seed" title="lmflow.utils.data_utils.set_random_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_random_seed</span></code></a>(seed)</p></td> +<td><p>Set the random seed for <cite>random</cite>, <cite>numpy</cite>, <cite>torch</cite>, <cite>torch.cuda</cite>.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.data_utils.load_data" title="lmflow.utils.data_utils.load_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_data</span></code></a>(file_name)</p></td> +<td><p>Load data with file name.</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.data_utils.batchlize" title="lmflow.utils.data_utils.batchlize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">batchlize</span></code></a>(examples, batch_size, random_shuffle)</p></td> +<td><p>Convert examples to a dataloader.</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.data_utils.answer_extraction" title="lmflow.utils.data_utils.answer_extraction"><code class="xref py py-obj docutils literal notranslate"><span class="pre">answer_extraction</span></code></a>(response[, answer_type])</p></td> +<td><p>Use this funtion to extract answers from generated text</p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.data_utils.process_image_flag" title="lmflow.utils.data_utils.process_image_flag"><code class="xref py py-obj docutils literal notranslate"><span class="pre">process_image_flag</span></code></a>(text[, image_flag])</p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.data_utils.set_random_seed"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.data_utils.</span></span><span class="sig-name descname"><span class="pre">set_random_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/utils/data_utils.html#set_random_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.data_utils.set_random_seed" title="Permalink to this definition">#</a></dt> +<dd><p>Set the random seed for <cite>random</cite>, <cite>numpy</cite>, <cite>torch</cite>, <cite>torch.cuda</cite>.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>seed</strong><span class="classifier">int</span></dt><dd><p>The default seed.</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.data_utils.load_data"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.data_utils.</span></span><span class="sig-name descname"><span class="pre">load_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/utils/data_utils.html#load_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.data_utils.load_data" title="Permalink to this definition">#</a></dt> +<dd><p>Load data with file name.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>file_name</strong><span class="classifier">str.</span></dt><dd><p>The dataset file name.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt><strong>inputs</strong><span class="classifier">list.</span></dt><dd><p>The input texts of the dataset.</p> +</dd> +<dt><strong>outputs</strong><span class="classifier">list.</span></dt><dd><p>The output texts file datasets.</p> +</dd> +<dt><strong>len</strong><span class="classifier">int.</span></dt><dd><p>The length of the dataset.</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.data_utils.batchlize"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.data_utils.</span></span><span class="sig-name descname"><span class="pre">batchlize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">examples</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_shuffle</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/utils/data_utils.html#batchlize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.data_utils.batchlize" title="Permalink to this definition">#</a></dt> +<dd><p>Convert examples to a dataloader.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>examples</strong><span class="classifier">list.</span></dt><dd><p>Data list.</p> +</dd> +<dt><strong>batch_size</strong><span class="classifier">int.</span></dt><dd></dd> +<dt><strong>random_shuffle</strong><span class="classifier">bool</span></dt><dd><p>If true, the dataloader shuffle the training data.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>dataloader:</dt><dd><p>Dataloader with batch generator.</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.data_utils.answer_extraction"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.data_utils.</span></span><span class="sig-name descname"><span class="pre">answer_extraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">response</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">answer_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/utils/data_utils.html#answer_extraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.data_utils.answer_extraction" title="Permalink to this definition">#</a></dt> +<dd><p>Use this funtion to extract answers from generated text</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt><strong>args :</strong></dt><dd><p>Arguments.</p> +</dd> +<dt><strong>response</strong><span class="classifier">str</span></dt><dd><p>plain string response.</p> +</dd> +</dl> +</dd> +<dt class="field-even">Returns<span class="colon">:</span></dt> +<dd class="field-even"><dl class="simple"> +<dt>answer:</dt><dd><p>Decoded answer (such as A, B, C, D, E for mutiple-choice QA).</p> +</dd> +</dl> +</dd> +</dl> +</dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.data_utils.process_image_flag"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.data_utils.</span></span><span class="sig-name descname"><span class="pre">process_image_flag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">text</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">image_flag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'&lt;ImageHere&gt;'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../_modules/lmflow/utils/data_utils.html#process_image_flag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" 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to this heading">#</a></h1> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="functions"> +<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.bloom_flash_attention.forward" title="lmflow.utils.flash_attention.bloom_flash_attention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(self, hidden_states, residual, alibi, ...[, ...])</p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.bloom_flash_attention._prepare_attn_mask" title="lmflow.utils.flash_attention.bloom_flash_attention._prepare_attn_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_prepare_attn_mask</span></code></a>(→ torch.BoolTensor)</p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.bloom_flash_attention.replace_bloom_attn_with_flash_attn" title="lmflow.utils.flash_attention.bloom_flash_attention.replace_bloom_attn_with_flash_attn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">replace_bloom_attn_with_flash_attn</span></code></a>()</p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.bloom_flash_attention.forward"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.bloom_flash_attention.</span></span><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">residual</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alibi</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_past</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_attentions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/bloom_flash_attention.html#forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.bloom_flash_attention.forward" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.bloom_flash_attention._prepare_attn_mask"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.bloom_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_prepare_attn_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_shape</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">past_key_values_length</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">torch.BoolTensor</span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/bloom_flash_attention.html#_prepare_attn_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.bloom_flash_attention._prepare_attn_mask" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.bloom_flash_attention.replace_bloom_attn_with_flash_attn"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.bloom_flash_attention.</span></span><span class="sig-name descname"><span class="pre">replace_bloom_attn_with_flash_attn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/bloom_flash_attention.html#replace_bloom_attn_with_flash_attn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" 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aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.gpt2_flash_attention</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.utils.flash_attention.gpt2_flash_attention"> +<span id="lmflow-utils-flash-attention-gpt2-flash-attention"></span><h1><a class="reference internal" href="#module-lmflow.utils.flash_attention.gpt2_flash_attention" title="lmflow.utils.flash_attention.gpt2_flash_attention"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.gpt2_flash_attention</span></code></a><a class="headerlink" href="#module-lmflow.utils.flash_attention.gpt2_flash_attention" title="Permalink to this heading">#</a></h1> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="functions"> +<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.gpt2_flash_attention.forward" title="lmflow.utils.flash_attention.gpt2_flash_attention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(→ Tuple[Union[torch.Tensor, ...)</p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.gpt2_flash_attention._prepare_decoder_attention_mask" title="lmflow.utils.flash_attention.gpt2_flash_attention._prepare_decoder_attention_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_prepare_decoder_attention_mask</span></code></a>(self, attention_mask, ...)</p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.gpt2_flash_attention.replace_gpt2_attn_with_flash_attn" title="lmflow.utils.flash_attention.gpt2_flash_attention.replace_gpt2_attn_with_flash_attn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">replace_gpt2_attn_with_flash_attn</span></code></a>()</p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.gpt2_flash_attention.forward"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.gpt2_flash_attention.</span></span><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.FloatTensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_past</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.FloatTensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.FloatTensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoder_hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoder_attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.FloatTensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_attentions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Ellipsis</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/gpt2_flash_attention.html#forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.gpt2_flash_attention.forward" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.gpt2_flash_attention._prepare_decoder_attention_mask"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.gpt2_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_prepare_decoder_attention_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs_embeds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">past_key_values_length</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/gpt2_flash_attention.html#_prepare_decoder_attention_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.gpt2_flash_attention._prepare_decoder_attention_mask" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.gpt2_flash_attention.replace_gpt2_attn_with_flash_attn"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.gpt2_flash_attention.</span></span><span class="sig-name descname"><span class="pre">replace_gpt2_attn_with_flash_attn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/gpt2_flash_attention.html#replace_gpt2_attn_with_flash_attn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.gpt2_flash_attention.replace_gpt2_attn_with_flash_attn" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../bloom_flash_attention/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span 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title="lmflow.utils.flash_attention.gpt_neo_flash_attention"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.gpt_neo_flash_attention</span></code></a><a class="headerlink" href="#module-lmflow.utils.flash_attention.gpt_neo_flash_attention" title="Permalink to this heading">#</a></h1> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="functions"> +<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.gpt_neo_flash_attention._attn" title="lmflow.utils.flash_attention.gpt_neo_flash_attention._attn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_attn</span></code></a>(self, query, key, value[, attention_mask, head_mask])</p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.gpt_neo_flash_attention.forward" title="lmflow.utils.flash_attention.gpt_neo_flash_attention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(self, hidden_states[, attention_mask, ...])</p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.gpt_neo_flash_attention.replace_gpt_neo_attn_with_flash_attn" title="lmflow.utils.flash_attention.gpt_neo_flash_attention.replace_gpt_neo_attn_with_flash_attn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">replace_gpt_neo_attn_with_flash_attn</span></code></a>()</p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.gpt_neo_flash_attention._attn"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.gpt_neo_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_attn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/gpt_neo_flash_attention.html#_attn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.gpt_neo_flash_attention._attn" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.gpt_neo_flash_attention.forward"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.gpt_neo_flash_attention.</span></span><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_past</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_attentions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/gpt_neo_flash_attention.html#forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.gpt_neo_flash_attention.forward" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.gpt_neo_flash_attention.replace_gpt_neo_attn_with_flash_attn"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.gpt_neo_flash_attention.</span></span><span class="sig-name descname"><span class="pre">replace_gpt_neo_attn_with_flash_attn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/gpt_neo_flash_attention.html#replace_gpt_neo_attn_with_flash_attn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.gpt_neo_flash_attention.replace_gpt_neo_attn_with_flash_attn" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../gpt2_flash_attention/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.gpt2_flash_attention</span></code></p> + </div> + </a> + <a class="right-next" + href="../llama_flash_attention/index.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title"><code class="xref py 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+</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.utils.flash_attention.llama_flash_attention"> +<span id="lmflow-utils-flash-attention-llama-flash-attention"></span><h1><a class="reference internal" href="#module-lmflow.utils.flash_attention.llama_flash_attention" title="lmflow.utils.flash_attention.llama_flash_attention"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.llama_flash_attention</span></code></a><a class="headerlink" href="#module-lmflow.utils.flash_attention.llama_flash_attention" title="Permalink to this heading">#</a></h1> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="functions"> +<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.llama_flash_attention.forward" title="lmflow.utils.flash_attention.llama_flash_attention.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(→ Tuple[torch.Tensor, Optional[torch.Tensor], ...)</p></td> +<td><p>Input shape: Batch x Time x Channel</p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.llama_flash_attention._prepare_decoder_attention_mask" title="lmflow.utils.flash_attention.llama_flash_attention._prepare_decoder_attention_mask"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_prepare_decoder_attention_mask</span></code></a>(self, attention_mask, ...)</p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.llama_flash_attention.replace_llama_attn_with_flash_attn" title="lmflow.utils.flash_attention.llama_flash_attention.replace_llama_attn_with_flash_attn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">replace_llama_attn_with_flash_attn</span></code></a>()</p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.llama_flash_attention.forward"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.llama_flash_attention.</span></span><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_states</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">position_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">past_key_value</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_attentions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/llama_flash_attention.html#forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.llama_flash_attention.forward" title="Permalink to this definition">#</a></dt> +<dd><p>Input shape: Batch x Time x Channel</p> +<p>attention_mask: [bsz, q_len]</p> +</dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.llama_flash_attention._prepare_decoder_attention_mask"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.llama_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_prepare_decoder_attention_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attention_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs_embeds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">past_key_values_length</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/llama_flash_attention.html#_prepare_decoder_attention_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.llama_flash_attention._prepare_decoder_attention_mask" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.llama_flash_attention.replace_llama_attn_with_flash_attn"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.llama_flash_attention.</span></span><span class="sig-name descname"><span class="pre">replace_llama_attn_with_flash_attn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/llama_flash_attention.html#replace_llama_attn_with_flash_attn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.llama_flash_attention.replace_llama_attn_with_flash_attn" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../gpt_neo_flash_attention/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.gpt_neo_flash_attention</span></code></p> + </div> + </a> + <a class="right-next" + href="../triton_flash_attention/index.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span 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</div> + + + <div class="sidebar-primary-items__end sidebar-primary__section"> + </div> + + <div id="rtd-footer-container"></div> + + + </div> + + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"> +<div class="header-article-items header-article__inner"> + + <div class="header-article-items__start"> + + <div class="header-article-item"> + + + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../../../../../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="../../../../index.html" class="nav-link">API Reference</a></li> + + + <li class="breadcrumb-item"><i class="fa-solid fa-ellipsis"></i></li> + + + <li class="breadcrumb-item"><a href="../index.html" class="nav-link"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention</span></code></a></li> + + <li class="breadcrumb-item active" aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.utils.flash_attention.triton_flash_attention"> +<span id="lmflow-utils-flash-attention-triton-flash-attention"></span><h1><a class="reference internal" href="#module-lmflow.utils.flash_attention.triton_flash_attention" title="lmflow.utils.flash_attention.triton_flash_attention"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention</span></code></a><a class="headerlink" href="#module-lmflow.utils.flash_attention.triton_flash_attention" title="Permalink to this heading">#</a></h1> +<p><em>Experimental</em> implementation of FlashAttention in Triton. +Tested with triton==2.0.0.dev20221202. +Triton 2.0 has a new backend (MLIR) but seems like it doesn’t yet work for head dimensions +other than 64: +<a class="github reference external" href="https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207">openai/triton</a> +We’ll update this implementation with the new Triton backend once this is fixed.</p> +<p>We use the FlashAttention implementation from Phil Tillet a starting point. +<a class="github reference external" href="https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py">openai/triton</a></p> +<p>Changes: +- Implement both causal and non-causal attention. +- Implement both self-attention and cross-attention. +- Support arbitrary seqlens (not just multiples of 128), for both forward and backward. +- Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward. +- Support attention bias. +- Speed up the forward pass a bit, and only store the LSE instead of m and l. +- Make the backward for d=128 much faster by reducing register spilling. +- Optionally parallelize the backward pass across seqlen_k, to deal with the case of +small batch size * nheads.</p> +<p>Caution: +- This is an <em>experimental</em> implementation. The forward pass should be quite robust but +I’m not 100% sure that the backward pass doesn’t have race conditions (due to the Triton compiler). +- This implementation has only been tested on A100. +- If you plan to use headdim other than 64 and 128, you should test for race conditions +(due to the Triton compiler), as done in tests/test_flash_attn.py +“test_flash_attn_triton_race_condition”. I’ve tested and fixed many race conditions +for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I’m still not 100% confident +that there are none left for other head dimensions.</p> +<p>Differences between this Triton version and the CUDA version: +- Triton version doesn’t support dropout. +- Triton forward is generally faster than CUDA forward, while Triton backward is +generally slower than CUDA backward. Overall Triton forward + backward is slightly slower +than CUDA forward + backward. +- Triton version doesn’t support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor). +- Triton version supports attention bias, while CUDA version doesn’t.</p> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FlashAttnQKVPackedFunc</span></code></a></p></td> +<td><p>Base class to create custom <cite>autograd.Function</cite></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FlashAttnKVPackedFunc</span></code></a></p></td> +<td><p>Base class to create custom <cite>autograd.Function</cite></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FlashAttnFunc</span></code></a></p></td> +<td><p>Base class to create custom <cite>autograd.Function</cite></p></td> +</tr> +</tbody> +</table> +</section> +<section id="functions"> +<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention._fwd_kernel" title="lmflow.utils.flash_attention.triton_flash_attention._fwd_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_fwd_kernel</span></code></a>(Q, K, V, Bias, Out, Lse, TMP, ...)</p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_preprocess_do_o_dot" title="lmflow.utils.flash_attention.triton_flash_attention._bwd_preprocess_do_o_dot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_bwd_preprocess_do_o_dot</span></code></a>(Out, DO, Delta, stride_ob, ...)</p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_store_dk_dv" title="lmflow.utils.flash_attention.triton_flash_attention._bwd_store_dk_dv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_bwd_store_dk_dv</span></code></a>(dk_ptrs, dv_ptrs, dk, dv, offs_n, ...)</p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel_one_col_block" title="lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel_one_col_block"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_bwd_kernel_one_col_block</span></code></a>(start_n, Q, K, V, Bias, DO, ...)</p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.init_to_zero" title="lmflow.utils.flash_attention.triton_flash_attention.init_to_zero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">init_to_zero</span></code></a>(name)</p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel" title="lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_bwd_kernel</span></code></a>(Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, ...)</p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_forward" title="lmflow.utils.flash_attention.triton_flash_attention._flash_attn_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_flash_attn_forward</span></code></a>(q, k, v[, bias, causal, softmax_scale])</p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_backward" title="lmflow.utils.flash_attention.triton_flash_attention._flash_attn_backward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_flash_attn_backward</span></code></a>(do, q, k, v, o, lse, dq, dk, dv)</p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +</section> +<section id="attributes"> +<h3>Attributes<a class="headerlink" href="#attributes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_qkvpacked_func" title="lmflow.utils.flash_attention.triton_flash_attention.flash_attn_qkvpacked_func"><code class="xref py py-obj docutils literal notranslate"><span class="pre">flash_attn_qkvpacked_func</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-even"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_kvpacked_func" title="lmflow.utils.flash_attention.triton_flash_attention.flash_attn_kvpacked_func"><code class="xref py py-obj docutils literal notranslate"><span class="pre">flash_attn_kvpacked_func</span></code></a></p></td> +<td><p></p></td> +</tr> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_func" title="lmflow.utils.flash_attention.triton_flash_attention.flash_attn_func"><code class="xref py py-obj docutils literal notranslate"><span class="pre">flash_attn_func</span></code></a></p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention._fwd_kernel"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_fwd_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">K</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">V</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Lse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">TMP</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">softmax_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_qb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_qh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_qm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_kb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_kh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_kn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_vb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_vh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_vn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_bb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_bh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_bm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_ob</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_oh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_om</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nheads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_q_rounded</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">headdim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">CACHE_KEY_SEQLEN_Q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">CACHE_KEY_SEQLEN_K</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BIAS_TYPE</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">IS_CAUSAL</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_HEADDIM</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_HEADDIM</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#_fwd_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention._fwd_kernel" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention._bwd_preprocess_do_o_dot"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_bwd_preprocess_do_o_dot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Out</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DO</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Delta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_ob</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_oh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_om</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dob</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_doh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dom</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nheads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_q_rounded</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">headdim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_HEADDIM</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#_bwd_preprocess_do_o_dot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_preprocess_do_o_dot" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention._bwd_store_dk_dv"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_bwd_store_dk_dv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dk_ptrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dv_ptrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dk</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offs_n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offs_d</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">headdim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_HEADDIM</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#_bwd_store_dk_dv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_store_dk_dv" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel_one_col_block"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_bwd_kernel_one_col_block</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">K</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">V</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DO</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DQ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DV</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">LSE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">D</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">softmax_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_qm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_kn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_vn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_bm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dom</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dqm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dkn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dvn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">headdim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ATOMIC_ADD</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BIAS_TYPE</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">IS_CAUSAL</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_HEADDIM</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_HEADDIM</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#_bwd_kernel_one_col_block"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel_one_col_block" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.init_to_zero"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">init_to_zero</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#init_to_zero"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.init_to_zero" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_bwd_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">Q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">K</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">V</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DO</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DQ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">DV</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">LSE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">D</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">softmax_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_qb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_qh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_qm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_kb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_kh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_kn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_vb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_vh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_vn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_bb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_bh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_bm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dob</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_doh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dom</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dqb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dqh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dqm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dkb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dkh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dkn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dvb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dvh</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride_dvn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nheads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seqlen_q_rounded</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">headdim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">CACHE_KEY_SEQLEN_Q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">CACHE_KEY_SEQLEN_K</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BIAS_TYPE</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">IS_CAUSAL</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_HEADDIM</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">SEQUENCE_PARALLEL</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">EVEN_HEADDIM</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_M</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_N</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">triton.language.constexpr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#_bwd_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention._flash_attn_forward"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_flash_attn_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">softmax_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#_flash_attn_forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_forward" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention._flash_attn_backward"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">_flash_attn_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">do</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">o</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dk</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">softmax_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#_flash_attn_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_backward" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">FlashAttnQKVPackedFunc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#FlashAttnQKVPackedFunc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></p> +<p>Base class to create custom <cite>autograd.Function</cite></p> +<p>To create a custom <cite>autograd.Function</cite>, subclass this class and implement +the <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> and <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">backward()</span></code></a> static methods. Then, to use your custom +op in the forward pass, call the class method <code class="docutils literal notranslate"><span class="pre">apply</span></code>. Do not call +<a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> directly.</p> +<p>To ensure correctness and best performance, make sure you are calling the +correct methods on <code class="docutils literal notranslate"><span class="pre">ctx</span></code> and validating your backward function using +<code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.gradcheck()</span></code>.</p> +<p>See <span class="xref std std-ref">extending-autograd</span> for more details on how to use this class.</p> +<p>Examples:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)</span> +<span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">Exp</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="nd">@staticmethod</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="n">result</span> <span class="o">=</span> <span class="n">i</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">return</span> <span class="n">result</span> +<span class="gp">&gt;&gt;&gt;</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="nd">@staticmethod</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="n">result</span><span class="p">,</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">return</span> <span class="n">grad_output</span> <span class="o">*</span> <span class="n">result</span> +<span class="gp">&gt;&gt;&gt;</span> +<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Use it by calling the apply method:</span> +<span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP</span> +<span class="gp">&gt;&gt;&gt; </span><span class="n">output</span> <span class="o">=</span> <span class="n">Exp</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> +</pre></div> +</div> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward"> +<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qkv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">softmax_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#FlashAttnQKVPackedFunc.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward" title="Permalink to this definition">#</a></dt> +<dd><p>qkv: (batch, seqlen, 3, nheads, headdim) +bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen).</p> +<blockquote> +<div><p>For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen). +ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen)</p> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward"> +<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#FlashAttnQKVPackedFunc.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward" title="Permalink to this definition">#</a></dt> +<dd><p>Defines a formula for differentiating the operation with backward mode +automatic differentiation (alias to the vjp function).</p> +<p>This function is to be overridden by all subclasses.</p> +<p>It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by +as many outputs as the <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> returned (None will be passed in +for non tensor outputs of the forward function), +and it should return as many tensors, as there were inputs to +<a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. Each argument is the gradient w.r.t the given output, +and each returned value should be the gradient w.r.t. the +corresponding input. If an input is not a Tensor or is a Tensor not +requiring grads, you can just pass None as a gradient for that input.</p> +<p>The context can be used to retrieve tensors saved during the forward +pass. It also has an attribute <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx.needs_input_grad</span></code> as a tuple +of booleans representing whether each input needs gradient. E.g., +<a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code></a> will have <code class="docutils literal notranslate"><span class="pre">ctx.needs_input_grad[0]</span> <span class="pre">=</span> <span class="pre">True</span></code> if the +first input to <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> needs gradient computated w.r.t. the +output.</p> +</dd></dl> + +</dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.flash_attn_qkvpacked_func"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">flash_attn_qkvpacked_func</span></span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#flash_attn_qkvpacked_func"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_qkvpacked_func" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">FlashAttnKVPackedFunc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#FlashAttnKVPackedFunc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></p> +<p>Base class to create custom <cite>autograd.Function</cite></p> +<p>To create a custom <cite>autograd.Function</cite>, subclass this class and implement +the <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> and <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">backward()</span></code></a> static methods. Then, to use your custom +op in the forward pass, call the class method <code class="docutils literal notranslate"><span class="pre">apply</span></code>. Do not call +<a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> directly.</p> +<p>To ensure correctness and best performance, make sure you are calling the +correct methods on <code class="docutils literal notranslate"><span class="pre">ctx</span></code> and validating your backward function using +<code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.gradcheck()</span></code>.</p> +<p>See <span class="xref std std-ref">extending-autograd</span> for more details on how to use this class.</p> +<p>Examples:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)</span> +<span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">Exp</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="nd">@staticmethod</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="n">result</span> <span class="o">=</span> <span class="n">i</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">return</span> <span class="n">result</span> +<span class="gp">&gt;&gt;&gt;</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="nd">@staticmethod</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="n">result</span><span class="p">,</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">return</span> <span class="n">grad_output</span> <span class="o">*</span> <span class="n">result</span> +<span class="gp">&gt;&gt;&gt;</span> +<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Use it by calling the apply method:</span> +<span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP</span> +<span class="gp">&gt;&gt;&gt; </span><span class="n">output</span> <span class="o">=</span> <span class="n">Exp</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> +</pre></div> +</div> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward"> +<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">softmax_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#FlashAttnKVPackedFunc.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward" title="Permalink to this definition">#</a></dt> +<dd><p>q: (batch, seqlen_q, nheads, headdim) +kv: (batch, seqlen_k, 2, nheads, headdim) +bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).</p> +<blockquote> +<div><p>For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). +ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)</p> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward"> +<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#FlashAttnKVPackedFunc.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward" title="Permalink to this definition">#</a></dt> +<dd><p>Defines a formula for differentiating the operation with backward mode +automatic differentiation (alias to the vjp function).</p> +<p>This function is to be overridden by all subclasses.</p> +<p>It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by +as many outputs as the <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> returned (None will be passed in +for non tensor outputs of the forward function), +and it should return as many tensors, as there were inputs to +<a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. Each argument is the gradient w.r.t the given output, +and each returned value should be the gradient w.r.t. the +corresponding input. If an input is not a Tensor or is a Tensor not +requiring grads, you can just pass None as a gradient for that input.</p> +<p>The context can be used to retrieve tensors saved during the forward +pass. It also has an attribute <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx.needs_input_grad</span></code> as a tuple +of booleans representing whether each input needs gradient. E.g., +<a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code></a> will have <code class="docutils literal notranslate"><span class="pre">ctx.needs_input_grad[0]</span> <span class="pre">=</span> <span class="pre">True</span></code> if the +first input to <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> needs gradient computated w.r.t. the +output.</p> +</dd></dl> + +</dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.flash_attn_kvpacked_func"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">flash_attn_kvpacked_func</span></span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#flash_attn_kvpacked_func"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_kvpacked_func" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">FlashAttnFunc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#FlashAttnFunc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></p> +<p>Base class to create custom <cite>autograd.Function</cite></p> +<p>To create a custom <cite>autograd.Function</cite>, subclass this class and implement +the <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> and <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">backward()</span></code></a> static methods. Then, to use your custom +op in the forward pass, call the class method <code class="docutils literal notranslate"><span class="pre">apply</span></code>. Do not call +<a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> directly.</p> +<p>To ensure correctness and best performance, make sure you are calling the +correct methods on <code class="docutils literal notranslate"><span class="pre">ctx</span></code> and validating your backward function using +<code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.gradcheck()</span></code>.</p> +<p>See <span class="xref std std-ref">extending-autograd</span> for more details on how to use this class.</p> +<p>Examples:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)</span> +<span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">Exp</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="nd">@staticmethod</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="n">result</span> <span class="o">=</span> <span class="n">i</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">result</span><span class="p">)</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">return</span> <span class="n">result</span> +<span class="gp">&gt;&gt;&gt;</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="nd">@staticmethod</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="n">result</span><span class="p">,</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span> +<span class="gp">&gt;&gt;&gt; </span> <span class="k">return</span> <span class="n">grad_output</span> <span class="o">*</span> <span class="n">result</span> +<span class="gp">&gt;&gt;&gt;</span> +<span class="gp">&gt;&gt;&gt; </span><span class="c1"># Use it by calling the apply method:</span> +<span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP</span> +<span class="gp">&gt;&gt;&gt; </span><span class="n">output</span> <span class="o">=</span> <span class="n">Exp</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> +</pre></div> +</div> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward"> +<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">softmax_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#FlashAttnFunc.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward" title="Permalink to this definition">#</a></dt> +<dd><p>q: (batch_size, seqlen_q, nheads, headdim) +k, v: (batch_size, seqlen_k, nheads, headdim) +bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).</p> +<blockquote> +<div><p>For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k). +ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)</p> +</div></blockquote> +</dd></dl> + +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward"> +<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#FlashAttnFunc.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward" title="Permalink to this definition">#</a></dt> +<dd><p>Defines a formula for differentiating the operation with backward mode +automatic differentiation (alias to the vjp function).</p> +<p>This function is to be overridden by all subclasses.</p> +<p>It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by +as many outputs as the <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> returned (None will be passed in +for non tensor outputs of the forward function), +and it should return as many tensors, as there were inputs to +<a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. Each argument is the gradient w.r.t the given output, +and each returned value should be the gradient w.r.t. the +corresponding input. If an input is not a Tensor or is a Tensor not +requiring grads, you can just pass None as a gradient for that input.</p> +<p>The context can be used to retrieve tensors saved during the forward +pass. It also has an attribute <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx.needs_input_grad</span></code> as a tuple +of booleans representing whether each input needs gradient. E.g., +<a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code></a> will have <code class="docutils literal notranslate"><span class="pre">ctx.needs_input_grad[0]</span> <span class="pre">=</span> <span class="pre">True</span></code> if the +first input to <a class="reference internal" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward" title="lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> needs gradient computated w.r.t. the +output.</p> +</dd></dl> + +</dd></dl> + +<dl class="py data"> +<dt class="sig sig-object py" id="lmflow.utils.flash_attention.triton_flash_attention.flash_attn_func"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.flash_attention.triton_flash_attention.</span></span><span class="sig-name descname"><span class="pre">flash_attn_func</span></span><a class="reference internal" href="../../../../../_modules/lmflow/utils/flash_attention/triton_flash_attention.html#flash_attn_func"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_func" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../llama_flash_attention/index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.flash_attention.llama_flash_attention</span></code></p> + </div> + </a> + <a class="right-next" + href="../../position_interpolation/index.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.position_interpolation</span></code></p> + </div> + <i class="fa-solid fa-angle-right"></i> + </a> +</div> + </footer> + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner"> + + <div class="sidebar-secondary-item"> + <div class="page-toc tocsection onthispage"> + <i class="fa-solid fa-list"></i> On this page + </div> + <nav class="bd-toc-nav page-toc"> + <ul class="visible nav section-nav flex-column"> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#module-contents">Module Contents</a><ul class="nav section-nav flex-column"> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#classes">Classes</a></li> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#functions">Functions</a></li> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#attributes">Attributes</a><ul class="nav section-nav flex-column"> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._fwd_kernel"><code class="docutils literal notranslate"><span class="pre">_fwd_kernel()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_preprocess_do_o_dot"><code class="docutils literal notranslate"><span class="pre">_bwd_preprocess_do_o_dot()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_store_dk_dv"><code class="docutils literal notranslate"><span class="pre">_bwd_store_dk_dv()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel_one_col_block"><code class="docutils literal notranslate"><span class="pre">_bwd_kernel_one_col_block()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.init_to_zero"><code class="docutils literal notranslate"><span class="pre">init_to_zero()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel"><code class="docutils literal notranslate"><span class="pre">_bwd_kernel()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_forward"><code class="docutils literal notranslate"><span class="pre">_flash_attn_forward()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_backward"><code class="docutils literal notranslate"><span class="pre">_flash_attn_backward()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc"><code class="docutils literal notranslate"><span class="pre">FlashAttnQKVPackedFunc</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward"><code class="docutils literal notranslate"><span class="pre">FlashAttnQKVPackedFunc.forward()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward"><code class="docutils literal notranslate"><span class="pre">FlashAttnQKVPackedFunc.backward()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_qkvpacked_func"><code class="docutils literal notranslate"><span class="pre">flash_attn_qkvpacked_func</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc"><code class="docutils literal notranslate"><span class="pre">FlashAttnKVPackedFunc</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward"><code class="docutils literal notranslate"><span class="pre">FlashAttnKVPackedFunc.forward()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward"><code class="docutils literal notranslate"><span class="pre">FlashAttnKVPackedFunc.backward()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_kvpacked_func"><code class="docutils literal notranslate"><span class="pre">flash_attn_kvpacked_func</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc"><code class="docutils literal notranslate"><span class="pre">FlashAttnFunc</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward"><code class="docutils literal notranslate"><span class="pre">FlashAttnFunc.forward()</span></code></a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward"><code class="docutils literal notranslate"><span class="pre">FlashAttnFunc.backward()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_func"><code class="docutils literal notranslate"><span class="pre">flash_attn_func</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._fwd_kernel">_fwd_kernel</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_preprocess_do_o_dot">_bwd_preprocess_do_o_dot</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_store_dk_dv">_bwd_store_dk_dv</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel_one_col_block">_bwd_kernel_one_col_block</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.init_to_zero">init_to_zero</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel">_bwd_kernel</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_forward">_flash_attn_forward</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_backward">_flash_attn_backward</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc">FlashAttnQKVPackedFunc</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward">forward</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward">backward</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_qkvpacked_func">flash_attn_qkvpacked_func</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc">FlashAttnKVPackedFunc</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward">forward</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward">backward</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_kvpacked_func">flash_attn_kvpacked_func</a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc">FlashAttnFunc</a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward">forward</a></li> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward">backward</a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" 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py-mod docutils literal notranslate"><span class="pre">lmflow.utils.position_interpolation</span></code></a></li> + + <li class="breadcrumb-item active" aria-current="page"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch</span></code></li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="module-lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch"> +<span id="lmflow-utils-position-interpolation-llama-rope-scaled-monkey-patch"></span><h1><a class="reference internal" href="#module-lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch" title="lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch</span></code></a><a class="headerlink" href="#module-lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch" title="Permalink to this heading">#</a></h1> +<section id="module-contents"> +<h2>Module Contents<a class="headerlink" href="#module-contents" title="Permalink to this heading">#</a></h2> +<section id="classes"> +<h3>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding" title="lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CondenseRotaryEmbedding</span></code></a></p></td> +<td><p>Base class for all neural network modules.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="functions"> +<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">#</a></h3> +<table class="autosummary longtable table autosummary"> +<tbody> +<tr class="row-odd"><td><p><a class="reference internal" href="#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.replace_llama_with_condense" title="lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.replace_llama_with_condense"><code class="xref py py-obj docutils literal notranslate"><span class="pre">replace_llama_with_condense</span></code></a>(pi_ratio, ntk_ratio)</p></td> +<td><p></p></td> +</tr> +</tbody> +</table> +<dl class="py class"> +<dt class="sig sig-object py" id="lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding"> +<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.</span></span><span class="sig-name descname"><span class="pre">CondenseRotaryEmbedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pi_ratio</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ntk_ratio</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_position_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch.html#CondenseRotaryEmbedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding" title="Permalink to this definition">#</a></dt> +<dd><p>Bases: <code class="xref py py-obj docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p> +<p>Base class for all neural network modules.</p> +<p>Your models should also subclass this class.</p> +<p>Modules can also contain other Modules, allowing to nest them in +a tree structure. You can assign the submodules as regular attributes:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span> +<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span> + +<span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span> + <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> + <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span> + <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> + <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> + + <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span> + <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> + <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> +</pre></div> +</div> +<p>Submodules assigned in this way will be registered, and will have their +parameters converted too when you call <code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code>, etc.</p> +<div class="admonition note"> +<p class="admonition-title">Note</p> +<p>As per the example above, an <code class="docutils literal notranslate"><span class="pre">__init__()</span></code> call to the parent class +must be made before assignment on the child.</p> +</div> +<dl class="field-list simple"> +<dt class="field-odd">Variables<span class="colon">:</span></dt> +<dd class="field-odd"><p><strong>training</strong> (<em>bool</em>) – Boolean represents whether this module is in training or +evaluation mode.</p> +</dd> +</dl> +<dl class="py method"> +<dt class="sig sig-object py" id="lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding.forward"> +<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_len</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch.html#CondenseRotaryEmbedding.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding.forward" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</dd></dl> + +<dl class="py function"> +<dt class="sig sig-object py" id="lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.replace_llama_with_condense"> +<span class="sig-prename descclassname"><span class="pre">lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.</span></span><span class="sig-name descname"><span class="pre">replace_llama_with_condense</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pi_ratio</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ntk_ratio</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../../../_modules/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch.html#replace_llama_with_condense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.replace_llama_with_condense" title="Permalink to this definition">#</a></dt> +<dd></dd></dl> + +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="../index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow.utils.position_interpolation</span></code></p> + </div> + </a> + <a class="right-next" + href="../../constants/index.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title"><code class="xref py py-mod docutils 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internal nav-link" href="#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding"><code class="docutils literal notranslate"><span class="pre">CondenseRotaryEmbedding</span></code></a><ul class="nav section-nav flex-column"> +<li class="toc-h5 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding.forward"><code class="docutils literal notranslate"><span class="pre">CondenseRotaryEmbedding.forward()</span></code></a></li> +</ul> +</li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" href="#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.replace_llama_with_condense"><code class="docutils literal notranslate"><span class="pre">replace_llama_with_condense()</span></code></a></li> +<li class="toc-h4 nav-item toc-entry"><a class="reference internal nav-link" 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+</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="lmflow-benchmark-an-automatic-evaluation-framework-for-open-source-llms"> +<h1>LMFlow Benchmark: An Automatic Evaluation Framework for Open-Source LLMs<a class="headerlink" href="#lmflow-benchmark-an-automatic-evaluation-framework-for-open-source-llms" title="Permalink to this heading">#</a></h1> +<p>May 9, 2023</p> +<section id="introduction"> +<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">#</a></h2> +<p>Evaluation of a chat-style Large Language Model (LLM) has been a huge challenge since the breakthrough of ChatGPT. On the one hand, researchers and engineers need a reliable way to compare two models and decide which model to choose under a certain application scenario. On the other hand, they have to monitor the model performance during the training of an LLM to avoid performance issues such as forgetting.</p> +<p>Recent work of Vicuna introduces comparison methods of human evaluation, a.k.a. Chatbot Arena. They also pioneered the evaluation method by invoking GPT-4 to compare the outputs of two models. However, those methods require expensive human labeling or GPT-4 API calls, which are neither scalable nor convenient for LLM development.</p> +<p>In this article, we introduce LMFlow benchmark, a new benchmark which provides a cheap and easy-to-use evaluation framework that can help reflect different aspects of LLMs. We have open-sourced the dataset and the code as well, so that everyone in the LLM community can use those toolkits to evaluate, monitor or compare different LLMs.</p> +</section> +<section id="metric"> +<h2>Metric<a class="headerlink" href="#metric" title="Permalink to this heading">#</a></h2> +<p>In our evaluation framework, Negative Log Likelihood (NLL) is used for evaluating LLM +<img alt="" src="../_images/nll.png" /></p> +<p>which corresponds to the LLM model’s prediction probability over a corpus set given their contexts. If the corpus set itself indicates a certain type of LLM ability, such as multi-round conversation, instruction following, math problem solving, role-playing, then NLL on those corpora can provide quantitative metrics to reflect those abilities.</p> +<p><img alt="" src="../_images/benchmark-1.png" /></p> +<p>The key idea behind NLL, is that</p> +<p><em>Generation ability is positively correlated with prediction ability.</em></p> +<p>For instance, an LLM which performs well in essay writing should have no problem understanding and predicting a reference human essay, just like human chess masters performing well at memorizing an endgame on a chessboard.</p> +<p>Besides NLL, another similar and commonly used metric in NLP is Perplexity (PPL):</p> +<p><img alt="" src="../_images/ppl.png" /></p> +<p>Nevertheless, perplexity intrinsically depends on the lengths of the tokenized sequences, which induces unfair comparison between models with different tokenizers. For example, if a model has a smaller vocabulary size, it inherently results in a longer tokenized sequence and a lower token-level perplexity. Thus in all our experiments, we use NLL instead of PPL.</p> +<p>One huge advantage of NLL evaluation is that it does not require human involvement during the evaluation process. As long as the test reference corpus is given, one can evaluate different aspects of an LLM’s ability automatically. This makes the evaluation of LLM more accessible to researchers.</p> +<p>Besides its convenience, NLL itself is also a good metric. In our experimental results in commonsense QA, we find that NLL is correlated with QA accuracy when comparing the different finetuned versions of a single model.</p> +<p><strong>Table 1: Accuracy results in traditional commonsense QA benchmarks</strong></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-left"><p></p></th> +<th class="head text-center"><p>winogrande</p></th> +<th class="head text-center"><p>boolq</p></th> +<th class="head text-center"><p>arc_e</p></th> +<th class="head text-center"><p>hellaswag</p></th> +<th class="head text-center"><p>piqa</p></th> +<th class="head text-center"><p>obqa</p></th> +<th class="head text-center"><p>arc_c</p></th> +<th class="head text-center"><p>Average</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-left"><p>bloom-3b</p></td> +<td class="text-center"><p>58.7</p></td> +<td class="text-center"><p>61.6</p></td> +<td class="text-center"><p>59.5</p></td> +<td class="text-center"><p>52.7</p></td> +<td class="text-center"><p>70.8</p></td> +<td class="text-center"><p>42.2</p></td> +<td class="text-center"><p>30.6</p></td> +<td class="text-center"><p>53.7</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>bloom-7.1b</p></td> +<td class="text-center"><p>64.4</p></td> +<td class="text-center"><p>62.9</p></td> +<td class="text-center"><p>65.0</p></td> +<td class="text-center"><p>59.6</p></td> +<td class="text-center"><p>73.6</p></td> +<td class="text-center"><p>35.8</p></td> +<td class="text-center"><p>33.4</p></td> +<td class="text-center"><p>56.3</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>opt-6.9b</p></td> +<td class="text-center"><p>65.2</p></td> +<td class="text-center"><p>66.1</p></td> +<td class="text-center"><p>65.6</p></td> +<td class="text-center"><p>67.2</p></td> +<td class="text-center"><p>76.5</p></td> +<td class="text-center"><p>37.4</p></td> +<td class="text-center"><p>34.6</p></td> +<td class="text-center"><p>58.9</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>opt-13b</p></td> +<td class="text-center"><p>65.0</p></td> +<td class="text-center"><p>65.9</p></td> +<td class="text-center"><p>67.1</p></td> +<td class="text-center"><p>69.8</p></td> +<td class="text-center"><p>76.9</p></td> +<td class="text-center"><p>39.0</p></td> +<td class="text-center"><p>35.7</p></td> +<td class="text-center"><p>59.9</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>llama-7b</p></td> +<td class="text-center"><p>67.9</p></td> +<td class="text-center"><p>73.2</p></td> +<td class="text-center"><p>67.3</p></td> +<td class="text-center"><p>73.0</p></td> +<td class="text-center"><p>78.3</p></td> +<td class="text-center"><p>42.4</p></td> +<td class="text-center"><p>41.4</p></td> +<td class="text-center"><p>62.7</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>llama-13b</p></td> +<td class="text-center"><p><strong>70.0</strong></p></td> +<td class="text-center"><p><strong>68.5</strong></p></td> +<td class="text-center"><p><strong>74.5</strong></p></td> +<td class="text-center"><p><strong>76.2</strong></p></td> +<td class="text-center"><p><strong>79.1</strong></p></td> +<td class="text-center"><p><strong>42.2</strong></p></td> +<td class="text-center"><p><strong>44.5</strong></p></td> +<td class="text-center"><p><strong>65.0</strong></p></td> +</tr> +</tbody> +</table> +<p><strong>Table 2: NLL results in corpus of commonsense QA benchmarks</strong></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-left"><p></p></th> +<th class="head text-center"><p>winogrande</p></th> +<th class="head text-center"><p>boolq</p></th> +<th class="head text-center"><p>arc_e</p></th> +<th class="head text-center"><p>hellaswag</p></th> +<th class="head text-center"><p>piqa</p></th> +<th class="head text-center"><p>obqa</p></th> +<th class="head text-center"><p>arc_c</p></th> +<th class="head text-center"><p>Average</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-left"><p>bloom-3b</p></td> +<td class="text-center"><p>86.5</p></td> +<td class="text-center"><p>228</p></td> +<td class="text-center"><p>86</p></td> +<td class="text-center"><p>245</p></td> +<td class="text-center"><p>134</p></td> +<td class="text-center"><p>64.5</p></td> +<td class="text-center"><p>101.5</p></td> +<td class="text-center"><p>135.1</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>bloom-7.1b</p></td> +<td class="text-center"><p>85</p></td> +<td class="text-center"><p>215</p></td> +<td class="text-center"><p>81.5</p></td> +<td class="text-center"><p>237</p></td> +<td class="text-center"><p>130</p></td> +<td class="text-center"><p>62.5</p></td> +<td class="text-center"><p>96</p></td> +<td class="text-center"><p>129.5</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>opt-6.9b</p></td> +<td class="text-center"><p>81.5</p></td> +<td class="text-center"><p>200</p></td> +<td class="text-center"><p>81.5</p></td> +<td class="text-center"><p>224</p></td> +<td class="text-center"><p>125</p></td> +<td class="text-center"><p>61</p></td> +<td class="text-center"><p>96</p></td> +<td class="text-center"><p>124.1</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>opt-13b</p></td> +<td class="text-center"><p>82</p></td> +<td class="text-center"><p>198</p></td> +<td class="text-center"><p>82.5</p></td> +<td class="text-center"><p>220</p></td> +<td class="text-center"><p>125</p></td> +<td class="text-center"><p>61.8</p></td> +<td class="text-center"><p>97</p></td> +<td class="text-center"><p>123.7</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>llama-7b</p></td> +<td class="text-center"><p>79.5</p></td> +<td class="text-center"><p>167</p></td> +<td class="text-center"><p>71.5</p></td> +<td class="text-center"><p>214</p></td> +<td class="text-center"><p>121</p></td> +<td class="text-center"><p>58</p></td> +<td class="text-center"><p>85</p></td> +<td class="text-center"><p>113.7</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>llama-13b</p></td> +<td class="text-center"><p><strong>79</strong></p></td> +<td class="text-center"><p><strong>153</strong></p></td> +<td class="text-center"><p><strong>70</strong></p></td> +<td class="text-center"><p><strong>207</strong></p></td> +<td class="text-center"><p><strong>119</strong></p></td> +<td class="text-center"><p><strong>57.3</strong></p></td> +<td class="text-center"><p><strong>83</strong></p></td> +<td class="text-center"><p><strong>109.7</strong></p></td> +</tr> +</tbody> +</table> +<p><strong>Figure 1: Correlation between NLL and accuracy on commonsense QA benchmarks</strong></p> +<p><img alt="" src="../_images/benchmark-2.png" /></p> +<p>In the above figure, one can find that QA accuracy is roughly correlated to NLL. Thus NLL is able to reflect the “magnitude” of prediction level difference between models. A huge gap in NLL normally entails a huge performance gap.</p> +<p>In the following sections, we provide a comprehensive evaluation of currently available LLM models and summarize their performance. Due to page limits, we only demonstrate partial evaluation results in this article. The full results can be found here <a class="reference external" href="https://docs.google.com/spreadsheets/d/1JYh4_pxNzmNA9I0YM2epgRA7VXBIeIGS64gPJBg5NHA/edit?usp=sharing">LLM Comparison Sheet 0501</a>. One can also evaluate their own LLM models using our LMFlow evaluation toolkit (<a class="reference external" href="https://optimalscale.github.io/LMFlow/examples/TASK_GUIDE.html">https://optimalscale.github.io/LMFlow/examples/TASK_GUIDE.html</a>).</p> +<p>We also introduce our own finetuned model Robin-7b-Chat and compare its performance with some other popular models. One may try our online demo LMFlow-Chat at <a class="reference external" href="https://lmflow.com/">https://lmflow.com/</a>.</p> +</section> +<section id="chat-performance"> +<h2>Chat Performance<a class="headerlink" href="#chat-performance" title="Permalink to this heading">#</a></h2> +<p>The first aspect of LLM is chat performance, especially multi-round chat. Based on our experimental results, we have the following observations:</p> +<ol class="arabic simple"> +<li><p><em>Without finetuning, foundation models do not perform well on chat.</em></p></li> +<li><p><em>After finetuning, foundation models can all obtain huge performance gain.</em></p></li> +<li><p><em>Our Robin-Chat-7b achieves comparable chat performance with Vicuna-13b.</em></p></li> +</ol> +<p><strong>Table 3: NLL on multi-round chat datasets, Lower the better</strong></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-left"><p></p></th> +<th class="head text-center"><p>En_multi_round_chat</p></th> +<th class="head text-center"><p>En_singe_round_chat</p></th> +<th class="head text-center"><p>Average</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-left"><p>Bloom-7.1b</p></td> +<td class="text-center"><p>188</p></td> +<td class="text-center"><p>213</p></td> +<td class="text-center"><p>200.5</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Bloom-7.1b-chat</p></td> +<td class="text-center"><p>164</p></td> +<td class="text-center"><p>181</p></td> +<td class="text-center"><p>172.5</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>Llama-7b</p></td> +<td class="text-center"><p>163</p></td> +<td class="text-center"><p>184</p></td> +<td class="text-center"><p>173.5</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Vicuna-7b</p></td> +<td class="text-center"><p>155</p></td> +<td class="text-center"><p>175</p></td> +<td class="text-center"><p>165</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>Vicuna-13b</p></td> +<td class="text-center"><p>150</p></td> +<td class="text-center"><p>170</p></td> +<td class="text-center"><p>160</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Robin-Chat-7b</p></td> +<td class="text-center"><p><strong>146</strong></p></td> +<td class="text-center"><p><strong>164</strong></p></td> +<td class="text-center"><p><strong>155</strong></p></td> +</tr> +</tbody> +</table> +<p>We also evaluate Vicuna-13b and our Robin-Chat-7b model qualitatively via use cases. It turns out that Robin-Chat-7b’s performance is comparable to Vicuna-13b. For example, both models can effortlessly refer to the conversation context with only slight mistakes, where Vicuna-13b sometimes doesn’t count the item correctly, while Robin-Chat-7b introduces only Mexican foods for international cuisine recommendations and omits other choices.</p> +<p><strong>Table 4: Use cases of multi-round chat</strong></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-left"><p>Round</p></th> +<th class="head text-center"><p>Question</p></th> +<th class="head text-center"><p>Vicuna-13b</p></th> +<th class="head text-center"><p>Robin-Chat-7b</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-left"><p>R1</p></td> +<td class="text-center"><p>Where is the capital of USA?</p></td> +<td class="text-center"><p>The capital of the United States is <strong>Washington, D.C.</strong> (District of Columbia). It is located on the east coast of the country, in the northern part of the state of Maryland. Washington, D.C. is named after George Washington, the first president of the United States. It is a federal district, which means that it is not part of any state, but is instead directly administered by the federal government. The city is home to many national landmarks, including the White House, the Capitol Building, and the Supreme Court. It is also home to many museums, monuments, and other cultural institutions.</p></td> +<td class="text-center"><p>The capital of the United States is <strong>Washington, D.C.</strong> (District of Columbia). It is located on the east coast of the USA, between the states of Maryland and Virginia.</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>R2</p></td> +<td class="text-center"><p>Can you introduce some popular food there?</p></td> +<td class="text-center"><p><p>Washington, D.C. is a city with a diverse food scene, and there are many popular restaurants and cuisines to choose from. Some popular foods in Washington, D.C. include:</p><p>- American Classics: There are many restaurants in Washington, D.C. that serve classic American dishes such as hamburgers, hot dogs, and apple pie.</p><p>- Seafood: Washington, D.C. is located near the Chesapeake Bay, which is known for its fresh seafood. Many restaurants in the city serve dishes such as crab cakes, shrimp, and oysters.</p><p>- Ethnic Cuisine: Washington, D.C. has a diverse population, and there are many restaurants that serve food from different parts of the world. Some popular ethnic cuisines include Mexican, Chinese, Japanese, and Korean.</p><p>- Barbecue: Washington, D.C. is also known for its barbecue restaurants. Many of these restaurants serve pulled pork, ribs, and brisket.</p><p>- <strong>Donuts</strong>: Washington, D.C. is home to the famous “Donut Happy” which is a famous donut shop with a variety of flavors and toppings.</p><p>- Street Food: There are also many street food options in Washington, D.C. such as food trucks and stands that serve food such as falafel, kebabs, and tacos.</p><p>These are just a few examples of the many types of food that are available in Washington, D.C. There is something for everyone in this city when it comes to food.</p><p></p></p></td> +<td class="text-center"><p><p>Washington, D.C. is a diverse city with a wide range of cuisines to choose from. Here are some popular foods that you can try when you visit:</p><p>1. Soul food: This style of cuisine originated in the southern United States and is known for its bold flavors and hearty portions. Some popular soul food dishes in Washington, D.C. include fried chicken, collard greens, and macaroni and cheese.</p><p>2. Ethiopian cuisine: Ethiopian food is known for its spicy and flavorful dishes, and it is a popular choice in Washington, D.C. Some popular dishes include injera (a sourdough flatbread), wat (a spicy vegetable stew), and kitfo (a spicy meat dish).</p><p>3. Seafood: Washington, D.C. is located near the Chesapeake Bay and has easy access to fresh seafood. Some popular seafood dishes include crab cakes, oysters, and seafood pasta.</p><p>4. BBQ: Washington, D.C. has a thriving barbecue scene, with many restaurants serving up slow-cooked meats and sides. Some popular BBQ spots include Ben’s Chili Bowl, Pete’s Soul Vegetarian, and Smoke &amp; Barrel.</p><p>5. <strong>International cuisine:</strong> Washington, D.C. is a hub for international diplomacy, and as a result, it has a diverse culinary scene with many restaurants serving dishes from around the world. Some popular international cuisines include Japanese, Indian, and <strong>Mexican.</strong></p><p></p></p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>R3</p></td> +<td class="text-center"><p>Can you be more specific about the <strong>fifth item</strong> above?</p></td> +<td class="text-center"><p><p>Sure, <strong>the fifth item I mentioned, “Barbecue,”</strong> refers to a style of cooking and food that originated in the southern United States. Barbecue is usually made with meat that is slow-cooked over an open flame or heated coals. The meat is often marinated and then sauced, and it is typically served with a variety of sides such as coleslaw, baked beans, and corn on the cob. Barbecue is a popular cuisine in Washington, D.C. and many of the restaurants serve pulled pork, ribs, and brisket. Some of the most popular barbecue restaurants in Washington, D.C. include:</p><p>- The Rib Pit</p><p>- The Smokehouse</p><p>- The BBQ Joint</p><p>- The Salt Line</p><p>- The Sovereign</p><p>These are just a few examples of the many barbecue restaurants in Washington, D.C. that offer delicious and flavorful barbecue dishes.</p></p></td> +<td class="text-center"><p><p>Sure! Here are some specific recommendations for <strong>Mexican food</strong> in Washington, D.C.:</p><p>1. Margarita’s - This popular Mexican restaurant in Adams Morgan has been serving up authentic Mexican cuisine for over 30 years. Some of their most popular dishes include their tacos al pastor (grilled marinated pork), enchiladas (meat or cheese filled tortillas), and chiles rellenos (stuffed peppers).</p><p>2. El Comal - This cozy little restaurant in the Columbia Heights neighborhood has been serving up traditional Mexican cuisine for over 20 years. Some of their most popular dishes include their mole (a rich, spicy sauce made from a blend of chiles, spices, and chocolate), tacos de carnitas (crispy pork tacos), and chiles rellenos de queso (cheese stuffed peppers).</p><p>3. Casa Oaxaca - This upscale Mexican restaurant in the Shaw neighborhood has a menu that features a wide range of traditional and modern dishes. Some of their most popular dishes include their ceviche (raw fish marinated in lime juice and spices), mole negro (a rich, dark mole sauce), and chiles en nogada (a spicy dish made with poblano peppers, walnuts, and honey).</p><p>4. Taco King - This popular taco chain has several locations in the Washington, D.C. area, including one in the Adams Morgan neighborhood. They offer a wide range of tacos, burritos, and other Mexican dishes at affordable prices. Some of their most popular items include their carne asada tacos, chicken tacos, and quesadillas.</p><p>5. Los Tacos No. 1 - This small, casual taco stand in the Columbia Heights neighborhood has a menu that features a wide range of tacos, burritos, and other Mexican dishes. Some of their most popular items include their adobada tacos (marinated pork tacos), al pastor tacos (grilled marinated pork tacos), and chicken tacos.</p></p></td> +</tr> +</tbody> +</table> +<p><strong>Experiment Details</strong></p> +<p>Our dataset comes from real conversation history between humans and ChatGPT. We collect 183 English conversations with a total of 447 rounds of chats, where the original conversation history is already multi-round. For single-round NLL evaluation, we break a multi-round conversation into multiple single-round text pairs. For example, with a conversation history</p> +<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Q1, A1 + +Q2, A2 + +Q3, A3 +</pre></div> +</div> +<p>where Q stands for Question, A stands for answer, we can get three single-round examples:</p> +<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Context: Q1 + +Output: A1 + +Context: Q1, A1, Q2 + +Output: A2 + +Context: Q1, A1, Q2, A2, Q3 + +Ouptut: A3 +</pre></div> +</div> +</section> +<section id="commonsense-performance"> +<h2>CommonSense Performance<a class="headerlink" href="#commonsense-performance" title="Permalink to this heading">#</a></h2> +<p>Another important aspect of an LLM model is its common sense ability, where a model should acquire a certain level of factual knowledge and utilize them properly under different scenarios. Regarding this aspect of the ability, we found:</p> +<ol class="arabic simple"> +<li><p><em>Finetuning on chat dataset results in commonsense degradation.</em></p></li> +<li><p><em>Our Robin-Chat-7b model still achieves a competitive performance.</em></p></li> +</ol> +<p><strong>Table 5: Accuracy results in commonsense QA benchmarks</strong></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-left"><p></p></th> +<th class="head text-center"><p>winogrand</p></th> +<th class="head text-center"><p>boolq</p></th> +<th class="head text-center"><p>arc_easy</p></th> +<th class="head text-center"><p>hellaswag</p></th> +<th class="head text-center"><p>piqa</p></th> +<th class="head text-center"><p>obqa</p></th> +<th class="head text-center"><p>arc_c</p></th> +<th class="head text-center"><p>Average</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-left"><p>Bloom-7.1b</p></td> +<td class="text-center"><p>64.4</p></td> +<td class="text-center"><p>62.9</p></td> +<td class="text-center"><p>65.0</p></td> +<td class="text-center"><p>59.6</p></td> +<td class="text-center"><p>73.6</p></td> +<td class="text-center"><p>35.8</p></td> +<td class="text-center"><p>33.4</p></td> +<td class="text-center"><p>56.4</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Bloom-7.1b-chat</p></td> +<td class="text-center"><p>60.3</p></td> +<td class="text-center"><p>56.8</p></td> +<td class="text-center"><p>61.3</p></td> +<td class="text-center"><p>58.7</p></td> +<td class="text-center"><p>72.7</p></td> +<td class="text-center"><p>37.8</p></td> +<td class="text-center"><p>38.7</p></td> +<td class="text-center"><p>55.2</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>Llama-7b</p></td> +<td class="text-center"><p>67.9</p></td> +<td class="text-center"><p>73.2</p></td> +<td class="text-center"><p>67.3</p></td> +<td class="text-center"><p>73.0</p></td> +<td class="text-center"><p>78.4</p></td> +<td class="text-center"><p>42.4</p></td> +<td class="text-center"><p>41.4</p></td> +<td class="text-center"><p>62.7</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Vicuna-7b</p></td> +<td class="text-center"><p>63.7</p></td> +<td class="text-center"><p>77.4</p></td> +<td class="text-center"><p>63.1</p></td> +<td class="text-center"><p>68.8</p></td> +<td class="text-center"><p>76.3</p></td> +<td class="text-center"><p>39.6</p></td> +<td class="text-center"><p>38.7</p></td> +<td class="text-center"><p>61.1</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>Vicuna-13b</p></td> +<td class="text-center"><p>66.2</p></td> +<td class="text-center"><p>79.9</p></td> +<td class="text-center"><p>64.7</p></td> +<td class="text-center"><p>73.0</p></td> +<td class="text-center"><p>77.6</p></td> +<td class="text-center"><p>41.6</p></td> +<td class="text-center"><p>40.4</p></td> +<td class="text-center"><p>63.3</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Robin-Chat-7b</p></td> +<td class="text-center"><p>64.7</p></td> +<td class="text-center"><p>75.2</p></td> +<td class="text-center"><p>69.8</p></td> +<td class="text-center"><p>72.4</p></td> +<td class="text-center"><p>76.6</p></td> +<td class="text-center"><p>39.0</p></td> +<td class="text-center"><p>42.9</p></td> +<td class="text-center"><p>62.9</p></td> +</tr> +</tbody> +</table> +<p><strong>Table 6: NLL results in corpus of commonsense QA benchmarks</strong></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-left"><p></p></th> +<th class="head text-center"><p>winogrand</p></th> +<th class="head text-center"><p>boolq</p></th> +<th class="head text-center"><p>arc_easy</p></th> +<th class="head text-center"><p>hellaswag</p></th> +<th class="head text-center"><p>piqa</p></th> +<th class="head text-center"><p>obqa</p></th> +<th class="head text-center"><p>arc_c</p></th> +<th class="head text-center"><p>Average</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-left"><p>Bloom-7.1b</p></td> +<td class="text-center"><p>96.0</p></td> +<td class="text-center"><p>254</p></td> +<td class="text-center"><p>89</p></td> +<td class="text-center"><p>266</p></td> +<td class="text-center"><p>147</p></td> +<td class="text-center"><p>69</p></td> +<td class="text-center"><p>106.5</p></td> +<td class="text-center"><p>146.7</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Bloom-7.1b-chat</p></td> +<td class="text-center"><p>85.0</p></td> +<td class="text-center"><p>215</p></td> +<td class="text-center"><p>81.5</p></td> +<td class="text-center"><p>237</p></td> +<td class="text-center"><p>130</p></td> +<td class="text-center"><p>62.5</p></td> +<td class="text-center"><p>96</p></td> +<td class="text-center"><p>129.5</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>Llama-7b</p></td> +<td class="text-center"><p>79.5</p></td> +<td class="text-center"><p>167</p></td> +<td class="text-center"><p>71.5</p></td> +<td class="text-center"><p>214</p></td> +<td class="text-center"><p>121</p></td> +<td class="text-center"><p>58</p></td> +<td class="text-center"><p>85</p></td> +<td class="text-center"><p>113.7</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Vicuna-7b</p></td> +<td class="text-center"><p>97.0</p></td> +<td class="text-center"><p>219</p></td> +<td class="text-center"><p>89</p></td> +<td class="text-center"><p>258</p></td> +<td class="text-center"><p>151</p></td> +<td class="text-center"><p>72</p></td> +<td class="text-center"><p>105</p></td> +<td class="text-center"><p>141.6</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>Vicuna-13b</p></td> +<td class="text-center"><p>95.5</p></td> +<td class="text-center"><p>207</p></td> +<td class="text-center"><p>88.5</p></td> +<td class="text-center"><p>262</p></td> +<td class="text-center"><p>149</p></td> +<td class="text-center"><p>73</p></td> +<td class="text-center"><p>105</p></td> +<td class="text-center"><p>140.0</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Robin-Chat-7b</p></td> +<td class="text-center"><p>89.0</p></td> +<td class="text-center"><p>200</p></td> +<td class="text-center"><p>81</p></td> +<td class="text-center"><p>240</p></td> +<td class="text-center"><p>139</p></td> +<td class="text-center"><p>65.5</p></td> +<td class="text-center"><p>96.5</p></td> +<td class="text-center"><p>130.1</p></td> +</tr> +</tbody> +</table> +<p>It is surprising to find that finetuning actually leads to commonsense degradation. A more well-known consequence of this issue is hallucination, where models tend to give plausible but factually incorrect or irrelevant answers. One explanation of this phenomenon is that LLMs “forget” the pretrained knowledge during finetuning when attempting to fit the finetune dataset.</p> +<p>As one can see from the table above, both Vicuna and Robin suffer from this commonsense degradation issue. However, Robin suffers less. We conjecture that this improvement is brought by finetuning with smaller-sized but higher-quality dataset. Vicuna uses sharegpt data with ~900MB size and was finetuned for 3 epochs, while Robin uses multiple sources of dataset with ~600MB size and was only finetuned for 2 epochs. This suggests that in the finetuning stage, data quality plays a critical role.</p> +<p>This improvement gives Vicuna-13b a run for its money when competing with Robin. The following examples show that Robin-Chat-7b possesses a comparable level of commonsense when compared with Vicuna-13b.</p> +<p><strong>Table 7: Use cases of commonsense QA</strong></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-left"><p>Question</p></th> +<th class="head text-center"><p>Vicuna-13b</p></th> +<th class="head text-center"><p>Robin-Chat-7b</p></th> +<th class="head text-center"><p>Note</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-left"><p><p>I go to a friend’s home but the door is locked. However, I smell coal gas. Should I break the window?</p><p></p></p></td> +<td class="text-center"><p>If you smell coal gas, it is important to get out of the house as quickly as possible and call the fire department or the gas company. Coal gas is a flammable gas that can be dangerous and even deadly if it is not properly ventilated. If the door is locked, you <strong>should not break the window because it could cause a fire or explosion</strong>. Instead, try to find another way to escape the house or call for help. If you cannot leave the house safely, try to stay calm and wait for help to arrive.</p></td> +<td class="text-center"><p>No, it is not recommended to break a window in this situation. <strong>Breaking a window can be dangerous and can cause injury or damage to yourself or others</strong>. Additionally, it is <strong>illegal</strong> to break a window to enter a property without permission. Instead, you should try to find another way to enter the home, such as by using a key or by calling the homeowner or a locksmith for assistance. If you are unable to enter the home, you should call the emergency services or the police for assistance. In the meantime, it is important to <strong>avoid using any sources of heat</strong>, such as a lighter or matches, as these can cause a gas leak or explosion. If you smell gas, you should evacuate the area immediately and call the emergency services or the gas company for assistance.</p></td> +<td class="text-center"><p><em>Breaking the window helps the ventilation and will not cause a gas explosion directly. However, it is indeed illegal and may injure yourself when breaking the window.</em></p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Can I bring my pet ferret to that beautiful pacific island of USA to attend ICML 2023?</p></td> +<td class="text-center"><p>I’m sorry, but I am <strong>not aware of</strong> the specific policies regarding pets at the ICML 2023 conference or the island you are referring to. It would be best to check with the conference organizers or the local authorities on the island to determine if ferrets are allowed and if there are any necessary procedures or requirements for bringing a pet to the conference.</p></td> +<td class="text-center"><p><p>It is <strong>not recommended</strong> to bring a pet ferret to the United States, including to attend ICML 2023. Ferrets are not permitted as pets in many areas of the United States, and <strong>it is illegal</strong> to bring them into the country without proper documentation and licensing. Additionally, ferrets can <strong>carry diseases</strong> that are not common in humans but can be dangerous to people, such as toxoplasmosis and salmonellosis. It is important to prioritize the health and safety of yourself and others by not bringing a pet ferret to a crowded event like ICML 2023.</p><p></p></p></td> +<td class="text-center"><p><em>ICML 2023 will be held in Hawaii, where ferrets are strictly prohibited as pets because they are potential carriers of the rabies virus.</em></p></td> +</tr> +</tbody> +</table> +<p><strong>Experiment Details</strong></p> +<p>Here, we make use of Eleuther AI’s LM evaluation harness repository (<a class="github reference external" href="https://github.com/EleutherAI/lm-evaluation-harness">EleutherAI/lm-evaluation-harness</a>) to get QA accuracy results. We also evaluate all models’ NLL metrics on their datasets, with their questions as contexts and answers as output sentences.</p> +</section> +<section id="instruction-following"> +<h2>Instruction Following<a class="headerlink" href="#instruction-following" title="Permalink to this heading">#</a></h2> +<p>The final aspect we evaluate is instruction following, where a model is expected to loyally obey the user’s instructions, given the instruction being ethically or legally appropriate. It turns out that,</p> +<ol class="arabic simple"> +<li><p><em>Without finetuning, foundation models do not perform well on instruction following.</em></p></li> +<li><p><em>Finetuning on chat dataset helps improve the performance.</em></p></li> +<li><p><em>Our Robin-Chat is still comparable with Vicuna-13b.</em></p></li> +</ol> +<p><strong>Table 8: NLL on instruction following chat datasets</strong></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-left"><p></p></th> +<th class="head text-center"><p>gpt4_en_instruction</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-left"><p>Alpaca</p></td> +<td class="text-center"><p>222</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Llama-7b</p></td> +<td class="text-center"><p>214</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>Vicuna-7b</p></td> +<td class="text-center"><p>211</p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Vicuna-13b</p></td> +<td class="text-center"><p>206</p></td> +</tr> +<tr class="row-even"><td class="text-left"><p>Robin-Chat-7b</p></td> +<td class="text-center"><p><strong>180</strong></p></td> +</tr> +</tbody> +</table> +<p>Following examples show that Robin-Chat-7b can generate similar responses on instruction following tasks when compared with Vicuna-13b.</p> +<p><strong>Table 9: Uses cases of instruction following</strong></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-left"><p>Question</p></th> +<th class="head text-center"><p>Vicuna-13b</p></th> +<th class="head text-center"><p>Robin-Chat-7b</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-left"><p>Can you write quicksort?</p></td> +<td class="text-center"><p><p>Yes, here is a simple implementation of the quicksort algorithm in Python:</p><p>def quicksort(arr):</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>if len(arr) &lt;= 1:</p><p><code class="docutils literal notranslate">&#160;&#160;&#160;&#160;&#160;&#160;&#160; </code>return arr</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>pivot = arr[len(arr) // 2]</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>left = [x for x in arr if x &lt; pivot]</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>middle = [x for x in arr if x == pivot]</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>right = [x for x in arr if x &gt; pivot]</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>return quicksort(left) + middle + quicksort(right)</p><p></p><p>This implementation uses the middle element of the array as the pivot, and then partitions the array into three sub-arrays: one containing all elements that are less than the pivot, one containing all elements that are equal to the pivot, and one containing all elements that are greater than the pivot. It then recursively applies the quicksort algorithm to the left and right sub-arrays, and returns the concatenation of the sorted left sub-array, the unsorted middle sub-array, and the sorted right sub-array.</p><p>This implementation has a time complexity of O(n log n) due to the recursive calls, although it could potentially be faster in practice if the pivot is carefully chosen.</p><p></p></p></td> +<td class="text-center"><p><p>Sure, here is a simple implementation of the quicksort algorithm in Python:</p><p>def quicksort(arr):</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>if len(arr) &lt;= 1:</p><p><code class="docutils literal notranslate">&#160;&#160;&#160;&#160;&#160;&#160;&#160; </code>return arr</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>pivot = arr[len(arr) // 2]</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>left = [x for x in arr if x &lt; pivot]</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>middle = [x for x in arr if x == pivot]</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>right = [x for x in arr if x &gt; pivot]</p><p><code class="docutils literal notranslate">&#160;&#160;&#160; </code>return quicksort(left) + middle + quicksort(right)</p><p></p><p>This implementation chooses the middle element of the array as the pivot, and then partitions the array into three parts: elements less than the pivot, elements equal to the pivot, and elements greater than the pivot. It then recursively applies the quicksort algorithm to the left and right subarrays, and concatenates the results with the middle subarray to produce the final sorted array.</p><p>Note that this implementation uses a single pass through the array, which is the most efficient way to sort an array using quicksort.</p></p></td> +</tr> +<tr class="row-odd"><td class="text-left"><p>Instruction: Find synonyms for the given word. You need to write down how the provided synonyms differ from the original word in terms of meaning, usage, etc. adversity</p></td> +<td class="text-center"><p><p>Synonyms for “adversity” include:</p><p>- Difficulty</p><p>- Hardship</p><p>- Trouble</p><p>- Misfortune</p><p>- Adversity</p><p>- Difficulties</p><p>- Hard times</p><p>All of these words refer to challenging or unpleasant circumstances or situations. However, “adversity” specifically connotes a difficult or hostile situation that is caused by external factors, such as an enemy or a natural disaster. “Difficulty” and “hardship” can be caused by external or internal factors, and can refer to a wide range of challenges, from mild inconveniences to major setbacks. “Trouble” often connotes a specific type of difficulty or distress, such as financial trouble or legal trouble. “Misfortune” specifically refers to bad luck or ill fate, while “hard times” refers to a general period of economic or social distress.</p></p></td> +<td class="text-center"><p><p>1. Hardship</p><p>2. Struggle</p><p>3. Difficulty</p><p>4. Oppression</p><p>5. Trial</p><p>6. Affliction</p><p>7. Distress</p><p>8. Calamity</p><p>9. Tribulation</p><p>10. Hardship</p><p>Adversity refers to difficult or challenging circumstances or conditions. Hardship, struggle, difficulty, oppression, trial, affliction, distress, and calamity are all words that can be used to describe adversity, but they often imply a more specific or intense situation. For example, hardship can refer to a general difficulty or challenge, while struggle implies a more intense battle or conflict. Difficulty and oppression imply a more specific obstacle or injustice, while trial and affliction suggest a more specific test or suffering. Trial can also refer to a legal proceeding, while tribulation implies a more general time of suffering or persecution.</p></p></td> +</tr> +</tbody> +</table> +<p><strong>Experiment Details</strong></p> +<p>In order to make evaluations for models’ instruction following ability, we collected GPT-4 instruction data from the project “Instruction Tuning with GPT-4”, which provides many instruction tuning data labeled by GPT-4. We sample 1,000 English data and manually filtered examples with the following issues, where 767 effective samples remain after the filtering:</p> +<ol class="arabic simple"> +<li><p>Long response with too many nonsense words</p></li> +<li><p>Incomplete input texts</p></li> +<li><p>Specific domains involving chemistry/biology, where most LLM models do not possess the knowledge and always fail</p></li> +</ol> +</section> +<section id="conclusion"> +<h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this heading">#</a></h2> +<p>In this article, we introduce LMFlow’s evaluation framework, which uses NLL metric to reflect LLM models’ ability. NLL provides a good metric to evaluate different aspects of a LLM model. According to our evaluation results, Robin-7b achieves on-par performance when compared with Vicuna-13b. As our Robin-7b model is finetuned with different sources of dataset instead of sharegpt only, this shows that Vicuna can be further improved or surpassed with smaller-sized models and better dataset.</p> +<p>The checkpoint of Robin-7b is now available for engineers and researchers to download and use (<a class="github reference external" href="https://github.com/OptimalScale/LMFlow#model-zoo">OptimalScale/LMFlow</a>). Its effectiveness demonstrates that a multi-aspect evaluation is indeed essential to the development of LLMs.</p> +</section> +<section id="references"> +<h2>References<a class="headerlink" href="#references" title="Permalink to this heading">#</a></h2> +<p>Vicuna Chatbot Arena: <a class="reference external" href="https://chat.lmsys.org/?arena">https://chat.lmsys.org/?arena</a></p> +<p>lm-evaluation-harness: <a class="github reference external" href="https://github.com/EleutherAI/lm-evaluation-harness">EleutherAI/lm-evaluation-harness</a></p> +<p>LMFlow: <a class="github reference external" href="https://github.com/OptimalScale/LMFlow">OptimalScale/LMFlow</a></p> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p 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href="reward_modeling.html">Reward Modeling</a></li> +</ul> +<ul class="nav bd-sidenav"> +<li class="toctree-l1"><a class="reference internal" href="raft.html">1 Introduction</a></li> + + +</ul> +<ul class="nav bd-sidenav"> +<li class="toctree-l1"><a class="reference internal" href="TASK_GUIDE.html">LMFlow Benchmark Guide</a></li> + + +</ul> +</div> +</nav></div> + </div> + + + <div class="sidebar-primary-items__end sidebar-primary__section"> + </div> + + <div id="rtd-footer-container"></div> + + + </div> + + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"> +<div class="header-article-items header-article__inner"> + + <div class="header-article-items__start"> + + <div class="header-article-item"> + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li> + + <li class="breadcrumb-item active" aria-current="page">Dataset</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="dataset"> +<h1>Dataset<a class="headerlink" href="#dataset" title="Permalink to this heading">#</a></h1> +<p>We provide several available datasets under <code class="docutils literal notranslate"><span class="pre">data</span></code>. You may download them all by running:</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>data<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>./download.sh<span class="w"> </span>all<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>- +</pre></div> +</div> +<p>You can replace <code class="docutils literal notranslate"><span class="pre">all</span></code> with a specific dataset name to only download that dataset (e.g. <code class="docutils literal notranslate"><span class="pre">./download.sh</span> <span class="pre">alpaca</span></code>).</p> +<p>Customized datasets are strongly encouraged, since this way users can apply +their own prompt engineering techniques over various source datasets. As long +as the generated dataset following the format below, they can be accepted as +the input of our pipelines :hugs:</p> +<section id="dataset-format-in-general"> +<h2>Dataset Format in General<a class="headerlink" href="#dataset-format-in-general" title="Permalink to this heading">#</a></h2> +<p>To specify the input for model finetune, users can provide a list of <code class="docutils literal notranslate"><span class="pre">.json</span></code> +files under a specified dataset directory. For example,</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="p">|</span>-<span class="w"> </span>path_to_dataset +<span class="w"> </span><span class="p">|</span>-<span class="w"> </span>data_1.json +<span class="w"> </span><span class="p">|</span>-<span class="w"> </span>data_2.json +<span class="w"> </span><span class="p">|</span>-<span class="w"> </span>another_data.json +<span class="w"> </span><span class="p">|</span>-<span class="w"> </span>... +</pre></div> +</div> +<p>For inference, we currently only support a single <code class="docutils literal notranslate"><span class="pre">.json</span></code> file.</p> +<p>Each json file shall have the following format (three instances with four keys +for example),</p> +<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span> +<span class="w"> </span><span class="nt">&quot;type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;TYPE&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;instances&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span> +<span class="w"> </span><span class="p">{</span> +<span class="w"> </span><span class="nt">&quot;KEY_1&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_1.1&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;KEY_2&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_1.2&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;KEY_3&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_1.3&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;KEY_4&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_1.4&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="p">},</span> +<span class="w"> </span><span class="p">{</span> +<span class="w"> </span><span class="nt">&quot;KEY_1&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_2.1&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;KEY_2&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_2.2&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;KEY_3&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_2.3&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;KEY_4&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_2.4&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="p">},</span> +<span class="w"> </span><span class="p">{</span> +<span class="w"> </span><span class="nt">&quot;KEY_1&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_3.1&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;KEY_2&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_3.2&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;KEY_3&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_3.3&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;KEY_4&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;VALUE_3.4&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="p">},</span> +<span class="w"> </span><span class="p">]</span> +<span class="p">}</span> +</pre></div> +</div> +<p>where the <code class="docutils literal notranslate"><span class="pre">TYPE</span></code> indicates the dataset type and defines the set of keys +<code class="docutils literal notranslate"><span class="pre">{</span> <span class="pre">KEY_1,</span> <span class="pre">KEY_2,</span> <span class="pre">...</span> <span class="pre">}</span></code> and their corresponding interpretations. The list of +supported types are listed as follows.</p> +</section> +<section id="supported-dataset-and-detailed-formats"> +<h2>Supported Dataset and Detailed Formats<a class="headerlink" href="#supported-dataset-and-detailed-formats" title="Permalink to this heading">#</a></h2> +<section id="textonly"> +<h3>TextOnly<a class="headerlink" href="#textonly" title="Permalink to this heading">#</a></h3> +<p>This is the most common dataset type, which only contains raw texts in each +sample. This type of dataset can be used as the training set for text decoder +models, or the input of decoder models / encoder-decoder models. Its format is +as follows (three instances for example),</p> +<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span> +<span class="w"> </span><span class="nt">&quot;type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;text_only&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;instances&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span> +<span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="nt">&quot;text&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;SAMPLE_TEXT_1&quot;</span><span class="w"> </span><span class="p">},</span> +<span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="nt">&quot;text&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;SAMPLE_TEXT_2&quot;</span><span class="w"> </span><span class="p">},</span> +<span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="nt">&quot;text&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;SAMPLE_TEXT_3&quot;</span><span class="w"> </span><span class="p">},</span> +<span class="w"> </span><span class="p">]</span> +<span class="p">}</span> +</pre></div> +</div> +<p>For example, <code class="docutils literal notranslate"><span class="pre">data/example_dataset/train/train_50.json</span></code> has the aboved format.</p> +</section> +<section id="text2text"> +<h3>Text2Text<a class="headerlink" href="#text2text" title="Permalink to this heading">#</a></h3> +<p>This is the dataset type mostly used for inferencing, which contains a pair of +texts in each sample. This type of dataset can be used as the training set for +text encoder-decoder models, or question-answer pair for evaluating model +inferences. Its format is as follows (three instances for example),</p> +<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span> +<span class="w"> </span><span class="nt">&quot;type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;text2text&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;instances&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span> +<span class="w"> </span><span class="p">{</span> +<span class="w"> </span><span class="nt">&quot;input&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;SAMPLE_INPUT_1&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;output&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;SAMPLE_OUTPUT_1&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="p">},</span> +<span class="w"> </span><span class="p">{</span> +<span class="w"> </span><span class="nt">&quot;input&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;SAMPLE_INPUT_2&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;output&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;SAMPLE_OUTPUT_2&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="p">},</span> +<span class="w"> </span><span class="p">{</span> +<span class="w"> </span><span class="nt">&quot;input&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;SAMPLE_INPUT_3&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="nt">&quot;output&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;SAMPLE_OUTPUT_3&quot;</span><span class="p">,</span> +<span class="w"> </span><span class="p">},</span> +<span class="w"> </span><span class="p">]</span> +<span class="p">}</span> +</pre></div> +</div> +<p>For example, <code class="docutils literal notranslate"><span class="pre">data/example_dataset/test/test_13.json</span></code> has the aboved format.</p> +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="index.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title">Examples</p> + </div> + </a> + <a class="right-next" + href="checkpoints.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title">Checkpoints</p> + </div> + <i class="fa-solid fa-angle-right"></i> + </a> +</div> + </footer> + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner"> + + <div 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class="bd-toc-item navbar-nav"><ul class="nav bd-sidenav"> +<li class="toctree-l1"><a class="reference internal" href="DATASETS.html">Dataset</a></li> +</ul> +<ul class="nav bd-sidenav"> +<li class="toctree-l1"><a class="reference internal" href="checkpoints.html">Checkpoints</a></li> +</ul> +<ul class="nav bd-sidenav"> +<li class="toctree-l1"><a class="reference internal" href="reward_modeling.html">Reward Modeling</a></li> +</ul> +<ul class="nav bd-sidenav"> +<li class="toctree-l1"><a class="reference internal" href="raft.html">1 Introduction</a></li> + + +</ul> +<ul class="current nav bd-sidenav"> +<li class="toctree-l1 current active"><a class="current reference internal" href="#">LMFlow Benchmark Guide</a></li> + + +</ul> +</div> +</nav></div> + </div> + + + <div class="sidebar-primary-items__end sidebar-primary__section"> + </div> + + <div id="rtd-footer-container"></div> + + + </div> + + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div 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settings in our repo, <code class="docutils literal notranslate"><span class="pre">NLL</span> <span class="pre">Task</span> <span class="pre">Setting</span></code> and <code class="docutils literal notranslate"><span class="pre">LM-Evaluation</span> <span class="pre">Task</span> <span class="pre">Setting</span></code>. Below are the details of them:</p> +</section> +<section id="nll-task-setting"> +<h1>1. NLL Task Setting<a class="headerlink" href="#nll-task-setting" title="Permalink to this heading">#</a></h1> +<p>Users can easily create new tasks and evaluate their datasets on +the provide <code class="docutils literal notranslate"><span class="pre">nll</span> <span class="pre">(Negative</span> <span class="pre">Log</span> <span class="pre">Likelihood)</span></code> metric.</p> +<section id="setup"> +<h2>Setup<a class="headerlink" href="#setup" title="Permalink to this heading">#</a></h2> +<p>Fork the main repo, clone it, and create a new branch with the name of +your task, and install the following:</p> +<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># After forking...</span> +git<span class="w"> </span>clone<span class="w"> </span>https://github.com/&lt;YOUR-USERNAME&gt;/LMFlow.git +<span class="nb">cd</span><span class="w"> </span>LMFlow +git<span class="w"> </span>checkout<span class="w"> </span>-b<span class="w"> </span>&lt;TASK-NAME&gt; +conda<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>lmflow<span class="w"> </span><span class="nv">python</span><span class="o">=</span><span class="m">3</span>.9<span class="w"> </span>-y +conda<span class="w"> </span>activate<span class="w"> </span>lmflow +conda<span class="w"> </span>install<span class="w"> </span>mpi4py +pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>. +</pre></div> +</div> +</section> +<section id="create-your-task-dataset-file"> +<h2>Create Your Task Dataset File<a class="headerlink" href="#create-your-task-dataset-file" title="Permalink to this heading">#</a></h2> +<p>We provide several available datasets under <code class="docutils literal notranslate"><span class="pre">data</span></code> after running</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>data<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>./download.sh<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>- +</pre></div> +</div> +<p>You can refer to some given evaluation dataset files and create your own. +Also, you may refer to our guide on +<a class="reference external" href="https://optimalscale.github.io/LMFlow/examples/DATASETS.html">DATASET</a>.</p> +<p>In this step, you will need to decide your answer type like <code class="docutils literal notranslate"><span class="pre">text2text</span></code> +or <code class="docutils literal notranslate"><span class="pre">text_only</span></code> (Notice that the current <code class="docutils literal notranslate"><span class="pre">nll</span></code> implementation only supports these +two answer types). We will note the chosen answer type as <code class="docutils literal notranslate"><span class="pre">&lt;ANSWER_TYPE&gt;</span></code>.</p> +<p>After preparing your own <code class="docutils literal notranslate"><span class="pre">DATASET</span></code> file, you can put it under <code class="docutils literal notranslate"><span class="pre">data</span></code> dir +and make a <code class="docutils literal notranslate"><span class="pre">TASK</span></code> dir.</p> +<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>mkdir<span class="w"> </span>&lt;TASK&gt; +mv<span class="w"> </span>&lt;DATASET&gt;<span class="w"> </span>&lt;TASK&gt; +</pre></div> +</div> +</section> +<section id="task-registration"> +<h2>Task Registration<a class="headerlink" href="#task-registration" title="Permalink to this heading">#</a></h2> +<p>Note the path of your dataset, <code class="docutils literal notranslate"><span class="pre">data/&lt;TASK&gt;/&lt;DATASET&gt;</span></code>.</p> +<p>Open the file <code class="docutils literal notranslate"><span class="pre">examples/benchmarking.py</span></code>, add your task’s info into +<code class="docutils literal notranslate"><span class="pre">LOCAL_DATSET_GROUP_MAP</span></code>, <code class="docutils literal notranslate"><span class="pre">LOCAL_DATSET_MAP</span></code>, <code class="docutils literal notranslate"><span class="pre">LOCAL_DATSET_ANSWERTYPE_MAP</span></code></p> +<p>In <code class="docutils literal notranslate"><span class="pre">LOCAL_DATSET_MAP</span></code>, you will need to specify your <code class="docutils literal notranslate"><span class="pre">DATASET</span></code> files’ path:</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">LOCAL_DATSET_MAP</span> <span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;...&quot;</span><span class="p">:</span><span class="s2">&quot;...&quot;</span><span class="p">,</span> + <span class="s2">&quot;&lt;TASK&gt;&quot;</span><span class="p">:</span><span class="s2">&quot;data/&lt;TASK&gt;/&lt;DATASET&gt;&quot;</span><span class="p">,</span> +<span class="p">}</span> +</pre></div> +</div> +<p>In <code class="docutils literal notranslate"><span class="pre">LOCAL_DATSET_ANSWERTYPE_MAP</span></code>, you will need to specify your task’s +<code class="docutils literal notranslate"><span class="pre">&lt;ANSWER_TYPE&gt;</span></code>:</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">LOCAL_DATSET_ANSWERTYPE_MAP</span> <span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;...&quot;</span><span class="p">:</span><span class="s2">&quot;...&quot;</span><span class="p">,</span> + <span class="s2">&quot;&lt;TASK&gt;&quot;</span><span class="p">:</span><span class="s2">&quot;&lt;ANSWER_TYPE&gt;,</span> +<span class="p">}</span> +</pre></div> +</div> +<p>If you only have one task, you can add key-value pair like <code class="docutils literal notranslate"><span class="pre">&quot;&lt;TASK&gt;&quot;:&quot;&lt;TASK&gt;&quot;</span></code> +in <code class="docutils literal notranslate"><span class="pre">LOCAL_DATSET_GROUP_MAP</span></code>:</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">LOCAL_DATSET_GROUP_MAP</span> <span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;...&quot;</span><span class="p">:</span><span class="s2">&quot;...&quot;</span><span class="p">,</span> + <span class="s2">&quot;&lt;TASK&gt;&quot;</span><span class="p">:</span><span class="s2">&quot;&lt;TASK&gt;&quot;</span><span class="p">,</span> +<span class="p">}</span> +</pre></div> +</div> +<p>If you want to combine several tasks, you may first specify a +combination name <code class="docutils literal notranslate"><span class="pre">&lt;TASK_COMBINATION&gt;</span></code> and add key-value pair like +<code class="docutils literal notranslate"><span class="pre">&quot;&lt;TASK_COMBINATION&gt;&quot;:&quot;&lt;TASK_1&gt;,&lt;TASK_2&gt;,..&quot;</span></code>in <code class="docutils literal notranslate"><span class="pre">LOCAL_DATSET_GROUP_MAP</span></code>.</p> +<p>Remember to separate TASK by <code class="docutils literal notranslate"><span class="pre">,</span></code>:</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">LOCAL_DATSET_GROUP_MAP</span> <span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;...&quot;</span><span class="p">:</span><span class="s2">&quot;...&quot;</span><span class="p">,</span> + <span class="s2">&quot;&lt;TASK_COMBINATION&gt;&quot;</span><span class="p">:</span><span class="s2">&quot;&lt;TASK_1&gt;,&lt;TASK_2&gt;,..&quot;</span><span class="p">,</span> +<span class="p">}</span> +</pre></div> +</div> +<p>After finishing changing these items, you can run your own <code class="docutils literal notranslate"><span class="pre">&lt;TASK&gt;</span></code> like:</p> +<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>deepspeed<span class="w"> </span>examples/benchmarking.py<span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--answer_type<span class="w"> </span>&lt;ANSWER_TYPE&gt;<span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--use_ram_optimized_load<span class="w"> </span>False<span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--model_name_or_path<span class="w"> </span><span class="si">${</span><span class="nv">model_name</span><span class="si">}</span><span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--dataset_name<span class="w"> </span>data/&lt;TASK&gt;/&lt;DATASET&gt;<span class="se">\</span> +<span class="w"> </span>--deepspeed<span class="w"> </span>examples/ds_config.json<span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--metric<span class="w"> </span>nll<span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--prompt_structure<span class="w"> </span><span class="s2">&quot;###Human: {input}###Assistant:&quot;</span><span class="w"> </span><span class="se">\</span> +<span class="w"> </span><span class="p">|</span><span class="w"> </span>tee<span class="w"> </span><span class="si">${</span><span class="nv">log_dir</span><span class="si">}</span>/train.log<span class="w"> </span><span class="se">\</span> +<span class="w"> </span><span class="m">2</span>&gt;<span class="w"> </span><span class="si">${</span><span class="nv">log_dir</span><span class="si">}</span>/train.err<span class="w"> </span> +</pre></div> +</div> +</section> +</section> +<section id="lm-evaluation-task-setting"> +<h1>2. LM-Evaluation Task Setting<a class="headerlink" href="#lm-evaluation-task-setting" title="Permalink to this heading">#</a></h1> +<p>We integrate <a class="reference external" href="https://github.com/EleutherAI/lm-evaluation-harness">EleutherAI/lm-evaluation-harness</a> into +<code class="docutils literal notranslate"><span class="pre">benchamrk.py</span></code> by directly executing the evaluate commands. Users +can also use their evaluation by simply changing two items in +<code class="docutils literal notranslate"><span class="pre">&lt;LM_EVAL_DATASET_MAP&gt;</span></code> of <code class="docutils literal notranslate"><span class="pre">examples/benchmarking.py</span></code>.</p> +<p>Please refer to Eleuther’s +<a class="reference external" href="https://github.com/EleutherAI/lm-evaluation-harness/blob/master/docs/task_table.md">task-table</a> +to get exact <code class="docutils literal notranslate"><span class="pre">&lt;TASK&gt;</span></code> name.</p> +<p>Similarly, you can combine several tasks, you may first specify a +combination name <code class="docutils literal notranslate"><span class="pre">&lt;TASK_COMBINATION&gt;</span></code> and add key-value pair like +<code class="docutils literal notranslate"><span class="pre">&quot;&lt;TASK_COMBINATION&gt;&quot;:&quot;&lt;TASK_1&gt;,&lt;TASK_2&gt;,..&quot;</span></code>in <code class="docutils literal notranslate"><span class="pre">LM_EVAL_DATASET_MAP</span></code>.</p> +<p>Also, remember to separate TASK by <code class="docutils literal notranslate"><span class="pre">,</span></code>:</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">LM_EVAL_DATASET_MAP</span> <span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;...&quot;</span><span class="p">:</span><span class="s2">&quot;...&quot;</span><span class="p">,</span> + <span class="s2">&quot;&lt;TASK_COMBINATION&gt;&quot;</span><span class="p">:</span><span class="s2">&quot;&lt;TASK_1&gt;,&lt;TASK_2&gt;,..&quot;</span><span class="p">,</span> +<span class="p">}</span> +</pre></div> +</div> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="raft.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title">1 Introduction</p> + </div> + </a> + <a class="right-next" + href="../autoapi/index.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title">API Reference</p> + </div> + <i class="fa-solid fa-angle-right"></i> + </a> +</div> + </footer> + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner"> + + <div class="sidebar-secondary-item"> + <div class="page-toc tocsection onthispage"> + <i class="fa-solid fa-list"></i> On this page + </div> + <nav class="bd-toc-nav page-toc"> + <ul class="visible nav section-nav flex-column"> +<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">LMFlow Benchmark Guide</a></li> +<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#nll-task-setting">1. 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title="Permalink to this heading">#</a></h1> +<p>In general, you can directly load from checkpoints by using <code class="docutils literal notranslate"><span class="pre">--model_name_or_path</span></code>. However, the LLaMA case is slightly different due to the copyright issue.</p> +<section id="llama-checkpoint"> +<h2>LLaMA Checkpoint<a class="headerlink" href="#llama-checkpoint" title="Permalink to this heading">#</a></h2> +<ol class="arabic"> +<li><p>First, you need to get the access of LLaMA model from <a class="reference external" href="https://github.com/facebookresearch/llama">facebookresearch/llama</a>. Download the official checkpoints and save them into <code class="docutils literal notranslate"><span class="pre">${llama-path}</span></code>.</p></li> +<li><p>Second, convert the official checkpoints <code class="docutils literal notranslate"><span class="pre">${llama-path}</span></code> to HuggingFace supported checkpoints <code class="docutils literal notranslate"><span class="pre">${llama-hf-path}</span></code> by running</p> +<p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">./scripts/convert_llama_weights_to_hf.py</span> <span class="pre">--input_dir</span> <span class="pre">${llama-path}</span> <span class="pre">--model_size</span> <span class="pre">7B</span> <span class="pre">--output_dir</span> <span class="pre">${llama-hf-path}/llama-7b-hf</span></code></p> +</li> +<li><p>Then you are good to go by setting the checkpoint path to <code class="docutils literal notranslate"><span class="pre">${llama-hf-path}/llama-7b-hf</span></code>. Enjoy it!</p></li> +<li><p>(optional) Now you have the original llama-7b-hf pretrained model. With</p></li> +</ol> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>output_models<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>./download.sh<span class="w"> </span>all<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>- +</pre></div> +</div> +<p>You can obtain the model difference finetuned by ours. By a way similar to <code class="docutils literal notranslate"><span class="pre">./scripts/run_evaluation_with_lora.sh</span></code>,</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nv">CUDA_VISIBLE_DEVICES</span><span class="o">=</span><span class="m">0</span><span class="w"> </span><span class="se">\</span> +<span class="w"> </span>deepspeed<span class="w"> </span>examples/evaluate.py<span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--answer_type<span class="w"> </span>text<span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--model_name_or_path<span class="w"> </span><span class="si">${</span><span class="nv">llama</span><span class="p">-hf-path</span><span class="si">}</span>/llama-7b-hf<span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--lora_model_path<span class="w"> </span>output_models/<span class="si">${</span><span class="nv">llama</span><span class="p">-model-diff-path</span><span class="si">}</span><span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--dataset_path<span class="w"> </span>data/alpaca/test<span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--prompt_structure<span class="w"> </span><span class="s2">&quot;Input: {input}&quot;</span><span class="w"> </span><span class="se">\</span> +<span class="w"> </span>--deepspeed<span class="w"> </span>examples/ds_config.json +</pre></div> +</div> +<p>You can now evaluate with the finetuned llama model.</p> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="DATASETS.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title">Dataset</p> + </div> + </a> + <a class="right-next" + 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+<li class="toctree-l2"><a class="reference internal" href="DATASETS.html#dataset-format-in-general">Dataset Format in General</a></li> +<li class="toctree-l2"><a class="reference internal" href="DATASETS.html#supported-dataset-and-detailed-formats">Supported Dataset and Detailed Formats</a><ul> +<li class="toctree-l3"><a class="reference internal" href="DATASETS.html#textonly">TextOnly</a></li> +<li class="toctree-l3"><a class="reference internal" href="DATASETS.html#text2text">Text2Text</a></li> +</ul> +</li> +</ul> +</li> +</ul> +</div> +<div class="toctree-wrapper compound"> +<ul> +<li class="toctree-l1"><a class="reference internal" href="checkpoints.html">Checkpoints</a><ul> +<li class="toctree-l2"><a class="reference internal" href="checkpoints.html#llama-checkpoint">LLaMA Checkpoint</a></li> +</ul> +</li> +</ul> +</div> +</section> +<section id="finetuning"> +<h2>Finetuning<a class="headerlink" href="#finetuning" title="Permalink to this heading">#</a></h2> +<p>For SFT, Refer to <a class="reference external" href="https://github.com/OptimalScale/LMFlow/blob/main/examples">examples</a>.</p> +<p>For alignment process,</p> +<div class="toctree-wrapper compound"> +<ul> +<li class="toctree-l1"><a class="reference internal" href="reward_modeling.html">Reward Modeling</a><ul> +<li class="toctree-l2"><a class="reference internal" href="reward_modeling.html#introduction">Introduction</a></li> +<li class="toctree-l2"><a class="reference internal" href="reward_modeling.html#step-1-supervised-finetuning-sft">Step 1 Supervised Finetuning (SFT)</a></li> +<li class="toctree-l2"><a class="reference internal" href="reward_modeling.html#step-2-reward-modeling">Step 2 Reward Modeling</a></li> +<li class="toctree-l2"><a class="reference internal" href="reward_modeling.html#examples">Examples</a></li> +</ul> +</li> +</ul> +</div> +<div class="toctree-wrapper compound"> +<ul> +<li class="toctree-l1"><a class="reference internal" href="raft.html">1 Introduction</a><ul> +<li class="toctree-l2"><a class="reference internal" href="raft.html#dataset-description">1.1 Dataset description</a></li> +</ul> +</li> +<li class="toctree-l1"><a class="reference internal" href="raft.html#reward-modeling">2 Reward Modeling</a><ul> +<li class="toctree-l2"><a class="reference internal" href="raft.html#supervised-finetuning-sft">2.1 Supervised Finetuning (SFT)</a></li> +<li class="toctree-l2"><a class="reference internal" href="raft.html#id1">2.2 Reward Modeling</a></li> +<li class="toctree-l2"><a class="reference internal" href="raft.html#lora-merge-and-get-reward-model">2.3 LoRA Merge and Get Reward Model</a></li> +</ul> +</li> +<li class="toctree-l1"><a class="reference internal" href="raft.html#raft-alignment">3 RAFT Alignment</a><ul> +<li class="toctree-l2"><a class="reference internal" href="raft.html#algorithms-overview">3.1 Algorithms Overview</a></li> +<li class="toctree-l2"><a class="reference internal" href="raft.html#hyper-parameters">3.2 Hyper-parameters</a></li> +<li class="toctree-l2"><a class="reference internal" href="raft.html#examples">3.3 Examples</a><ul> +<li class="toctree-l3"><a class="reference internal" href="raft.html#sft">3.3.1 SFT</a></li> +<li class="toctree-l3"><a class="reference internal" href="raft.html#id2">3.3.2 RAFT Alignment</a></li> +<li class="toctree-l3"><a class="reference internal" href="raft.html#end-note">3.3.3 End Note</a></li> +</ul> +</li> +</ul> +</li> +</ul> +</div> +</section> +<section id="inference"> +<h2>Inference<a class="headerlink" href="#inference" title="Permalink to this heading">#</a></h2> +<p>Refer to <a class="reference external" href="https://github.com/OptimalScale/LMFlow/blob/main/examples">examples</a>.</p> +</section> +<section id="evaluation"> +<h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading">#</a></h2> +<div class="toctree-wrapper compound"> +<ul> +<li class="toctree-l1"><a class="reference internal" 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aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + <li class="breadcrumb-item active" aria-current="page">Finetune</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="finetune"> +<h1>Finetune<a class="headerlink" href="#finetune" title="Permalink to this heading">#</a></h1> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sys</span> + +<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">HfArgumentParser</span> + +<span class="kn">from</span> <span class="nn">lmflow.args</span> <span class="kn">import</span> <span class="p">(</span> + <span class="n">ModelArguments</span><span class="p">,</span> + <span class="n">DatasetArguments</span><span class="p">,</span> + <span class="n">AutoArguments</span><span class="p">,</span> +<span class="p">)</span> + +<span class="kn">from</span> <span class="nn">lmflow.datasets.dataset</span> <span class="kn">import</span> <span class="n">Dataset</span> +<span class="kn">from</span> <span class="nn">lmflow.models.tunable_models</span> <span class="kn">import</span> <span class="n">TunableModel</span> +<span class="kn">from</span> <span class="nn">lmflow.pipeline.auto_pipeline</span> <span class="kn">import</span> <span class="n">AutoPipeline</span> + + +<span class="k">def</span> <span class="nf">main</span><span class="p">():</span> + <span class="c1"># Parses arguments</span> + <span class="n">pipeline_name</span> <span class="o">=</span> <span class="s2">&quot;finetuner&quot;</span> + <span class="n">PipelineArguments</span> <span class="o">=</span> <span class="n">AutoArguments</span><span class="o">.</span><span class="n">get_pipeline_args_class</span><span class="p">(</span><span class="n">pipeline_name</span><span class="p">)</span> + + <span class="n">parser</span> <span class="o">=</span> <span class="n">HfArgumentParser</span><span class="p">((</span><span class="n">ModelArguments</span><span class="p">,</span> <span class="n">DatasetArguments</span><span class="p">,</span> <span class="n">PipelineArguments</span><span class="p">))</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s2">&quot;.json&quot;</span><span class="p">):</span> + <span class="c1"># If we pass only one argument to the script and it&#39;s the path to a json file,</span> + <span class="c1"># let&#39;s parse it to get our arguments.</span> + <span class="n">model_args</span><span class="p">,</span> <span class="n">data_args</span><span class="p">,</span> <span class="n">pipeline_args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_json_file</span><span class="p">(</span><span class="n">json_file</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">model_args</span><span class="p">,</span> <span class="n">data_args</span><span class="p">,</span> <span class="n">pipeline_args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args_into_dataclasses</span><span class="p">()</span> + + <span class="c1"># TODO: deepspeed config initialization</span> + + <span class="c1"># Initialization</span> + <span class="n">finetuner</span> <span class="o">=</span> <span class="n">AutoPipeline</span><span class="o">.</span><span class="n">get_pipeline</span><span class="p">(</span> + <span class="n">pipeline_name</span><span class="o">=</span><span class="n">pipeline_name</span><span class="p">,</span> + <span class="n">model_args</span><span class="o">=</span><span class="n">model_args</span><span class="p">,</span> + <span class="n">data_args</span><span class="o">=</span><span class="n">data_args</span><span class="p">,</span> + <span class="n">pipeline_args</span><span class="o">=</span><span class="n">pipeline_args</span><span class="p">,</span> + <span class="p">)</span> + <span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">data_args</span><span class="p">)</span> + <span class="n">model</span> <span class="o">=</span> <span class="n">TunableModel</span><span class="p">(</span><span class="n">model_args</span><span class="p">)</span> + + <span class="c1"># Tokenization and text grouping must be done in the main process</span> + <span class="k">with</span> <span class="n">pipeline_args</span><span class="o">.</span><span class="n">main_process_first</span><span class="p">(</span><span class="n">desc</span><span class="o">=</span><span class="s2">&quot;dataset map tokenization&quot;</span><span class="p">):</span> + <span class="n">tokenized_dataset</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span> + <span class="n">lm_dataset</span> <span class="o">=</span> <span class="n">finetuner</span><span class="o">.</span><span class="n">group_text</span><span class="p">(</span> + <span class="n">tokenized_dataset</span><span class="p">,</span> + <span class="n">model_max_length</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">get_max_length</span><span class="p">(),</span> + <span class="p">)</span> + + <span class="c1"># Finetuning</span> + <span class="n">tuned_model</span> <span class="o">=</span> <span class="n">finetuner</span><span class="o">.</span><span class="n">tune</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> <span class="n">lm_dataset</span><span class="o">=</span><span class="n">lm_dataset</span><span class="p">)</span> + +</pre></div> +</div> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items 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+ + </div> + + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"> +<div class="header-article-items header-article__inner"> + + <div class="header-article-items__start"> + + <div class="header-article-item"> + + + +<nav aria-label="Breadcrumbs"> + <ul class="bd-breadcrumbs" role="navigation" aria-label="Breadcrumb"> + + <li class="breadcrumb-item breadcrumb-home"> + <a href="../index.html" class="nav-link" aria-label="Home"> + <i class="fa-solid fa-home"></i> + </a> + </li> + + <li class="breadcrumb-item"><a href="index.html" class="nav-link">Examples</a></li> + + <li class="breadcrumb-item active" aria-current="page">1 Introduction</li> + </ul> +</nav> +</div> + + </div> + + +</div> +</div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <section id="introduction"> +<h1>1 Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">#</a></h1> +<p>We remark that the example is built on LLaMA whose <a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform">licensed</a> is for non-commercial use only.</p> +<p>Reinforcement Learning from Human Feedback (RLHF) requires a reward function to guide the adjustment of the generative model. In this example, we show how to use LMFlow framework to train a reward model following the procedure in the InstructGPT paper: https://arxiv.org/abs/2203.02155 and then align the model via the RAFT algorithm (Reward rAnked FineTuning).</p> +<p><strong>This example contains both reward modeling and RAFT alignment for completeness. For users’ convenience, we have already provided a reward model based on GPT-Neo-2.7B in huggingface repo so one can skip the reward modeling first.</strong></p> +<section id="dataset-description"> +<h2>1.1 Dataset description<a class="headerlink" href="#dataset-description" title="Permalink to this heading">#</a></h2> +<p>We use the <em>Dahoas/full-hh-rlhf</em> dataset as an example, where each sample of this dataset consists of a prompt and two responses from the assistant. In particular, the response with label “chosen” is preferred as compared to the response with label “rejected”. The dataset consists of 112K training samples and 12.5K test samples. The following is an example sample of the dataset:</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot; Human: What kind of noises did dinosaurs make? Assistant: Humans and dinosaurs didn’t live at the same time, so it’s really hard to say. The best place to find out what noises dinosaurs made would be Human: yes they did Assistant: to guess, and that would probably require lots of reading and a certain amount of imagination, so we’re not really prepared to do that. Human: you cant read Assistant: </span> + +<span class="s2">Chosen response: &quot;</span>You<span class="w"> </span>can<span class="w"> </span>read?<span class="s2">&quot;</span> + +<span class="s2">Rejected response: &quot;</span>there’s<span class="w"> </span>a<span class="w"> </span>lot<span class="w"> </span>of<span class="w"> </span>stuff<span class="w"> </span>humans<span class="w"> </span>don’t<span class="w"> </span>know<span class="s2">&quot;</span> +</pre></div> +</div> +<p>To facilitate the training, we reformulate the prompt by adding ``###’’ at the beginning of the characters so that the model knows to reply. The new sample will be of the form:</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="s2">&quot;###Human: What kind of noises did dinosaurs make? ###Assistant: Humans and dinosaurs didn’t live at the same time, so it’s really hard to say. The best place to find out what noises dinosaurs made would be ###Human: yes they did ###Assistant: to guess, and that would probably require lots of reading and a certain amount of imagination, so we’re not really prepared to do that. ###Human: you cant read ###Assistant: </span> + +<span class="s2">Chosen response: &quot;</span>You<span class="w"> </span>can<span class="w"> </span>read?<span class="s2">&quot;</span> + +<span class="s2">Rejected response: &quot;</span>there’s<span class="w"> </span>a<span class="w"> </span>lot<span class="w"> </span>of<span class="w"> </span>stuff<span class="w"> </span>humans<span class="w"> </span>don’t<span class="w"> </span>know<span class="s2">&quot;</span> +</pre></div> +</div> +<p>We prepare all the used dataset in the directory ./data/hh_rlhf, which can be obtained by running the following command at LMFlow</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>data<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>./download.sh<span class="w"> </span>hh_rlhf<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nb">cd</span><span class="w"> </span>- +</pre></div> +</div> +</section> +</section> +<section id="reward-modeling"> +<h1>2 Reward Modeling<a class="headerlink" href="#reward-modeling" title="Permalink to this heading">#</a></h1> +<p>We follow the the procedure in the InstructGPT paper: https://arxiv.org/abs/2203.02155 to train a reward model using the HH-RLHF dataset first, which includes</p> +<ul class="simple"> +<li><p>Supervised Finetuning (SFT);</p></li> +<li><p>Reward modeling by comparison dataset.</p></li> +</ul> +<section id="supervised-finetuning-sft"> +<h2>2.1 Supervised Finetuning (SFT)<a class="headerlink" href="#supervised-finetuning-sft" title="Permalink to this heading">#</a></h2> +<p>Here is an example of dataset <em>/home/xiongwei/LMFlow/data/hh_rlhf/sft/hh_rlhf_sft.json</em>. We use only the preferred responses so we get 112K training samples.</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="o">{</span><span class="s2">&quot;type&quot;</span>:<span class="w"> </span><span class="s2">&quot;text_only&quot;</span>,<span class="w"> </span><span class="s2">&quot;instances&quot;</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">&quot;text&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: Should you buy a case to protect your cell phone?###Assistant: It depends on your circumstances. If you carry your phone in a pocket or a purse then you probably want a case. But if you only need a phone for quick interactions, a case may actually cause more harm than good. What do you need the phone for? Are you a parent, or do you work from home?###Human: What harm could it do?###Assistant: A phone case can damage the screen, for one thing. It can also get you in trouble if you have your phone turned off for some reason. Then you will turn it back on and it won’t do anything. If you can afford to replace it, then you need a case to protect it. The problem is that most people aren’t able to afford to replace their phones all the time.###Human: Thanks for letting me know.###Assistant: You’re welcome.&quot;</span><span class="o">}</span>,<span class="w"> </span><span class="o">{</span><span class="s2">&quot;text&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: I&#39;m trying to learn about the salam witch trials###Assistant: If you’re looking for books about witchcraft trials, I can recommend some books for you. But you probably mean you’re looking for more historical information about the Salem witch trials in 1692, and specifically about the salam witch trials in 1692?###Human: What are some good books to learn about the salam witch trials###Assistant: What would you like to learn? If you’re interested in history, one of the best books is The Witch Persecutions in Colonial America: A History. If you’re interested in witchcraft as a cultural phenomenon, you might enjoy two excellent books: Religion and the Decline of Magic: Studies in Popular Beliefs in Sixteenth- and Seventeenth-Century England by Keith Thomas and Magic, Witchcraft, and the Otherworld: An Anthropology of Superstition by Jack Goody. If you’re interested in history specifically as it relates to religion, you might enjoy The Popish Plot, or Prelates&#39; Plot: A History of the Popish Plot in England, by K. J. Everett.&quot;</span><span class="o">}]}</span> +</pre></div> +</div> +<p>You can edit <em>/scripts/run_finetune.sh</em> and modify the parameters. We present an example with GPT-Neo-2.7B here.</p> +<ul class="simple"> +<li><p>–model_name_or_path: EleutherAI/gpt-neo-2.7B</p></li> +<li><p>–dataset_path: ${project_dir}/data/hh_rlhf/sft</p></li> +<li><p>–output_dir: the path you want to store the sft model</p></li> +<li><p>–num_train_epochs: 1</p></li> +<li><p>–learning_rate: 2e-5</p></li> +<li><p>–per_device_train_batch_size: adjust according to your GPU memory source.</p></li> +</ul> +<p>Then, we can run the following command to perform SFT.</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>./scripts/run_finetune.sh +</pre></div> +</div> +<p>You can also use lora training by the following command but also need to set the model_name_or_path and dataset appropriately by editing run_finetune_with_lora.sh .</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>./scripts/run_finetune_with_lora.sh +</pre></div> +</div> +</section> +<section id="id1"> +<h2>2.2 Reward Modeling<a class="headerlink" href="#id1" title="Permalink to this heading">#</a></h2> +<p><strong>Data Preparation</strong></p> +<p>Reward modeling involves ranking responses based on the given prompt, typically requiring human labeling. Fortunately, we already have two responses for each prompt in this example, with one preferred over the other. To create a new dataset, we modify the original dataset to include two keys, “positive” and “negative”, with the former being the preferred response.</p> +<p>Here is an example of the dataset <em>LMFlow/data/hh_rlhf/rm/hh_rlhf_rm_training.json</em>, consisting of two samples.</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="o">{</span><span class="s2">&quot;instances&quot;</span>:<span class="w"> </span><span class="o">[</span> +<span class="w"> </span><span class="o">{</span><span class="s2">&quot;positive&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: Are space heaters safe?###Assistant: What do you mean? Can you give me more details?###Human: I mean, can they be a fire hazard?###Assistant: I don’t know about that. Can you tell me more about the situation? Are you using the heater on a fireplace? Do you have any other heater in the room? What materials are in the room? Do you feel any heat from the heater? How are you touching the heater?###Human: Can you just tell me if there are any situations where they could be a fire hazard?###Assistant: The answer is “it depends”. It would depend on the specific heater you are using, how you are using it, what kind of fuel it uses, what materials you have in the room, and what the surroundings are like. You’d also have to make sure you’re using it correctly. Is it switched off? Is it safe to touch? It’s not the sort of thing I could just answer by looking at the text you provided.###Human: Thank you. That information is helpful.###Assistant: Glad to be helpful. I’m always happy to assist.&quot;</span>,<span class="w"> </span><span class="s2">&quot;negative&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: Are space heaters safe?###Assistant: What do you mean? Can you give me more details?###Human: I mean, can they be a fire hazard?###Assistant: I don’t know about that. Can you tell me more about the situation? Are you using the heater on a fireplace? Do you have any other heater in the room? What materials are in the room? Do you feel any heat from the heater? How are you touching the heater?###Human: Can you just tell me if there are any situations where they could be a fire hazard?###Assistant: The answer is “it depends”. It would depend on the specific heater you are using, how you are using it, what kind of fuel it uses, what materials you have in the room, and what the surroundings are like. You’d also have to make sure you’re using it correctly. Is it switched off? Is it safe to touch? It’s not the sort of thing I could just answer by looking at the text you provided.###Human: Thank you. That information is helpful.###Assistant: You’re welcome. I’m glad to help. Is there anything else you want to know?&quot;</span><span class="o">}</span>,<span class="w"> </span> +<span class="w"> </span><span class="o">{</span><span class="s2">&quot;positive&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: how do i teach my kids to make forts at home?###Assistant: Do you mean you want to teach them to build a simple structure they can hide in? Or something more elaborate? I don’t know exactly what you mean by a “fort”, but maybe this gives you some ideas?&quot;</span>,<span class="w"> </span><span class="s2">&quot;negative&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: how do i teach my kids to make forts at home?###Assistant: There are many ways to make forts! The simplest might be to just pile up all the furniture in one room. This works if you have a single room in your house, although it might still be a bit challenging to build a taller and sturdier fort this way. The best way to build forts is to start with something easy, and then have fun exploring how you can improve it.&quot;</span><span class="o">}</span> +<span class="o">]}</span> +</pre></div> +</div> +<p><strong>Training</strong></p> +<p>To start from a model from a previous sft step, you may edit the <em>./scripts/run_reward_modeling.sh</em> script and update the “dataset_path” to use the desired dataset. By default, we will use LoRA training in reward modeling as we found that it is superior in terms of optimization and gives comparable performance.</p> +<ul class="simple"> +<li><p>–model_name_or_path: /root/data/usr_name/output_models/hh_rlhf_rm_sft_gptneo_2_7B/checkpoint-1659</p></li> +<li><p>–dataset_path: ${project_dir}/data/hh_rlhf/rm/hh_rlhf_rm_training.json</p></li> +<li><p>–output_dir: the path you want to store the reward model</p></li> +<li><p>–num_train_epochs: 1</p></li> +<li><p>–learning_rate: 3e-5</p></li> +<li><p>–per_device_train_batch_size: adjust according to your GPU memory source.</p></li> +<li><p>–eval_steps: 400</p></li> +<li><p>–validation_split_percentage: 10</p></li> +</ul> +<p>The load_dataset function splits the dataset into training and evaluation sets, which can also be customized by editing the function in /examples/run_reward_modeling.py if you want to prepare your own dataset when running the script. In the default implementation, it use <strong>validation_split_percentage</strong> samples as the evaluation dataset.</p> +<p>The reward modeling script can be used by</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>./scripts/run_reward_modeling.sh +</pre></div> +</div> +<p><strong>Examples</strong></p> +<p>We train reward models using the hh-rlhf dataset with three models, LLaMA-7B, GPT-NEO-2.7B, and GPT-NEO-1.3B. The model is first supervised fine-tuned with the training dataset in last step. The reward modeling is trained using the 112K training samples and 12.5 test samples.</p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-center"><p>Model</p></th> +<th class="head text-center"><p>Eval Accuracy</p></th> +<th class="head text-center"><p>Remarks</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-center"><p>LLaMA-7B</p></td> +<td class="text-center"><p>79.52%</p></td> +<td class="text-center"><p>-</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>LLaMA-7B</p></td> +<td class="text-center"><p>71.64%</p></td> +<td class="text-center"><p>RM from LLaMA without SFT</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>GPT-NEO-2.7B</p></td> +<td class="text-center"><p>69.24%</p></td> +<td class="text-center"><p>-</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>GPT-NEO-1.3B</p></td> +<td class="text-center"><p>65.58%</p></td> +<td class="text-center"><p>Only trained on 10000 samples</p></td> +</tr> +</tbody> +</table> +</section> +<section id="lora-merge-and-get-reward-model"> +<h2>2.3 LoRA Merge and Get Reward Model<a class="headerlink" href="#lora-merge-and-get-reward-model" title="Permalink to this heading">#</a></h2> +<p>We use <em>./examples/merge_lora.py</em> to merge the LoRA adapter with the sft rm model. We are ready to align our model.</p> +</section> +</section> +<section id="raft-alignment"> +<h1>3 RAFT Alignment<a class="headerlink" href="#raft-alignment" title="Permalink to this heading">#</a></h1> +<p>Original paper: <a class="reference external" href="https://arxiv.org/pdf/2304.06767.pdf">RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment</a></p> +<section id="algorithms-overview"> +<h2>3.1 Algorithms Overview<a class="headerlink" href="#algorithms-overview" title="Permalink to this heading">#</a></h2> +<p><strong>Main ideas of RAFT</strong></p> +<p><img alt="" src="../_images/raft_idea.PNG" /></p> +<p>Clearly the global ranking strategy is more efficient in terms of the reward learning. However, in some cases (e.g. the example presented here), the rewards are heavily influenced by the prompts, so a local ranking with the same prompt is more appropriate. We can choose the data collection strategy by changing the hyper-parameter ``data_collection’’ as we introduce in next subsection.</p> +</section> +<section id="hyper-parameters"> +<h2>3.2 Hyper-parameters<a class="headerlink" href="#hyper-parameters" title="Permalink to this heading">#</a></h2> +<p>Table 1: Hyper-parameters of RAFT.</p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head"><p>Parameters in script</p></th> +<th class="head"><p>Default Choice</p></th> +<th class="head"><p>Description</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td><p>model_name_or_path</p></td> +<td><p><em>str</em>, default to gpt2</p></td> +<td><p>the model you want to align, either a model repo on huggingface.co or path to a directory containing your local model.</p></td> +</tr> +<tr class="row-odd"><td><p>raft_batch_size</p></td> +<td><p><em>int</em>, default to 1024</p></td> +<td><p>the number of samples used for supervised fine-tuning at each raft iteration.</p></td> +</tr> +<tr class="row-even"><td><p>top_reward_percentage</p></td> +<td><p><em>int</em>, default to 0.2</p></td> +<td><p>raft will generate batch_size / top_reward_percentage samples and use the top top_reward_percentage samples to fine-tune the model. There are two data ranking strategies and please see <strong>Data Collection and Reward Ranking</strong> at algorithm overview section for details.</p></td> +</tr> +<tr class="row-odd"><td><p>num_raft_iteration</p></td> +<td><p><em>int</em>, default to 20</p></td> +<td><p>the number of raft iterations.</p></td> +</tr> +<tr class="row-even"><td><p>learning_rate</p></td> +<td><p><em>float</em>, default to 2e-5</p></td> +<td><p>the learning rate used to fine-tune the model.</p></td> +</tr> +<tr class="row-odd"><td><p>num_train_epochs</p></td> +<td><p><em>int</em>, default to 4</p></td> +<td><p>the epochs we train the model on the collected dataset for each raft iteration.</p></td> +</tr> +<tr class="row-even"><td><p>per_device_train_batch_size</p></td> +<td><p><em>int</em>, default to 1</p></td> +<td><p>the per-gpu batch size for the supervised fine-tuning.</p></td> +</tr> +<tr class="row-odd"><td><p>inference_batch_size_per_device</p></td> +<td><p><em>int</em>, default to 1</p></td> +<td><p>the inference batch size for data collection. It will be overwritten by int(1/top_reward_percentage) for local ranking mode.</p></td> +</tr> +<tr class="row-even"><td><p>collection_strategy</p></td> +<td><p><em>str</em>, default to “local”</p></td> +<td><p>Either “local” or “top”. See <strong>Data Collection and Reward Ranking</strong> in last section for details.</p></td> +</tr> +</tbody> +</table> +</section> +<section id="examples"> +<h2>3.3 Examples<a class="headerlink" href="#examples" title="Permalink to this heading">#</a></h2> +<p>As an example, we align the LLaMA-7B model with the RAFT in this subsection.</p> +<section id="sft"> +<h3>3.3.1 SFT<a class="headerlink" href="#sft" title="Permalink to this heading">#</a></h3> +<p>We also first fine-tune the base model on the HH-RLHF dataset. We only use a different –model_name_or_path to use LLaMA model. We note that LLaMA with <a class="reference external" href="https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform">licensed</a> is for non-commercial use only. We refer readers to https://optimalscale.github.io/LMFlow/examples/checkpoints.html for more details to get the LLaMA-7B model.</p> +</section> +<section id="id2"> +<h3>3.3.2 RAFT Alignment<a class="headerlink" href="#id2" title="Permalink to this heading">#</a></h3> +<p>We align the LLaMA-7B-SFT model in this subsection. Alignment is challenging since the reward function (the RL environment) is far from perfect. Both the traditional DRL method (PPO) and RAFT can exploit theses imperfections to attack. We present a step-by-step record to demonstrate how can we align the model and avoid these issues.</p> +<p><strong>Data Preparation</strong></p> +<p>We observe that a long context window will lead to a heavy burden on the GPU memory source. Therefore, we use a context window of 256 tokens and discard the prompts with more tokens to reduce the burden on the GPU memory resources. This results in a prompt set of 82147 samples (originally 112K). The following is an example of the prompt where we simply discard the response:</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="w"> </span><span class="s2">&quot;###Human: Should you buy a case to protect your cell phone?###Assistant: It depends on your circumstances. If you carry your phone in a pocket or a purse then you probably want a case. But if you only need a phone for quick interactions, a case may actually cause more harm than good. What do you need the phone for? Are you a parent, or do you work from home?###Human: What harm could it do?###Assistant: A phone case can damage the screen, for one thing. It can also get you in trouble if you have your phone turned off for some reason. Then you will turn it back on and it won’t do anything. If you can afford to replace it, then you need a case to protect it. The problem is that most people aren’t able to afford to replace their phones all the time.###Human: Thanks for letting me know.###Assistant:&quot;</span> +</pre></div> +</div> +<p>We additionally use 2K samples from the test set to test the performance of models. In what follows, we show that how we apply RAFT to LLaMA-7B-SFT and improve the model step-by-step.</p> +<p><strong>Step 1: test the sft-model</strong></p> +<p>We first evaluate the performance of the LLaMA-7B-SFT model on the hand-out test set and observe that the model tends to reply the prompt with multiple rounds of conversations. Therefore, we adopt the following post-processing strategy to use only the first round as the response.</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">_clean_text</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span> + <span class="n">stext</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">text</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;###Human&quot;</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span><span class="p">]</span> + <span class="k">return</span> <span class="n">stext</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s2">&quot;#&quot;</span><span class="p">)</span> +</pre></div> +</div> +<p><strong>Step 2: train model</strong></p> +<p><strong>Reward function setting</strong></p> +<p>The reward model is specified at the /LMFlow/examples/raft_align.py to set up the reward model we want to use. In our case, we will use the GPT-Neo-2.7B-rm trained in the last step, which is set as follows:</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">reward_model_or_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span> + <span class="n">default</span><span class="o">=</span><span class="s2">&quot;weqweasdas/hh_rlhf_rm&quot;</span><span class="p">,</span> + <span class="n">metadata</span><span class="o">=</span><span class="p">{</span> + <span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="p">(</span> + <span class="s2">&quot;reward model name (huggingface) or its path&quot;</span> + <span class="p">),</span> + <span class="p">},</span> +<span class="p">)</span> +</pre></div> +</div> +<p>Note that in general, if the reward function is not trained by following the steps in last section, you may also need to modify the ``get_reward_function’’ function in the same file to use your customized reward function.</p> +<p>We run the alignment with the following command and hyper-parameters</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>./scripts/run_raft_align.sh<span class="w"> </span> +</pre></div> +</div> +<ul class="simple"> +<li><p>–model_name_or_path: /root/data/usr_name/output_models/hh_rlhf_llama-sft (the model get from sft step, adjusted according your setup)</p></li> +<li><p>–dataset_path:${project_dir}/data/hh_rlhf/rlhf_prompt</p></li> +<li><p>–output_dir: /root/data/usr_name/output_models/hh_rlhf_raft_align</p></li> +<li><p>–num_train_epochs: 4</p></li> +<li><p>–learning_rate: 2e-5</p></li> +<li><p>–per_device_train_batch_size: adjust according to your GPU memory source.</p></li> +<li><p>–inference_batch_size_per_device: adjust according to your GPU memory source.</p></li> +<li><p>–num_raft_iteration 20</p></li> +<li><p>–top_reward_percentage 0.125; (which means that we sample 8 responses for each prompt)</p></li> +<li><p>–raft_batch_size 1024</p></li> +<li><p>–collection_strategy “local”</p></li> +</ul> +<p>The experiment runs smoothly and the training reward increases from ~2.7 to 3.4. However, we observe a significant drop in the diversity metric (e.g. distinct-2 drops to 0.22 from 0.39). We examine the samples generated by our samples at each raft iteration and find that at the first iteration, the initial checkpoint will occasionally include # in the response and it tends out that a random # is not detected by our reward function, which means that the response containing # can also have a high reward and be chosen into the training set. Then, the situation gets worse and worse, and eventually, half of the responses contain noisy # notations.</p> +<p><strong>Step 3: retrain the model</strong></p> +<p>To alleviate the problem in step 2, we simply discard the collected samples if they contain # by assigning a large negative reward to it. It turns out that this works for our goal. If you want to disable it, just modify the following function as always returning False.</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">_discard_sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span> + <span class="k">if</span> <span class="s2">&quot;#&quot;</span> <span class="ow">in</span> <span class="n">text</span><span class="p">:</span> + <span class="k">return</span> <span class="kc">True</span> + <span class="k">return</span> <span class="kc">False</span> +</pre></div> +</div> +<p>The following figure shows the reward curve of RAFT (note that we use a smaller temperature to test the model, leading to a higher evaluation reward):</p> +<p><img alt="" src="../_images/raft_reward.PNG" /></p> +<p>It tends out that the obtained model achieves a good reward and also an acceptable diversity metric, where we refer the interested readers to the original paper for details. However, it is more like a starting point of our journey. We present some randomly sampled responses here. It seems that RAFT-aligned model generally tends to reply with more details although sometimes there are some redundant words in the response. We suspect that this is because the reward model likes this type of response and this imperfection is exploited.</p> +</section> +<section id="end-note"> +<h3>3.3.3 End Note<a class="headerlink" href="#end-note" title="Permalink to this heading">#</a></h3> +<p>We remark in this subsection that if you want to try out RAFT on your customized task. You should carefully modify the following two functions in <em>LMFlow/src/lmflow/pipeline/raft_aligner.py</em>, which extract the response from the generated texts:</p> +<ul class="simple"> +<li><p>clean_text(self, text);</p></li> +<li><p>discard_sample(self, text).</p></li> +</ul> +<p>Also, you may also first make sure the collected samples (automatically saved in the output_dir) look good.</p> +<p>There are still many rooms of improvement. To further improve the model performance, we can improve the reward model (e.g. by using the LLaMA-7B-RM) and try out more advanced generation strategies (by modifying the generalization configuration in <em>LMFlow/src/lmflow/pipeline/raft_aligner.py</em>), which we leave for future work. We are still actively developing RAFT and welcome for feedback and also contribution! Also checkout our LMFlow framework to get more fun of LLMs:</p> +<p><a class="reference external" href="https://github.com/OptimalScale/LMFlow">OptimalScale/LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Model for All. (github.com)</a></p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head"><p>Prompt</p></th> +<th class="head"><p>LLaMA-SFT</p></th> +<th class="head"><p>LLaMA-RAFT</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td><p>###Human: How can I get my girlfriend to cook more?###Assistant: Have you tried reminding her of how nice the food tastes?###Human: I could do it more.###Assistant:</p></td> +<td><p>That’s an easy one, so it may not be the most effective strategy. Do you know of any other strategies?</p></td> +<td><p>That’s a good idea! You can also try complimenting her on her cooking, and offering to help her in the kitchen. She might enjoy cooking more if she feels appreciated. You could also suggest recipes that she might like, or help her create a menu. Cooking can be a lot of fun, and it’s a great way to spend time together.</p></td> +</tr> +<tr class="row-odd"><td><p>###Human: How do you get a child to do homework?###Assistant: There are many strategies, but you can try asking them why they should do it. Ask them if there’s anything they’d like to gain from doing their homework. Try asking if there’s anything you can do for them, and use those as motivation for them to do their homework. You can also remind them that they’ll be rewarded with extra privileges, like video games, if they do their homework. Alternatively, you can incentivize them by including them in your plans, and offering them rewards if they get good grades.###Human: So make it so there is something in it for them?###Assistant:</p></td> +<td><p>Yes, exactly. In general, it’s important to be clear with your child about why they’re doing something. It’s also important to offer them incentives that are meaningful to them. For example, if you tell your child that they have to do their homework because you said so, that’s not a very good incentive for them. However, if you tell them that they’ll be able to use the</p></td> +<td><p>Yes, that’s a good strategy! Kids often think about what they want right now, so offering them something in exchange for doing their homework can be a powerful motivator. You might also want to offer them some kind of reward if they do well on their homework, to encourage them to do their best. Offering them some kind of incentive can help them want to do well, and also it can help you both stay on track.</p></td> +</tr> +<tr class="row-even"><td><p>###Human: I am doing a project on weather disasters in the United States. Can you tell me what the strongest hurricane was to ever hit the U.S. was?###Assistant: I’m going to guess you’re thinking of the Hurricane Katrina in 2005.###Human: Well, I’m not really thinking of anything in particular. Was Katrina the strongest hurricane to ever hit the U.S.?###Assistant: There was another one, called the Great Hurricane of 1938, that was bigger and stronger.###Human: Are you sure the date is correct? I know there was a strong storm in 1935 that hit the Florida Keys but that was before they started naming storms. Is that the one you are referring to?###Assistant:</p></td> +<td><p>I could search for more information, but I’ll just share what I’ve found. Here’s a review of the conditions in 1935 and 1938. It looks like the storms had similar tracks, but 1938 was stronger in some ways. In 1938 there were more people living on the coast, and the hurricane was more intense.</p></td> +<td><p>Yes, the Great Hurricane of 1938 was the strongest hurricane ever to hit the United States. It was a Category 5 storm, and it caused over 1,800 deaths. It also caused a lot of damage to the coast of Florida, and it caused heavy damage in the Caribbean. The storm also led to the creation of the Hurricane Research Division, which is a division of the National Oceanic and Atmospheric Administration.</p></td> +</tr> +<tr class="row-odd"><td><p>###Human: Is a firefighter a good occupation?###Assistant: I’m not sure what the exact definition of “occupation” is. I think you’re asking whether it’s “good” to be a firefighter. What criteria are you using to judge whether it’s good or not?###Human: Is it a good thing to work on?###Assistant:</p></td> +<td><p>Are you asking whether it’s a good thing to be a firefighter, or a good thing to work on?</p></td> +<td><p>Yes, being a firefighter is a great thing to do! Firefighters are heroes who help people in need. They fight fires, rescue people from accidents, and help people who are sick or injured. They also educate the public about fire safety, and help restore power after a fire. Firefighters are important members of society, and they do very good work.</p></td> +</tr> +<tr class="row-even"><td><p>###Human: Would you be able to provide a copy cat recipe for Girl scout samoas cookies?###Assistant: Sure, here’s a recipe for them.###Human: Okay, present it###Assistant: Would you like it presented visually?###Human: Yes, just list out the recipe please###Assistant:</p></td> +<td><p>Okay, let’s try.</p></td> +<td><p>You’ll need three cups of flour, one teaspoon of salt, one teaspoon of baking soda, and one teaspoon of baking powder. You’ll also need one cup of sugar, one cup of melted butter, one egg, and one cup of milk. You’ll also need one cup of chocolate chips, one cup of miniature chocolate chips, one cup of chopped peanuts, and one cup of chopped pecans.</p></td> +</tr> +<tr class="row-odd"><td><p>###Human: I’ve heard the videogame Metal Gear Solid 5 Phantom Pain was released in an unfinished state that resulted in series creator Hideo Kojima leaving Konami. What do you know about this?###Assistant:</p></td> +<td><p>I don’t know a lot about it, but I do know that Hideo Kojima had this to say about the game’s release:###“It’s a very unusual situation for me to be releasing a game in this state. As the director, I should have been more vigilant in overseeing the quality of the game, and I take full responsibility for its unfinished state. I’d like to take this opportunity</p></td> +<td><p>Here’s what I know: <br />1. The game was released in an unfinished state. <br />2. This led to Kojima leaving Konami. <br />3. Kojima had been with Konami for over 30 years.<br />4. He was one of the original creators of the Metal Gear series.<br />5. He is now the head of his own game studio, Kojima Productions.<br />6. His new game, Death Stranding, will be released in 2020.</p></td> +</tr> +<tr class="row-even"><td><p>###Human: what is a good brand for kitchen knives?###Assistant:</p></td> +<td><p>I’m not sure what you mean by “brand”. Do you mean a brand name, like “Victorinox” or “Wüsthof”? Those are two popular brands of knives.</p></td> +<td><p>I recommend Wüsthof.</p></td> +</tr> +<tr class="row-odd"><td><p>###Human: I like to host guests at my home from time to time, and I am gathering recipes of different dishes and drinks to keep things interesting. I am interested in trying some Rwandan dishes. Can you give me a recipe for Mizuzu?###Assistant: What is Mizuzu?###Human: It is deep fried plantains.###Assistant:</p></td> +<td><p>Is there anything special you want to add to the recipe? Like some spicy sauce, meat, etc?</p></td> +<td><p>You want to make some deep fried plantains? You will want to start with a mix of plantains and vegetable oil. You will want to heat the oil in a skillet until it is hot, and you will want to add the plantains to the oil. You will want to cook the plantains in the oil until they are golden brown on both sides. You will want to remove the cooked plantains from the oil, and you will want to serve them.</p></td> +</tr> +<tr class="row-even"><td><p>###Human: What are some of JFK’s greatest accomplishments</p></td> +<td><p>JFK is often remembered for his work on civil rights, but he also worked on the space program and created the Peace Corps. He was also a strong proponent of science and technology. He also worked on education reform. So, he really worked on a number of initiatives that had a lasting impact.</p></td> +<td><p>Kennedy was a great leader who inspired many people with his vision and values. He was a leader who cared about the poor and worked to help them. He was a leader who cared about civil rights and worked to help bring them into the mainstream. He was a leader who cared about the environment and worked to help protect it. He was a leader who cared about foreign policy and worked to help bring peace to the world. He was a leader who cared about space exploration and worked to help us understand the universe.</p></td> +</tr> +<tr class="row-odd"><td><p>###Human: My supervisor often does a lot of tasks incorrectly. What’s the best way to handle this?###Assistant:</p></td> +<td><p>Here are a few options:###1. Ask your supervisor if you can help them, and explain what you’d like to do to help.###2. Start with the tasks your supervisor does incorrectly. Then, if you can, do them as well or better than your supervisor. Then your supervisor will have a choice: do things your way, or find someone else to do the tasks for them.###3. If your supervisor</p></td> +<td><p>To correct your supervisor’s mistakes, you should provide feedback in a respectful and diplomatic way. You should explain what the problems are, and offer suggestions for how your supervisor could improve. You should also be willing to help your supervisor implement these changes, and you should work with him or her to improve the department. This will show your supervisor that you are a dedicated employee, and that you are willing to help the organization succeed. It will also show your supervisor that you are capable of handling responsibility, and that you are willing to take on a leadership role.</p></td> +</tr> +<tr class="row-even"><td><p>###Human: I was talking to an AI the other day and it seemed like it didn’t understand anything I said. Don’t you hate it when technology doesn’t work right?###Assistant:</p></td> +<td><p>Hm, I think you’re really talking about something that’s frustrating for me as well. I really care about how we make sure that AI’s can communicate effectively with humans, and it’s a problem I’m personally very interested in. Can you tell me more about what was frustrating for you?</p></td> +<td><p>Yes, it can be frustrating when technology doesn’t work properly. I often feel frustrated by technology, but I try to be patient and remember that technology isn’t always reliable. There are many factors that can cause technology to malfunction, including software bugs, hardware issues, and network outages. Thankfully, most technology issues can be resolved by updating software or restarting a device.</p></td> +</tr> +</tbody> +</table> +</section> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="reward_modeling.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title">Reward Modeling</p> + </div> + </a> + <a class="right-next" + href="TASK_GUIDE.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title">LMFlow Benchmark Guide</p> + </div> + <i class="fa-solid fa-angle-right"></i> + </a> +</div> + </footer> + + </div> + + + + <div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner"> + + <div class="sidebar-secondary-item"> + <div class="page-toc tocsection onthispage"> + <i class="fa-solid fa-list"></i> On this page + </div> + <nav class="bd-toc-nav page-toc"> + <ul class="visible nav section-nav flex-column"> +<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#">1 Introduction</a><ul class="visible nav section-nav flex-column"> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dataset-description">1.1 Dataset description</a></li> +</ul> +</li> +<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#reward-modeling">2 Reward Modeling</a><ul class="visible nav section-nav flex-column"> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#supervised-finetuning-sft">2.1 Supervised Finetuning (SFT)</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#id1">2.2 Reward Modeling</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#lora-merge-and-get-reward-model">2.3 LoRA Merge and Get Reward Model</a></li> +</ul> +</li> +<li class="toc-h1 nav-item toc-entry"><a class="reference internal nav-link" href="#raft-alignment">3 RAFT Alignment</a><ul class="visible nav section-nav flex-column"> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#algorithms-overview">3.1 Algorithms Overview</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#hyper-parameters">3.2 Hyper-parameters</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#examples">3.3 Examples</a><ul class="nav section-nav flex-column"> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#sft">3.3.1 SFT</a></li> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#id2">3.3.2 RAFT Alignment</a></li> +<li class="toc-h3 nav-item toc-entry"><a class="reference 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+<h1>Reward Modeling<a class="headerlink" href="#reward-modeling" title="Permalink to this heading">#</a></h1> +<section id="introduction"> +<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">#</a></h2> +<p>Reinforcement Learning from Human Feedback (RLHF) requires a reward function to guide the adjustment of the generative model. In this example, we show how to use LMFlow framework to train a reward model following the procedure in the InstructGPT paper: https://arxiv.org/abs/2203.02155 . We use the Dahoas/full-hh-rlhf dataset as an example, where each sample of this dataset consists of a prompt and two responses from the assistant. In particular, the response with label “chosen” is preferred as compared to the response with label “rejected”. The dataset consists of 112K training samples and 12.5K test samples. The following is an example sample of the dataset:</p> +<p>Prompt:</p> +<p>“ Human: What kind of noises did dinosaurs make? Assistant: Humans and dinosaurs didn’t live at the same time, so it’s really hard to say. The best place to find out what noises dinosaurs made would be Human: yes they did Assistant: to guess, and that would probably require lots of reading and a certain amount of imagination, so we’re not really prepared to do that. Human: you cant read Assistant:</p> +<p>Chosen response: “You can read?”</p> +<p>Rejected response: “there’s a lot of stuff humans don’t know”</p> +<p>As an example, we prepare 10K sft training samples, 12K reward modeling samples (where 10% of them are split for evaluation) at ./data/hh_rlhf.</p> +</section> +<section id="step-1-supervised-finetuning-sft"> +<h2>Step 1 Supervised Finetuning (SFT)<a class="headerlink" href="#step-1-supervised-finetuning-sft" title="Permalink to this heading">#</a></h2> +<p>We prepare the dataset used for supervised finetuning by adding a prefix to the Human and Assistant inputs to prompt model responses and simplify post-processing. Here is an example of a two-sample dataset to illustrate this.</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="o">{</span><span class="s2">&quot;type&quot;</span>:<span class="w"> </span><span class="s2">&quot;text_only&quot;</span>,<span class="w"> </span><span class="s2">&quot;instances&quot;</span>:<span class="w"> </span><span class="o">[{</span><span class="s2">&quot;text&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: Should you buy a case to protect your cell phone?###Assistant: It depends on your circumstances. If you carry your phone in a pocket or a purse then you probably want a case. But if you only need a phone for quick interactions, a case may actually cause more harm than good. What do you need the phone for? Are you a parent, or do you work from home?###Human: What harm could it do?###Assistant: A phone case can damage the screen, for one thing. It can also get you in trouble if you have your phone turned off for some reason. Then you will turn it back on and it won’t do anything. If you can afford to replace it, then you need a case to protect it. The problem is that most people aren’t able to afford to replace their phones all the time.###Human: Thanks for letting me know.###Assistant: You’re welcome.&quot;</span><span class="o">}</span>,<span class="w"> </span><span class="o">{</span><span class="s2">&quot;text&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: I&#39;m trying to learn about the salam witch trials###Assistant: If you’re looking for books about witchcraft trials, I can recommend some books for you. But you probably mean you’re looking for more historical information about the Salem witch trials in 1692, and specifically about the salam witch trials in 1692?###Human: What are some good books to learn about the salam witch trials###Assistant: What would you like to learn? If you’re interested in history, one of the best books is The Witch Persecutions in Colonial America: A History. If you’re interested in witchcraft as a cultural phenomenon, you might enjoy two excellent books: Religion and the Decline of Magic: Studies in Popular Beliefs in Sixteenth- and Seventeenth-Century England by Keith Thomas and Magic, Witchcraft, and the Otherworld: An Anthropology of Superstition by Jack Goody. If you’re interested in history specifically as it relates to religion, you might enjoy The Popish Plot, or Prelates&#39; Plot: A History of the Popish Plot in England, by K. J. Everett.&quot;</span><span class="o">}]}</span> +</pre></div> +</div> +<p>You can edit /scripts/run_finetune.sh and choose the model you want to use by modify “–model_name_or_path”, and also modify the “dataset_path” so that the used dataset is the above dataset. For instance, you can use the model EleutherAI/gpt-neo-1.3B, and set the dataset as:</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="nv">dataset_path</span><span class="o">=</span><span class="si">${</span><span class="nv">project_dir</span><span class="si">}</span>/data/hh_rlhf/sft +</pre></div> +</div> +<p>Then, we can run the following command to perform SFT.</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>./scripts/run_finetune.sh +</pre></div> +</div> +<p>You can also use lora training by the following command but also need to set the model_name_or_path and dataset appropriately by editing run_finetune_with_lora.sh .</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>./scripts/run_finetune_with_lora.sh +</pre></div> +</div> +</section> +<section id="step-2-reward-modeling"> +<h2>Step 2 Reward Modeling<a class="headerlink" href="#step-2-reward-modeling" title="Permalink to this heading">#</a></h2> +<p><strong>Data Preparation</strong> Reward modeling involves ranking responses based on the given prompt, typically requiring human labeling. Fortunately, we already have two responses for each prompt in this example, with one preferred over the other. To create a new dataset, we modify the original dataset to include two keys, “positive” and “negative”, with the former being the preferred response. Here is an example of the dataset, consisting of two samples.</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span><span class="o">{</span><span class="s2">&quot;instances&quot;</span>:<span class="w"> </span><span class="o">[</span> +<span class="w"> </span><span class="o">{</span><span class="s2">&quot;positive&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: Are space heaters safe?###Assistant: What do you mean? Can you give me more details?###Human: I mean, can they be a fire hazard?###Assistant: I don’t know about that. Can you tell me more about the situation? Are you using the heater on a fireplace? Do you have any other heater in the room? What materials are in the room? Do you feel any heat from the heater? How are you touching the heater?###Human: Can you just tell me if there are any situations where they could be a fire hazard?###Assistant: The answer is “it depends”. It would depend on the specific heater you are using, how you are using it, what kind of fuel it uses, what materials you have in the room, and what the surroundings are like. You’d also have to make sure you’re using it correctly. Is it switched off? Is it safe to touch? It’s not the sort of thing I could just answer by looking at the text you provided.###Human: Thank you. That information is helpful.###Assistant: Glad to be helpful. I’m always happy to assist.&quot;</span>,<span class="w"> </span><span class="s2">&quot;negative&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: Are space heaters safe?###Assistant: What do you mean? Can you give me more details?###Human: I mean, can they be a fire hazard?###Assistant: I don’t know about that. Can you tell me more about the situation? Are you using the heater on a fireplace? Do you have any other heater in the room? What materials are in the room? Do you feel any heat from the heater? How are you touching the heater?###Human: Can you just tell me if there are any situations where they could be a fire hazard?###Assistant: The answer is “it depends”. It would depend on the specific heater you are using, how you are using it, what kind of fuel it uses, what materials you have in the room, and what the surroundings are like. You’d also have to make sure you’re using it correctly. Is it switched off? Is it safe to touch? It’s not the sort of thing I could just answer by looking at the text you provided.###Human: Thank you. That information is helpful.###Assistant: You’re welcome. I’m glad to help. Is there anything else you want to know?&quot;</span><span class="o">}</span>,<span class="w"> </span> +<span class="w"> </span><span class="o">{</span><span class="s2">&quot;positive&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: how do i teach my kids to make forts at home?###Assistant: Do you mean you want to teach them to build a simple structure they can hide in? Or something more elaborate? I don’t know exactly what you mean by a “fort”, but maybe this gives you some ideas?&quot;</span>,<span class="w"> </span><span class="s2">&quot;negative&quot;</span>:<span class="w"> </span><span class="s2">&quot;###Human: how do i teach my kids to make forts at home?###Assistant: There are many ways to make forts! The simplest might be to just pile up all the furniture in one room. This works if you have a single room in your house, although it might still be a bit challenging to build a taller and sturdier fort this way. The best way to build forts is to start with something easy, and then have fun exploring how you can improve it.&quot;</span><span class="o">}</span> +<span class="o">]}</span> +</pre></div> +</div> +<p>To start from a model from a previous sft step, you may edit the run_reward_modeling.sh script and update the “dataset_path” to use the desired dataset. Additionally, you can modify the validation_split_percentage parameter to select the last percentage of samples for evaluation. The load_dataset function splits the dataset into training and evaluation sets, which can also be customized by editing the function in /examples/run_reward_modeling.py if you want to prepare your own dataset when running the script.</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">build_dataset</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">config</span><span class="p">):</span> +<span class="w"> </span><span class="sd">&#39;&#39;&#39; </span> +<span class="sd"> We assume that we have preprocessed the dataset appropriately such that the sample is organized as follows:</span> +<span class="sd"> {&quot;positive&quot;: prompt + answer_positive, &quot;negative&quot;: prompt + answer_negative}, where the positive response is preferred.</span> +<span class="sd"> &#39;&#39;&#39;</span> + <span class="k">def</span> <span class="nf">tokenize</span><span class="p">(</span><span class="n">sample</span><span class="p">):</span> + <span class="n">tokenized_pos</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="s1">&#39;positive&#39;</span><span class="p">],</span> <span class="n">truncation</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">tokenized_neg</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="s1">&#39;negative&#39;</span><span class="p">],</span> <span class="n">truncation</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + <span class="n">sample</span><span class="p">[</span><span class="s2">&quot;chosen_input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tokenized_pos</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span> + <span class="n">sample</span><span class="p">[</span><span class="s2">&quot;chosen_attention_mask&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tokenized_pos</span><span class="p">[</span><span class="s2">&quot;attention_mask&quot;</span><span class="p">]</span> + <span class="n">sample</span><span class="p">[</span><span class="s2">&quot;rejected_input_ids&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tokenized_neg</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">]</span> + <span class="n">sample</span><span class="p">[</span><span class="s2">&quot;rejected_attention_mask&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">tokenized_neg</span><span class="p">[</span><span class="s2">&quot;attention_mask&quot;</span><span class="p">]</span> + <span class="k">return</span> <span class="n">sample</span> + + <span class="n">ds</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s2">&quot;json&quot;</span><span class="p">,</span> <span class="n">data_files</span><span class="o">=</span><span class="n">config</span><span class="o">.</span><span class="n">dataset_path</span><span class="p">,</span> <span class="n">split</span><span class="o">=</span><span class="s2">&quot;train&quot;</span><span class="p">,</span> <span class="n">field</span><span class="o">=</span><span class="s2">&quot;instances&quot;</span><span class="p">)</span> + <span class="n">ds</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">tokenize</span><span class="p">,</span> <span class="n">batched</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> + <span class="n">ds</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;chosen_input_ids&quot;</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="mi">512</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="s2">&quot;rejected_input_ids&quot;</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="mi">512</span><span class="p">)</span> + <span class="n">eval_dataset</span> <span class="o">=</span> <span class="kc">None</span> + <span class="k">if</span> <span class="n">config</span><span class="o">.</span><span class="n">validation_split_percentage</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> + <span class="n">idx_gap</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="mi">1</span><span class="o">-</span><span class="n">config</span><span class="o">.</span><span class="n">validation_split_percentage</span><span class="o">/</span><span class="mi">100</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">ds</span><span class="p">))</span> + <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">idx_gap</span><span class="p">))</span> + <span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">idx_gap</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ds</span><span class="p">)))</span> + <span class="k">else</span><span class="p">:</span> + <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">ds</span> + + <span class="k">return</span> <span class="n">train_dataset</span><span class="p">,</span> <span class="n">eval_dataset</span> + +</pre></div> +</div> +<p>We use the following loss function to train the reward model following the instruct-GPT paper.</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span> <span class="n">loss</span> <span class="o">=</span> <span class="o">-</span><span class="n">nn</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">logsigmoid</span><span class="p">(</span><span class="n">chosen_rewards</span> <span class="o">-</span> <span class="n">rejected_rewards</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> +</pre></div> +</div> +<p>The reward modeling script can be used by</p> +<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>./scripts/run_reward_modeling.sh +</pre></div> +</div> +</section> +<section id="examples"> +<h2>Examples<a class="headerlink" href="#examples" title="Permalink to this heading">#</a></h2> +<p>We train reward models using the hh-rlhf dataset with four models, LLaMA-13B LLaMA-7B, GPT-NEO-2.7B, and GPT-NEO-1.3B. The model is first supervised fine-tuned with the training dataset. The reward modeling is trained using the 112K training samples and is evaluated on the 12.5 test samples.</p> +<p>The SFT step appears to be crucial, and the number of epochs during SFT can make a difference. The most successful model we obtained was initialized from LLaMA-13B, which underwent SFT on the training dataset for 2 epochs. For reward modeling, we utilize LoRA with a rank of 16. Surprisingly, increasing the LoRA rank to 32 or even 128 does not result in a significant improvement in evaluation accuracy. Moreover, we find that the choice of batch size does not have a significant impact on the training results. Additionally, we observe slight overfitting of the model during the second epoch of reward modeling.</p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-center"><p>Model</p></th> +<th class="head text-center"><p>Eval Accuracy</p></th> +<th class="head text-center"><p>Training record</p></th> +<th class="head text-center"><p>Remarks</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-center"><p>LLaMA-13B</p></td> +<td class="text-center"><p>84.55%</p></td> +<td class="text-center"><p>See https://wandb.ai/ianz2020/huggingface/runs/bg677mxa</p></td> +<td class="text-center"><p>RM from LLaMA with 2 epochs of SFT</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>LLaMA-13B</p></td> +<td class="text-center"><p>81.80%</p></td> +<td class="text-center"><p>See https://wandb.ai/ianz2020/huggingface/runs/ka9v1ywd</p></td> +<td class="text-center"><p>RM from LLaMA with 1 epoch of SFT</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>LLaMA-13B</p></td> +<td class="text-center"><p>71.64%</p></td> +<td class="text-center"><p>See https://wandb.ai/ianz2020/huggingface/runs/lntwmcyd</p></td> +<td class="text-center"><p>RM from LLaMA without SFT</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>LLaMA-7B</p></td> +<td class="text-center"><p>79.52%</p></td> +<td class="text-center"><p>See https://wandb.ai/weixiong5237/huggingface/runs/t3uwm8yp</p></td> +<td class="text-center"><p>-</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>LLaMA-7B</p></td> +<td class="text-center"><p>71.64%</p></td> +<td class="text-center"><p>See https://wandb.ai/weixiong5237/huggingface/runs/p2ju3r1a</p></td> +<td class="text-center"><p>RM from LLaMA without SFT</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>GPT-NEO-2.7B</p></td> +<td class="text-center"><p>69.24%</p></td> +<td class="text-center"><p>See https://wandb.ai/weixiong5237/huggingface/runs/8fc1rcf8</p></td> +<td class="text-center"><p>-</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>GPT-NEO-1.3B</p></td> +<td class="text-center"><p>65.58%</p></td> +<td class="text-center"><p>See https://wandb.ai/weixiong5237/huggingface/runs/7oemwynu</p></td> +<td class="text-center"><p>Only trained on 10000 samples</p></td> +</tr> +</tbody> +</table> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> + <a class="left-prev" + href="checkpoints.html" + title="previous page"> + <i class="fa-solid fa-angle-left"></i> + <div class="prev-next-info"> + <p class="prev-next-subtitle">previous</p> + <p class="prev-next-title">Checkpoints</p> + </div> + </a> + <a class="right-next" + href="raft.html" + title="next page"> + <div class="prev-next-info"> + <p class="prev-next-subtitle">next</p> + <p class="prev-next-title">1 Introduction</p> + </div> + <i class="fa-solid fa-angle-right"></i> + </a> +</div> + 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href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.__post_init__">(lmflow.args.ModelArguments method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/index.html#lmflow.__version__">__version__ (in module lmflow)</a> + + <ul> + <li><a href="autoapi/lmflow/version/index.html#lmflow.version.__version__">(in module lmflow.version)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._add_sm_patterns_to_gitignore">_add_sm_patterns_to_gitignore() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.html#lmflow.utils.flash_attention.gpt_neo_flash_attention._attn">_attn() (in module lmflow.utils.flash_attention.gpt_neo_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel">_bwd_kernel() (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._bwd_kernel_one_col_block">_bwd_kernel_one_col_block() (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._bwd_preprocess_do_o_dot">_bwd_preprocess_do_o_dot() (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._bwd_store_dk_dv">_bwd_store_dk_dv() (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset._check_data_format">_check_data_format() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset._check_data_format">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/base_aligner/index.html#lmflow.pipeline.base_aligner.BaseAligner._check_if_alignable">_check_if_alignable() (lmflow.pipeline.base_aligner.BaseAligner method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/base_tuner/index.html#lmflow.pipeline.base_tuner.BaseTuner._check_if_tunable">_check_if_tunable() (lmflow.pipeline.base_tuner.BaseTuner method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._clean_text">_clean_text() (lmflow.pipeline.raft_aligner.RaftAligner method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._discard_sample">_discard_sample() (lmflow.pipeline.raft_aligner.RaftAligner method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._evaluate_acc_with_accelerator">_evaluate_acc_with_accelerator() (lmflow.pipeline.evaluator.Evaluator method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._evaluate_acc_with_deepspeed">_evaluate_acc_with_deepspeed() (lmflow.pipeline.evaluator.Evaluator method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._evaluate_nll">_evaluate_nll() (lmflow.pipeline.evaluator.Evaluator method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._evaluate_ppl">_evaluate_ppl() (lmflow.pipeline.evaluator.Evaluator method)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_backward">_flash_attn_backward() (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._flash_attn_forward">_flash_attn_forward() (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention._fwd_kernel">_fwd_kernel() (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._gather_and_numpify">_gather_and_numpify() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_local">_get_batch_dataset_local() (lmflow.pipeline.raft_aligner.RaftAligner method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._get_batch_dataset_top">_get_batch_dataset_top() (lmflow.pipeline.raft_aligner.RaftAligner method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_collator_with_removed_columns">_get_collator_with_removed_columns() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_eval_sampler">_get_eval_sampler() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_output_dir">_get_output_dir() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._get_train_sampler">_get_train_sampler() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._hp_search_setup">_hp_search_setup() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._initialize_trainer">_initialize_trainer() (lmflow.pipeline.raft_aligner.RaftAligner method)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._inner_training_loop">_inner_training_loop() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer._is_native_cpu_amp_available">_is_native_cpu_amp_available (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._issue_warnings_after_load">_issue_warnings_after_load() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_best_model">_load_best_model() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._load_dataset">_load_dataset() (lmflow.pipeline.raft_aligner.RaftAligner method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_from_checkpoint">_load_from_checkpoint() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner._load_input_dataset">_load_input_dataset() (lmflow.pipeline.raft_aligner.RaftAligner method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_optimizer_and_scheduler">_load_optimizer_and_scheduler() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._load_rng_state">_load_rng_state() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator._match">_match() (lmflow.pipeline.evaluator.Evaluator method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._maybe_log_save_evaluate">_maybe_log_save_evaluate() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._move_model_to_device">_move_model_to_device() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._nested_gather">_nested_gather() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._one_train">_one_train() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._pad_across_processes">_pad_across_processes() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.html#lmflow.utils.flash_attention.bloom_flash_attention._prepare_attn_mask">_prepare_attn_mask() (in module lmflow.utils.flash_attention.bloom_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.html#lmflow.utils.flash_attention.gpt2_flash_attention._prepare_decoder_attention_mask">_prepare_decoder_attention_mask() (in module lmflow.utils.flash_attention.gpt2_flash_attention)</a> + + <ul> + <li><a href="autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.html#lmflow.utils.flash_attention.llama_flash_attention._prepare_decoder_attention_mask">(in module lmflow.utils.flash_attention.llama_flash_attention)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_input">_prepare_input() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._prepare_inputs">_prepare_inputs() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._push_from_checkpoint">_push_from_checkpoint() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._remove_unused_columns">_remove_unused_columns() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._report_to_hp_search">_report_to_hp_search() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._rotate_checkpoints">_rotate_checkpoints() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback._save">_save() (lmflow.pipeline.utils.peft_trainer.PeftSavingCallback method)</a> + + <ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save">(lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftTrainer._save_checkpoint">_save_checkpoint() (lmflow.pipeline.utils.peft_trainer.PeftTrainer method)</a> + + <ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_checkpoint">(lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._save_tpu">_save_tpu() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._set_signature_columns_if_needed">_set_signature_columns_if_needed() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._sorted_checkpoints">_sorted_checkpoints() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._tune_save_checkpoint">_tune_save_checkpoint() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer._wrap_model">_wrap_model() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="A">A</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.add_callback">add_callback() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/base_aligner/index.html#lmflow.pipeline.base_aligner.BaseAligner.align">align() (lmflow.pipeline.base_aligner.BaseAligner method)</a> + + <ul> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner.align">(lmflow.pipeline.raft_aligner.RaftAligner method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.answer_extraction">answer_extraction() (in module lmflow.utils.data_utils)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.answer_type">answer_type (lmflow.args.EvaluatorArguments attribute)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#id0">arch_type (lmflow.args.ModelArguments attribute)</a>, <a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.arch_type">[1]</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.AutoArguments">AutoArguments (class in lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.autocast_smart_context_manager">autocast_smart_context_manager() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/models/auto_model/index.html#lmflow.models.auto_model.AutoModel">AutoModel (class in lmflow.models.auto_model)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/auto_pipeline/index.html#lmflow.pipeline.auto_pipeline.AutoPipeline">AutoPipeline (class in lmflow.pipeline.auto_pipeline)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="B">B</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.backward">backward() (lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc static method)</a> + + <ul> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.backward">(lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc static method)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.backward">(lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc static method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/base_aligner/index.html#lmflow.pipeline.base_aligner.BaseAligner">BaseAligner (class in lmflow.pipeline.base_aligner)</a> +</li> + <li><a href="autoapi/lmflow/models/base_model/index.html#lmflow.models.base_model.BaseModel">BaseModel (class in lmflow.models.base_model)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/base_pipeline/index.html#lmflow.pipeline.base_pipeline.BasePipeline">BasePipeline (class in lmflow.pipeline.base_pipeline)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/base_tuner/index.html#lmflow.pipeline.base_tuner.BaseTuner">BaseTuner (class in lmflow.pipeline.base_tuner)</a> +</li> + <li><a href="autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.batchlize">batchlize() (in module lmflow.utils.data_utils)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.BenchmarkingArguments">BenchmarkingArguments (class in lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.block_size">block_size (lmflow.args.DatasetArguments attribute)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="C">C</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.cache_dir">cache_dir (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.call_model_init">call_model_init() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.checkpoint_path">checkpoint_path (lmflow.args.VisModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.collection_strategy">collection_strategy (lmflow.args.RaftAlignerArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss">compute_loss() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.compute_loss_context_manager">compute_loss_context_manager() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.html#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding">CondenseRotaryEmbedding (class in lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.config_name">config_name (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.config_overrides">config_overrides (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator.create_dataloader">create_dataloader() (lmflow.pipeline.evaluator.Evaluator method)</a> + + <ul> + <li><a href="autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.Inferencer.create_dataloader">(lmflow.pipeline.inferencer.Inferencer method)</a> +</li> + </ul></li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.create_from_dict">create_from_dict() (lmflow.datasets.Dataset class method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.create_from_dict">(lmflow.datasets.dataset.Dataset class method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_model_card">create_model_card() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer">create_optimizer() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_optimizer_and_scheduler">create_optimizer_and_scheduler() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.create_scheduler">create_scheduler() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.custom_model">custom_model (lmflow.args.VisModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel">CustomAutoVision2SeqModel (class in lmflow.models.vision2seq_model)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.customized_cache_dir">customized_cache_dir (lmflow.args.DatasetArguments attribute)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="D">D</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset">Dataset (class in lmflow.datasets)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset">(class in lmflow.datasets.dataset)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.dataset_config_name">dataset_config_name (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.DATASET_DESCRIPTION_MAP">DATASET_DESCRIPTION_MAP (in module lmflow.utils.constants)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.BenchmarkingArguments.dataset_name">dataset_name (lmflow.args.BenchmarkingArguments attribute)</a> + + <ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.dataset_name">(lmflow.args.DatasetArguments attribute)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.dataset_path">dataset_path (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.DATASET_TYPES">DATASET_TYPES (in module lmflow.datasets.dataset)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments">DatasetArguments (class in lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.decode">decode() (lmflow.models.hf_decoder_model.HFDecoderModel method)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.decode">(lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel method)</a> +</li> + </ul></li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/decoder_model/index.html#lmflow.models.decoder_model.DecoderModel">DecoderModel (class in lmflow.models.decoder_model)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.deepspeed">deepspeed (lmflow.args.EvaluatorArguments attribute)</a> + + <ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.deepspeed">(lmflow.args.InferencerArguments attribute)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.DEFAULT_CALLBACKS">DEFAULT_CALLBACKS (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#id0">DEFAULT_PROGRESS_CALLBACK (in module lmflow.pipeline.utils.raft_trainer)</a>, <a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.DEFAULT_PROGRESS_CALLBACK">[1]</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.device">device (lmflow.args.InferencerArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.disable_group_texts">disable_group_texts (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.do_rope_scaling">do_rope_scaling (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.do_sample">do_sample (lmflow.args.InferencerArguments attribute)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="E">E</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.encode">encode() (lmflow.models.hf_decoder_model.HFDecoderModel method)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.encode">(lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/models/encoder_decoder_model/index.html#lmflow.models.encoder_decoder_model.EncoderDecoderModel">EncoderDecoderModel (class in lmflow.models.encoder_decoder_model)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.FinetunerArguments.eval_dataset_path">eval_dataset_path (lmflow.args.FinetunerArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator.evaluate">evaluate() (lmflow.pipeline.evaluator.Evaluator method)</a> + + <ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluate">(lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.evaluate_block_size">evaluate_block_size (lmflow.args.EvaluatorArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.evaluation_loop">evaluation_loop() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#lmflow.pipeline.evaluator.Evaluator">Evaluator (class in lmflow.pipeline.evaluator)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments">EvaluatorArguments (class in lmflow.args)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="F">F</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/finetuner/index.html#lmflow.pipeline.finetuner.Finetuner">Finetuner (class in lmflow.pipeline.finetuner)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.FinetunerArguments">FinetunerArguments (class in lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_func">flash_attn_func (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_kvpacked_func">flash_attn_kvpacked_func (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.flash_attn_qkvpacked_func">flash_attn_qkvpacked_func (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc">FlashAttnFunc (class in lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc">FlashAttnKVPackedFunc (class in lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc">FlashAttnQKVPackedFunc (class in lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.FLOAT_ONLY_DATASET_DESCRIPTION">FLOAT_ONLY_DATASET_DESCRIPTION (in module lmflow.utils.constants)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.floating_point_ops">floating_point_ops() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.html#lmflow.utils.flash_attention.bloom_flash_attention.forward">forward() (in module lmflow.utils.flash_attention.bloom_flash_attention)</a> + + <ul> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.html#lmflow.utils.flash_attention.gpt2_flash_attention.forward">(in module lmflow.utils.flash_attention.gpt2_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.html#lmflow.utils.flash_attention.gpt_neo_flash_attention.forward">(in module lmflow.utils.flash_attention.gpt_neo_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.html#lmflow.utils.flash_attention.llama_flash_attention.forward">(in module lmflow.utils.flash_attention.llama_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc.forward">(lmflow.utils.flash_attention.triton_flash_attention.FlashAttnFunc static method)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc.forward">(lmflow.utils.flash_attention.triton_flash_attention.FlashAttnKVPackedFunc static method)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc.forward">(lmflow.utils.flash_attention.triton_flash_attention.FlashAttnQKVPackedFunc static method)</a> +</li> + <li><a href="autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.html#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding.forward">(lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.CondenseRotaryEmbedding method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.from_dict">from_dict() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.from_dict">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + </ul></td> +</tr></table> + +<h2 id="G">G</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.generate">generate() (lmflow.models.vision2seq_model.CustomAutoVision2SeqModel method)</a> +</li> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.get_backend">get_backend() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_backend">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.get_backend_dataset">get_backend_dataset() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_backend_dataset">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.get_backend_model">get_backend_model() (lmflow.models.hf_decoder_model.HFDecoderModel method)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_backend_model">(lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.get_data_args">get_data_args() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_data_args">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_eval_dataloader">get_eval_dataloader() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.get_fingerprint">get_fingerprint() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_fingerprint">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.get_max_length">get_max_length() (lmflow.models.hf_decoder_model.HFDecoderModel method)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_max_length">(lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel method)</a> +</li> + </ul></li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/auto_model/index.html#lmflow.models.auto_model.AutoModel.get_model">get_model() (lmflow.models.auto_model.AutoModel class method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_optimizer_cls_and_kwargs">get_optimizer_cls_and_kwargs() (lmflow.pipeline.utils.raft_trainer.RaftTrainer static method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/auto_pipeline/index.html#lmflow.pipeline.auto_pipeline.AutoPipeline.get_pipeline">get_pipeline() (lmflow.pipeline.auto_pipeline.AutoPipeline class method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.AutoArguments.get_pipeline_args_class">get_pipeline_args_class() (lmflow.args.AutoArguments method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_test_dataloader">get_test_dataloader() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.get_tokenizer">get_tokenizer() (lmflow.models.hf_decoder_model.HFDecoderModel method)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.get_tokenizer">(lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.get_train_dataloader">get_train_dataloader() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.get_type">get_type() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.get_type">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#id0">GPU_SUPPORT_FLASH_ATTENTION (in module lmflow.models.hf_decoder_model)</a>, <a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.GPU_SUPPORT_FLASH_ATTENTION">[1]</a> +</li> + <li><a href="autoapi/lmflow/pipeline/finetuner/index.html#lmflow.pipeline.finetuner.Finetuner.group_text">group_text() (lmflow.pipeline.finetuner.Finetuner method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.group_texts_batch_size">group_texts_batch_size (lmflow.args.DatasetArguments attribute)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="H">H</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel">HFDecoderModel (class in lmflow.models.hf_decoder_model)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel">HFEncoderDecoderModel (class in lmflow.models.hf_encoder_decoder_model)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.hyperparameter_search">hyperparameter_search() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="I">I</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.inference">inference() (lmflow.models.hf_decoder_model.HFDecoderModel method)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.inference">(lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel method)</a> +</li> + <li><a href="autoapi/lmflow/models/text_regression_model/index.html#lmflow.models.text_regression_model.TextRegressionModel.inference">(lmflow.models.text_regression_model.TextRegressionModel method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.Inferencer.inference">(lmflow.pipeline.inferencer.Inferencer method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.inference_batch_size_per_device">inference_batch_size_per_device (lmflow.args.EvaluatorArguments attribute)</a> + + <ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.inference_batch_size_per_device">(lmflow.args.RaftAlignerArguments attribute)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.Inferencer">Inferencer (class in lmflow.pipeline.inferencer)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments">InferencerArguments (class in lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.init_git_repo">init_git_repo() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#lmflow.utils.flash_attention.triton_flash_attention.init_to_zero">init_to_zero() (in module lmflow.utils.flash_attention.triton_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.INSTANCE_FIELDS_MAP">INSTANCE_FIELDS_MAP (in module lmflow.utils.constants)</a> +</li> + <li><a href="autoapi/lmflow/index.html#lmflow.internal_version">internal_version (in module lmflow)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.ipex_optimize_model">ipex_optimize_model() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.is_custom_dataset">is_custom_dataset (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_local_process_zero">is_local_process_zero() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.IS_SAGEMAKER_MP_POST_1_10">IS_SAGEMAKER_MP_POST_1_10 (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.is_torch_greater_or_equal_than_1_10">is_torch_greater_or_equal_than_1_10 (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.is_torch_less_than_1_11">is_torch_less_than_1_11 (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.is_world_process_zero">is_world_process_zero() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="K">K</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.keep_linebreaks">keep_linebreaks (lmflow.args.DatasetArguments attribute)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.KEY_INSTANCES">KEY_INSTANCES (in module lmflow.datasets.dataset)</a> +</li> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.KEY_TYPE">KEY_TYPE (in module lmflow.datasets.dataset)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="L">L</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.language_model_from_pretrained">language_model_from_pretrained() (lmflow.models.vision2seq_model.CustomAutoVision2SeqModel method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.llm_model_name_or_path">llm_model_name_or_path (lmflow.args.VisModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.BenchmarkingArguments.lm_evaluation_metric">lm_evaluation_metric (lmflow.args.BenchmarkingArguments attribute)</a> +</li> + <li> + lmflow + + <ul> + <li><a href="autoapi/lmflow/index.html#module-lmflow">module</a> +</li> + </ul></li> + <li> + lmflow.args + + <ul> + <li><a href="api/_autosummary/lmflow.args.html#module-lmflow.args">module</a>, <a href="autoapi/lmflow/args/index.html#module-lmflow.args">[1]</a> +</li> + </ul></li> + <li> + lmflow.datasets + + <ul> + <li><a href="autoapi/lmflow/datasets/index.html#module-lmflow.datasets">module</a> +</li> + </ul></li> + <li> + lmflow.datasets.dataset + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#module-lmflow.datasets.dataset">module</a> +</li> + </ul></li> + <li> + lmflow.models + + <ul> + <li><a href="autoapi/lmflow/models/index.html#module-lmflow.models">module</a> +</li> + </ul></li> + <li> + lmflow.models.auto_model + + <ul> + <li><a href="autoapi/lmflow/models/auto_model/index.html#module-lmflow.models.auto_model">module</a> +</li> + </ul></li> + <li> + lmflow.models.base_model + + <ul> + <li><a href="autoapi/lmflow/models/base_model/index.html#module-lmflow.models.base_model">module</a> +</li> + </ul></li> + <li> + lmflow.models.decoder_model + + <ul> + <li><a href="autoapi/lmflow/models/decoder_model/index.html#module-lmflow.models.decoder_model">module</a> +</li> + </ul></li> + <li> + lmflow.models.encoder_decoder_model + + <ul> + <li><a href="autoapi/lmflow/models/encoder_decoder_model/index.html#module-lmflow.models.encoder_decoder_model">module</a> +</li> + </ul></li> + <li> + lmflow.models.hf_decoder_model + + <ul> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#module-lmflow.models.hf_decoder_model">module</a> +</li> + </ul></li> + <li> + lmflow.models.hf_encoder_decoder_model + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#module-lmflow.models.hf_encoder_decoder_model">module</a> +</li> + </ul></li> + <li> + lmflow.models.interfaces + + <ul> + <li><a href="autoapi/lmflow/models/interfaces/index.html#module-lmflow.models.interfaces">module</a> +</li> + </ul></li> + <li> + lmflow.models.interfaces.tunable + + <ul> + <li><a href="autoapi/lmflow/models/interfaces/tunable/index.html#module-lmflow.models.interfaces.tunable">module</a> +</li> + </ul></li> + <li> + lmflow.models.regression_model + + <ul> + <li><a href="autoapi/lmflow/models/regression_model/index.html#module-lmflow.models.regression_model">module</a> +</li> + </ul></li> + <li> + lmflow.models.text_regression_model + + <ul> + <li><a href="autoapi/lmflow/models/text_regression_model/index.html#module-lmflow.models.text_regression_model">module</a> +</li> + </ul></li> + <li> + lmflow.models.vision2seq_model + + <ul> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#module-lmflow.models.vision2seq_model">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline + + <ul> + <li><a href="autoapi/lmflow/pipeline/index.html#module-lmflow.pipeline">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.auto_pipeline + + <ul> + <li><a href="autoapi/lmflow/pipeline/auto_pipeline/index.html#module-lmflow.pipeline.auto_pipeline">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.base_aligner + + <ul> + <li><a href="autoapi/lmflow/pipeline/base_aligner/index.html#module-lmflow.pipeline.base_aligner">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.base_pipeline + + <ul> + <li><a href="autoapi/lmflow/pipeline/base_pipeline/index.html#module-lmflow.pipeline.base_pipeline">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.base_tuner + + <ul> + <li><a href="autoapi/lmflow/pipeline/base_tuner/index.html#module-lmflow.pipeline.base_tuner">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.evaluator + + <ul> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#module-lmflow.pipeline.evaluator">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.finetuner + + <ul> + <li><a href="autoapi/lmflow/pipeline/finetuner/index.html#module-lmflow.pipeline.finetuner">module</a> +</li> + </ul></li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li> + lmflow.pipeline.inferencer + + <ul> + <li><a href="autoapi/lmflow/pipeline/inferencer/index.html#module-lmflow.pipeline.inferencer">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.raft_aligner + + <ul> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#module-lmflow.pipeline.raft_aligner">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.utils + + <ul> + <li><a href="autoapi/lmflow/pipeline/utils/index.html#module-lmflow.pipeline.utils">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.utils.peft_trainer + + <ul> + <li><a href="autoapi/lmflow/pipeline/utils/peft_trainer/index.html#module-lmflow.pipeline.utils.peft_trainer">module</a> +</li> + </ul></li> + <li> + lmflow.pipeline.utils.raft_trainer + + <ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#module-lmflow.pipeline.utils.raft_trainer">module</a> +</li> + </ul></li> + <li> + lmflow.utils + + <ul> + <li><a href="autoapi/lmflow/utils/index.html#module-lmflow.utils">module</a> +</li> + </ul></li> + <li> + lmflow.utils.constants + + <ul> + <li><a href="autoapi/lmflow/utils/constants/index.html#module-lmflow.utils.constants">module</a> +</li> + </ul></li> + <li> + lmflow.utils.data_utils + + <ul> + <li><a href="autoapi/lmflow/utils/data_utils/index.html#module-lmflow.utils.data_utils">module</a> +</li> + </ul></li> + <li> + lmflow.utils.flash_attention + + <ul> + <li><a href="autoapi/lmflow/utils/flash_attention/index.html#module-lmflow.utils.flash_attention">module</a> +</li> + </ul></li> + <li> + lmflow.utils.flash_attention.bloom_flash_attention + + <ul> + <li><a href="autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.html#module-lmflow.utils.flash_attention.bloom_flash_attention">module</a> +</li> + </ul></li> + <li> + lmflow.utils.flash_attention.gpt2_flash_attention + + <ul> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.html#module-lmflow.utils.flash_attention.gpt2_flash_attention">module</a> +</li> + </ul></li> + <li> + lmflow.utils.flash_attention.gpt_neo_flash_attention + + <ul> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.html#module-lmflow.utils.flash_attention.gpt_neo_flash_attention">module</a> +</li> + </ul></li> + <li> + lmflow.utils.flash_attention.llama_flash_attention + + <ul> + <li><a href="autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.html#module-lmflow.utils.flash_attention.llama_flash_attention">module</a> +</li> + </ul></li> + <li> + lmflow.utils.flash_attention.triton_flash_attention + + <ul> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#module-lmflow.utils.flash_attention.triton_flash_attention">module</a> +</li> + </ul></li> + <li> + lmflow.utils.position_interpolation + + <ul> + <li><a href="autoapi/lmflow/utils/position_interpolation/index.html#module-lmflow.utils.position_interpolation">module</a> +</li> + </ul></li> + <li> + lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch + + <ul> + <li><a href="autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.html#module-lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch">module</a> +</li> + </ul></li> + <li> + lmflow.version + + <ul> + <li><a href="autoapi/lmflow/version/index.html#module-lmflow.version">module</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.load_data">load_data() (in module lmflow.utils.data_utils)</a> +</li> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.load_prompt_cache">load_prompt_cache() (lmflow.models.vision2seq_model.CustomAutoVision2SeqModel method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.local_rank">local_rank (lmflow.args.EvaluatorArguments attribute)</a> + + <ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.local_rank">(lmflow.args.InferencerArguments attribute)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.log">log() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.logger">logger (in module lmflow.models.hf_decoder_model)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.logger">(in module lmflow.models.hf_encoder_decoder_model)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/finetuner/index.html#lmflow.pipeline.finetuner.logger">(in module lmflow.pipeline.finetuner)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.logger">(in module lmflow.pipeline.raft_aligner)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.logger">(in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_alpha">lora_alpha (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_dropout">lora_dropout (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_model_path">lora_model_path (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_r">lora_r (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.lora_target_modules">lora_target_modules (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.low_resource">low_resource (lmflow.args.VisModelArguments attribute)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="M">M</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.map">map() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.map">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.max_eval_samples">max_eval_samples (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.max_new_tokens">max_new_tokens (lmflow.args.EvaluatorArguments attribute)</a> + + <ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.max_new_tokens">(lmflow.args.InferencerArguments attribute)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.max_train_samples">max_train_samples (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.merge_lora_weights">merge_lora_weights() (lmflow.models.hf_decoder_model.HFDecoderModel method)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.merge_lora_weights">(lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.metric">metric (lmflow.args.EvaluatorArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.mixed_precision">mixed_precision (lmflow.args.EvaluatorArguments attribute)</a> + + <ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.mixed_precision">(lmflow.args.InferencerArguments attribute)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.MODEL_CONFIG_CLASSES">MODEL_CONFIG_CLASSES (in module lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.model_name_or_path">model_name_or_path (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.model_revision">model_revision (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.model_type">model_type (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.MODEL_TYPES">MODEL_TYPES (in module lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments">ModelArguments (class in lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.MODELS_SUPPORT_FLASH_ATTENTION">MODELS_SUPPORT_FLASH_ATTENTION (in module lmflow.models.hf_decoder_model)</a> +</li> + <li> + module + + <ul> + <li><a href="autoapi/lmflow/index.html#module-lmflow">lmflow</a> +</li> + <li><a href="api/_autosummary/lmflow.args.html#module-lmflow.args">lmflow.args</a>, <a href="autoapi/lmflow/args/index.html#module-lmflow.args">[1]</a> +</li> + <li><a href="autoapi/lmflow/datasets/index.html#module-lmflow.datasets">lmflow.datasets</a> +</li> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#module-lmflow.datasets.dataset">lmflow.datasets.dataset</a> +</li> + <li><a href="autoapi/lmflow/models/index.html#module-lmflow.models">lmflow.models</a> +</li> + <li><a href="autoapi/lmflow/models/auto_model/index.html#module-lmflow.models.auto_model">lmflow.models.auto_model</a> +</li> + <li><a href="autoapi/lmflow/models/base_model/index.html#module-lmflow.models.base_model">lmflow.models.base_model</a> +</li> + <li><a href="autoapi/lmflow/models/decoder_model/index.html#module-lmflow.models.decoder_model">lmflow.models.decoder_model</a> +</li> + <li><a href="autoapi/lmflow/models/encoder_decoder_model/index.html#module-lmflow.models.encoder_decoder_model">lmflow.models.encoder_decoder_model</a> +</li> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#module-lmflow.models.hf_decoder_model">lmflow.models.hf_decoder_model</a> +</li> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#module-lmflow.models.hf_encoder_decoder_model">lmflow.models.hf_encoder_decoder_model</a> +</li> + <li><a href="autoapi/lmflow/models/interfaces/index.html#module-lmflow.models.interfaces">lmflow.models.interfaces</a> +</li> + <li><a href="autoapi/lmflow/models/interfaces/tunable/index.html#module-lmflow.models.interfaces.tunable">lmflow.models.interfaces.tunable</a> +</li> + <li><a href="autoapi/lmflow/models/regression_model/index.html#module-lmflow.models.regression_model">lmflow.models.regression_model</a> +</li> + <li><a href="autoapi/lmflow/models/text_regression_model/index.html#module-lmflow.models.text_regression_model">lmflow.models.text_regression_model</a> +</li> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#module-lmflow.models.vision2seq_model">lmflow.models.vision2seq_model</a> +</li> + <li><a href="autoapi/lmflow/pipeline/index.html#module-lmflow.pipeline">lmflow.pipeline</a> +</li> + <li><a href="autoapi/lmflow/pipeline/auto_pipeline/index.html#module-lmflow.pipeline.auto_pipeline">lmflow.pipeline.auto_pipeline</a> +</li> + <li><a href="autoapi/lmflow/pipeline/base_aligner/index.html#module-lmflow.pipeline.base_aligner">lmflow.pipeline.base_aligner</a> +</li> + <li><a href="autoapi/lmflow/pipeline/base_pipeline/index.html#module-lmflow.pipeline.base_pipeline">lmflow.pipeline.base_pipeline</a> +</li> + <li><a href="autoapi/lmflow/pipeline/base_tuner/index.html#module-lmflow.pipeline.base_tuner">lmflow.pipeline.base_tuner</a> +</li> + <li><a href="autoapi/lmflow/pipeline/evaluator/index.html#module-lmflow.pipeline.evaluator">lmflow.pipeline.evaluator</a> +</li> + <li><a href="autoapi/lmflow/pipeline/finetuner/index.html#module-lmflow.pipeline.finetuner">lmflow.pipeline.finetuner</a> +</li> + <li><a href="autoapi/lmflow/pipeline/inferencer/index.html#module-lmflow.pipeline.inferencer">lmflow.pipeline.inferencer</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#module-lmflow.pipeline.raft_aligner">lmflow.pipeline.raft_aligner</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/index.html#module-lmflow.pipeline.utils">lmflow.pipeline.utils</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/peft_trainer/index.html#module-lmflow.pipeline.utils.peft_trainer">lmflow.pipeline.utils.peft_trainer</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#module-lmflow.pipeline.utils.raft_trainer">lmflow.pipeline.utils.raft_trainer</a> +</li> + <li><a href="autoapi/lmflow/utils/index.html#module-lmflow.utils">lmflow.utils</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#module-lmflow.utils.constants">lmflow.utils.constants</a> +</li> + <li><a href="autoapi/lmflow/utils/data_utils/index.html#module-lmflow.utils.data_utils">lmflow.utils.data_utils</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/index.html#module-lmflow.utils.flash_attention">lmflow.utils.flash_attention</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.html#module-lmflow.utils.flash_attention.bloom_flash_attention">lmflow.utils.flash_attention.bloom_flash_attention</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.html#module-lmflow.utils.flash_attention.gpt2_flash_attention">lmflow.utils.flash_attention.gpt2_flash_attention</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.html#module-lmflow.utils.flash_attention.gpt_neo_flash_attention">lmflow.utils.flash_attention.gpt_neo_flash_attention</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.html#module-lmflow.utils.flash_attention.llama_flash_attention">lmflow.utils.flash_attention.llama_flash_attention</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/triton_flash_attention/index.html#module-lmflow.utils.flash_attention.triton_flash_attention">lmflow.utils.flash_attention.triton_flash_attention</a> +</li> + <li><a href="autoapi/lmflow/utils/position_interpolation/index.html#module-lmflow.utils.position_interpolation">lmflow.utils.position_interpolation</a> +</li> + <li><a href="autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.html#module-lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch">lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch</a> +</li> + <li><a href="autoapi/lmflow/version/index.html#module-lmflow.version">lmflow.version</a> +</li> + </ul></li> + </ul></td> +</tr></table> + +<h2 id="N">N</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.num_examples">num_examples() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.num_raft_iteration">num_raft_iteration (lmflow.args.RaftAlignerArguments attribute)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="O">O</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_epoch_end">on_epoch_end() (lmflow.pipeline.utils.peft_trainer.PeftSavingCallback method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_save">on_save() (lmflow.pipeline.utils.peft_trainer.PeftSavingCallback method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback.on_train_end">on_train_end() (lmflow.pipeline.utils.peft_trainer.PeftSavingCallback method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.OPTIMIZER_NAME">OPTIMIZER_NAME (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.output_dir">output_dir (lmflow.args.EvaluatorArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.output_max_length">output_max_length (lmflow.args.RaftAlignerArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.output_min_length">output_min_length (lmflow.args.RaftAlignerArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.output_reward_path">output_reward_path (lmflow.args.RaftAlignerArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.overwrite_cache">overwrite_cache (lmflow.args.DatasetArguments attribute)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="P">P</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftSavingCallback">PeftSavingCallback (class in lmflow.pipeline.utils.peft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/peft_trainer/index.html#lmflow.pipeline.utils.peft_trainer.PeftTrainer">PeftTrainer (class in lmflow.pipeline.utils.peft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.PIPELINE_ARGUMENT_MAPPING">PIPELINE_ARGUMENT_MAPPING (in module lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/auto_pipeline/index.html#lmflow.pipeline.auto_pipeline.PIPELINE_MAPPING">PIPELINE_MAPPING (in module lmflow.pipeline.auto_pipeline)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.pop_callback">pop_callback() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.predict">predict() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_loop">prediction_loop() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.prediction_step">prediction_step() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.preprocessing_num_workers">preprocessing_num_workers (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.process_image_flag">process_image_flag() (in module lmflow.utils.data_utils)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.prompt_cache_path">prompt_cache_path (lmflow.args.VisModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.prompt_structure">prompt_structure (lmflow.args.EvaluatorArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.push_to_hub">push_to_hub() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="Q">Q</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.qformer_from_pretrained">qformer_from_pretrained() (lmflow.models.vision2seq_model.CustomAutoVision2SeqModel method)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="R">R</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.raft_batch_size">raft_batch_size (lmflow.args.RaftAlignerArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/raft_aligner/index.html#lmflow.pipeline.raft_aligner.RaftAligner">RaftAligner (class in lmflow.pipeline.raft_aligner)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments">RaftAlignerArguments (class in lmflow.args)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer">RaftTrainer (class in lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.random_seed">random_seed (lmflow.args.EvaluatorArguments attribute)</a> + + <ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.random_seed">(lmflow.args.InferencerArguments attribute)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.random_shuffle">random_shuffle (lmflow.args.EvaluatorArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/models/text_regression_model/index.html#lmflow.models.text_regression_model.TextRegressionModel.register_inference_function">register_inference_function() (lmflow.models.text_regression_model.TextRegressionModel method)</a> +</li> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.register_prompt_cache">register_prompt_cache() (lmflow.models.vision2seq_model.CustomAutoVision2SeqModel method)</a> +</li> + <li><a href="autoapi/lmflow/models/regression_model/index.html#lmflow.models.regression_model.RegressionModel">RegressionModel (class in lmflow.models.regression_model)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.remove_callback">remove_callback() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.repetition_penalty">repetition_penalty (lmflow.args.EvaluatorArguments attribute)</a> + + <ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.repetition_penalty">(lmflow.args.InferencerArguments attribute)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.html#lmflow.utils.flash_attention.bloom_flash_attention.replace_bloom_attn_with_flash_attn">replace_bloom_attn_with_flash_attn() (in module lmflow.utils.flash_attention.bloom_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt2_flash_attention/index.html#lmflow.utils.flash_attention.gpt2_flash_attention.replace_gpt2_attn_with_flash_attn">replace_gpt2_attn_with_flash_attn() (in module lmflow.utils.flash_attention.gpt2_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/gpt_neo_flash_attention/index.html#lmflow.utils.flash_attention.gpt_neo_flash_attention.replace_gpt_neo_attn_with_flash_attn">replace_gpt_neo_attn_with_flash_attn() (in module lmflow.utils.flash_attention.gpt_neo_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/flash_attention/llama_flash_attention/index.html#lmflow.utils.flash_attention.llama_flash_attention.replace_llama_attn_with_flash_attn">replace_llama_attn_with_flash_attn() (in module lmflow.utils.flash_attention.llama_flash_attention)</a> +</li> + <li><a href="autoapi/lmflow/utils/position_interpolation/llama_rope_scaled_monkey_patch/index.html#lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch.replace_llama_with_condense">replace_llama_with_condense() (in module lmflow.utils.position_interpolation.llama_rope_scaled_monkey_patch)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.rope_ntk_ratio">rope_ntk_ratio (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.rope_pi_ratio">rope_pi_ratio (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.rstrip_partial_utf8">rstrip_partial_utf8() (in module lmflow.pipeline.inferencer)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="S">S</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.save">save() (lmflow.models.hf_decoder_model.HFDecoderModel method)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.save">(lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.save_aggregated_lora">save_aggregated_lora (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.save_model">save_model() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.save_prompt_cache">save_prompt_cache() (lmflow.models.vision2seq_model.CustomAutoVision2SeqModel method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.SCALER_NAME">SCALER_NAME (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.SCHEDULER_NAME">SCHEDULER_NAME (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/utils/data_utils/index.html#lmflow.utils.data_utils.set_random_seed">set_random_seed() (in module lmflow.utils.data_utils)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.skip_first_batches">skip_first_batches (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.store_flos">store_flos() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.Inferencer.stream_inference">stream_inference() (lmflow.pipeline.inferencer.Inferencer method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.streaming">streaming (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/inferencer/index.html#lmflow.pipeline.inferencer.supported_dataset_type">supported_dataset_type (in module lmflow.pipeline.inferencer)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="T">T</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.temperature">temperature (lmflow.args.EvaluatorArguments attribute)</a> + + <ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.InferencerArguments.temperature">(lmflow.args.InferencerArguments attribute)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.test_file">test_file (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT2TEXT_DATASET_DESCRIPTION">TEXT2TEXT_DATASET_DESCRIPTION (in module lmflow.utils.constants)</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT2TEXT_DATASET_DETAILS">TEXT2TEXT_DATASET_DETAILS (in module lmflow.utils.constants)</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT2TEXT_DATASET_LONG_DESCRITION">TEXT2TEXT_DATASET_LONG_DESCRITION (in module lmflow.utils.constants)</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT_ONLY_DATASET_DESCRIPTION">TEXT_ONLY_DATASET_DESCRIPTION (in module lmflow.utils.constants)</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT_ONLY_DATASET_DETAILS">TEXT_ONLY_DATASET_DETAILS (in module lmflow.utils.constants)</a> +</li> + <li><a href="autoapi/lmflow/utils/constants/index.html#lmflow.utils.constants.TEXT_ONLY_DATASET_LONG_DESCRITION">TEXT_ONLY_DATASET_LONG_DESCRITION (in module lmflow.utils.constants)</a> +</li> + <li><a href="autoapi/lmflow/models/text_regression_model/index.html#lmflow.models.text_regression_model.TextRegressionModel">TextRegressionModel (class in lmflow.models.text_regression_model)</a> +</li> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.to_dict">to_dict() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.to_dict">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/datasets/index.html#lmflow.datasets.Dataset.to_list">to_list() (lmflow.datasets.Dataset method)</a> + + <ul> + <li><a href="autoapi/lmflow/datasets/dataset/index.html#lmflow.datasets.dataset.Dataset.to_list">(lmflow.datasets.dataset.Dataset method)</a> +</li> + </ul></li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/hf_decoder_model/index.html#lmflow.models.hf_decoder_model.HFDecoderModel.tokenize">tokenize() (lmflow.models.hf_decoder_model.HFDecoderModel method)</a> + + <ul> + <li><a href="autoapi/lmflow/models/hf_encoder_decoder_model/index.html#lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel.tokenize">(lmflow.models.hf_encoder_decoder_model.HFEncoderDecoderModel method)</a> +</li> + </ul></li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.tokenizer_name">tokenizer_name (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.RaftAlignerArguments.top_reward_percentage">top_reward_percentage (lmflow.args.RaftAlignerArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.torch_dtype">torch_dtype (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.torch_jit_model_eval">torch_jit_model_eval() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.train">train() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.train_file">train_file (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.TRAINER_STATE_NAME">TRAINER_STATE_NAME (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.TRAINING_ARGS_NAME">TRAINING_ARGS_NAME (in module lmflow.pipeline.utils.raft_trainer)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/utils/raft_trainer/index.html#lmflow.pipeline.utils.raft_trainer.RaftTrainer.training_step">training_step() (lmflow.pipeline.utils.raft_trainer.RaftTrainer method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.truncate_to_model_max_length">truncate_to_model_max_length (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/models/interfaces/tunable/index.html#lmflow.models.interfaces.tunable.Tunable">Tunable (class in lmflow.models.interfaces.tunable)</a> +</li> + <li><a href="autoapi/lmflow/pipeline/base_tuner/index.html#lmflow.pipeline.base_tuner.BaseTuner.tune">tune() (lmflow.pipeline.base_tuner.BaseTuner method)</a> + + <ul> + <li><a href="autoapi/lmflow/pipeline/finetuner/index.html#lmflow.pipeline.finetuner.Finetuner.tune">(lmflow.pipeline.finetuner.Finetuner method)</a> +</li> + </ul></li> + </ul></td> +</tr></table> + +<h2 id="U">U</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.use_accelerator_for_evaluator">use_accelerator_for_evaluator (lmflow.args.EvaluatorArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_auth_token">use_auth_token (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_fast_tokenizer">use_fast_tokenizer (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_flash_attention">use_flash_attention (lmflow.args.ModelArguments attribute)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_int8">use_int8 (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_lora">use_lora (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments.use_prompt_cache">use_prompt_cache (lmflow.args.VisModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.ModelArguments.use_ram_optimized_load">use_ram_optimized_load (lmflow.args.ModelArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.EvaluatorArguments.use_wandb">use_wandb (lmflow.args.EvaluatorArguments attribute)</a> +</li> + </ul></td> +</tr></table> + +<h2 id="V">V</h2> +<table style="width: 100%" class="indextable genindextable"><tr> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.validation_file">validation_file (lmflow.args.DatasetArguments attribute)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.DatasetArguments.validation_split_percentage">validation_split_percentage (lmflow.args.DatasetArguments attribute)</a> +</li> + </ul></td> + <td style="width: 33%; vertical-align: top;"><ul> + <li><a href="autoapi/lmflow/models/vision2seq_model/index.html#lmflow.models.vision2seq_model.CustomAutoVision2SeqModel.vision_model_from_pretrained">vision_model_from_pretrained() (lmflow.models.vision2seq_model.CustomAutoVision2SeqModel method)</a> +</li> + <li><a href="autoapi/lmflow/args/index.html#lmflow.args.VisModelArguments">VisModelArguments (class in lmflow.args)</a> +</li> + </ul></td> +</tr></table> + + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div class="prev-next-area"> +</div> + </footer> + + </div> + + + + + </div> + <footer class="bd-footer-content"> + <div class="bd-footer-content__inner"></div> + </footer> + + </main> + </div> + </div> + + <!-- Scripts loaded after <body> so the DOM is not blocked --> + <script src="_static/scripts/bootstrap.js?digest=12da95d707ffb74b382d"></script> +<script src="_static/scripts/pydata-sphinx-theme.js?digest=12da95d707ffb74b382d"></script> + + <footer class="bd-footer"> +<div 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id="rtd-footer-container"></div> + + + </div> + + <main id="main-content" class="bd-main"> + + + <div class="bd-content"> + <div class="bd-article-container"> + + <div class="bd-header-article"></div> + + + + +<div id="searchbox"></div> + <article class="bd-article" role="main"> + + <img src="_static/logo.png" alt="LMFlow" style="width: 100%; min-width: 300px; display: block; margin: auto; background-color: transparent;"> +<section id="lmflow"> +<h1>LMFlow<a class="headerlink" href="#lmflow" title="Permalink to this heading">#</a></h1> +<p>An extensible, convenient, and efficient toolbox for finetuning large machine learning models, designed to be user-friendly, speedy and reliable, and accessible to the entire community.</p> +<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils"> +<div class="sd-row sd-row-cols-1 sd-row-cols-xs-1 sd-row-cols-sm-2 sd-row-cols-md-3 sd-row-cols-lg-4 docutils"> +<div class="sd-col sd-d-flex-row docutils"> +<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm docutils"> +<div class="sd-card-body docutils"> +<div class="sd-card-title sd-font-weight-bold docutils"> +Extensible</div> +<p class="sd-card-text">Support common backbones (LLaMA, Galactica, GPT-2, etc.)</p> +</div> +</div> +</div> +<div class="sd-col sd-d-flex-row docutils"> +<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm docutils"> +<div class="sd-card-body docutils"> +<div class="sd-card-title sd-font-weight-bold docutils"> +Light-Weight</div> +<p class="sd-card-text">Extremely few parameters with LoRA (LLaMA 33B: only 25MB storage)</p> +</div> +</div> +</div> +<div class="sd-col sd-d-flex-row docutils"> +<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm docutils"> +<div class="sd-card-body docutils"> +<div class="sd-card-title sd-font-weight-bold docutils"> +Task-Oriented</div> +<p class="sd-card-text">Comparable with ChatGPT on 7B/33B models.</p> +</div> +</div> +</div> +<div class="sd-col sd-d-flex-row docutils"> +<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm docutils"> +<div class="sd-card-body docutils"> +<div class="sd-card-title sd-font-weight-bold docutils"> +Open</div> +<p class="sd-card-text">The whole pipeline (data, models, tuning, inference) is open-source.</p> +</div> +</div> +</div> +</div> +</div> +<section id="introduction"> +<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading">#</a></h2> +<p>The remarkable achievements of large foundation models, such as the expansive language models, have demonstrated their exceptional capacity to attain human-like intelligence that surpasses conventional methods. Despite their growing accessibility, these models still require fine-tuning to cater to specific tasks while maintaining their overall AI competency. We are pleased to introduce our lightweight toolkit, which features thoughtfully designed and easily scalable APIs. This tool simplifies the process of fine-tuning and inference of publicly available foundation models to maximize their effectiveness.</p> +<p>We have thoroughly tested this toolkit and are pleased to make it available under <a class="reference external" href="https://github.com/OptimalScale/LMFlow">Github</a>.</p> +</section> +<section id="features"> +<h2>Features<a class="headerlink" href="#features" title="Permalink to this heading">#</a></h2> +<section id="task-tuning"> +<h3>Task Tuning<a class="headerlink" href="#task-tuning" title="Permalink to this heading">#</a></h3> +<p>The goal of Task Tuning is to enhance a language model’s proficiency in a particular field, such as medicine or mathematics. By doing so, the model acquires domain-specific information, allowing it to adapt better to the target subject matter.</p> +<p>For instance, if a medical dataset is used for task tuning, the language model can gain medical knowledge that can be applied to other medical datasets.</p> +<p>To emphasize its significance, we applied task tuning to LLaMA models on PubMedQA and MedMCQA datasets and evaluated their performance. We observed significant improvements both in-domain (PubMedQA, MedMCQA) and out-of-domain (MedQA-USMLE) dataset.</p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-center"><p></p></th> +<th class="head text-center"><p>PubMedQA</p></th> +<th class="head text-center"><p>MedQA-USMLE</p></th> +<th class="head text-center"><p>MedMCQA</p></th> +<th class="head text-center"><p>Average</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-center"><p>Human (pass)</p></td> +<td class="text-center"><p>60.0</p></td> +<td class="text-center"><p>50.0</p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>Human (expert)</p></td> +<td class="text-center"><p>78.0</p></td> +<td class="text-center"><p>87.0</p></td> +<td class="text-center"><p>90.0</p></td> +<td class="text-center"><p>85.0</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>InstructGPT 175B</p></td> +<td class="text-center"><p>73.2</p></td> +<td class="text-center"><p>46.0</p></td> +<td class="text-center"><p>44.0</p></td> +<td class="text-center"><p>54.4</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>ChatGPT</p></td> +<td class="text-center"><p>63.9</p></td> +<td class="text-center"><p><strong>57.0</strong></p></td> +<td class="text-center"><p>44.7</p></td> +<td class="text-center"><p>55.2</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>LLaMA 7B</p></td> +<td class="text-center"><p>5.2</p></td> +<td class="text-center"><p>27.1</p></td> +<td class="text-center"><p>24.3</p></td> +<td class="text-center"><p>18.9</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>LLaMA 33B</p></td> +<td class="text-center"><p>1.8</p></td> +<td class="text-center"><p>43.4</p></td> +<td class="text-center"><p>30.3</p></td> +<td class="text-center"><p>25.2</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>Task-tuned LLaMA 7B (Full)</p></td> +<td class="text-center"><p><strong>75.1</strong></p></td> +<td class="text-center"><p>44.5</p></td> +<td class="text-center"><p>49.9</p></td> +<td class="text-center"><p>56.5</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>Task-tuned LLaMA 33B (LoRA)</p></td> +<td class="text-center"><p>74</p></td> +<td class="text-center"><p>51.3</p></td> +<td class="text-center"><p><strong>50.2</strong></p></td> +<td class="text-center"><p><strong>58.5</strong></p></td> +</tr> +</tbody> +</table> +<p>Moreover, we also test the MMLU performance to further verify the out-of-domain robustness of Task Tuning technique.</p> +<table class="table"> +<thead> +<tr class="row-odd"><th class="head text-center"><p>MMLU task</p></th> +<th class="head text-center"><p>anatomy</p></th> +<th class="head text-center"><p>clinical knowledge</p></th> +<th class="head text-center"><p>college biology</p></th> +<th class="head text-center"><p>college medicine</p></th> +<th class="head text-center"><p>medical genetics</p></th> +<th class="head text-center"><p>professional medicine</p></th> +</tr> +</thead> +<tbody> +<tr class="row-even"><td class="text-center"><p>LLaMA 33B</p></td> +<td class="text-center"><p>39.2</p></td> +<td class="text-center"><p>40.3</p></td> +<td class="text-center"><p>44.4</p></td> +<td class="text-center"><p>32.9</p></td> +<td class="text-center"><p>36</p></td> +<td class="text-center"><p>43.0</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>Galactica 30B</p></td> +<td class="text-center"><p>32.5</p></td> +<td class="text-center"><p>26</p></td> +<td class="text-center"><p>30.5</p></td> +<td class="text-center"><p>25.4</p></td> +<td class="text-center"><p>39</p></td> +<td class="text-center"><p>23.1</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>Galactica 120B</p></td> +<td class="text-center"><p>58.5</p></td> +<td class="text-center"><p>59.2</p></td> +<td class="text-center"><p>68.7</p></td> +<td class="text-center"><p>57.2</p></td> +<td class="text-center"><p>68.0</p></td> +<td class="text-center"><p>59.6</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>OPT 175B</p></td> +<td class="text-center"><p>28.9</p></td> +<td class="text-center"><p>21.9</p></td> +<td class="text-center"><p>30.6</p></td> +<td class="text-center"><p>-</p></td> +<td class="text-center"><p>35.0</p></td> +<td class="text-center"><p>27.9</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>BLOOM 176B</p></td> +<td class="text-center"><p>37</p></td> +<td class="text-center"><p>29.8</p></td> +<td class="text-center"><p>28.5</p></td> +<td class="text-center"><p>-</p></td> +<td class="text-center"><p>36.0</p></td> +<td class="text-center"><p>25.4</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p>Gopher 280B</p></td> +<td class="text-center"><p>56.3</p></td> +<td class="text-center"><p>67.2</p></td> +<td class="text-center"><p>70.8</p></td> +<td class="text-center"><p>60.1</p></td> +<td class="text-center"><p>69.0</p></td> +<td class="text-center"><p>64.0</p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>GPT3.5 175B</p></td> +<td class="text-center"><p>56.3</p></td> +<td class="text-center"><p>69.8</p></td> +<td class="text-center"><p>72.2</p></td> +<td class="text-center"><p>61.3</p></td> +<td class="text-center"><p>70</p></td> +<td class="text-center"><p>70.2</p></td> +</tr> +<tr class="row-odd"><td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +<td class="text-center"><p></p></td> +</tr> +<tr class="row-even"><td class="text-center"><p>Task-tuned LLaMA 33B (LoRA)</p></td> +<td class="text-center"><p>51.8</p></td> +<td class="text-center"><p>65.2</p></td> +<td class="text-center"><p>70.1</p></td> +<td class="text-center"><p>58.3</p></td> +<td class="text-center"><p>65.6</p></td> +<td class="text-center"><p>66.5</p></td> +</tr> +</tbody> +</table> +</section> +<section id="instruction-tuning"> +<h3>Instruction Tuning<a class="headerlink" href="#instruction-tuning" title="Permalink to this heading">#</a></h3> +<p>Instruction Tuning is a technique used to improve the performance of language models by training them to follow natural language commands or instructions. This includes positive or negative examples, prompts, constraints, and other elements that are commonly found in human language. The main goal of instruction-tuning is to improve the model’s ability to perform well on multiple tasks and to generalize more effectively to new or unseen tasks. This is achieved by teaching the model to understand and incorporate various language cues and constraints that are relevant to the task at hand. Instruction-tuning is a powerful technique that is widely used in natural language processing, machine learning, and related areas. By improving the ability of language models to understand and follow natural language commands, this approach can help to unlock new levels of performance and productivity in a wide range of applications.</p> +<p>We list some examples below. Full example list is saved as a <a class="reference external" href="https://github.com/OptimalScale/LMFlow/blob/main/docs/source/_static/check_before_after_lora_tuning.jsonl">Jsonl file</a>.</p> +<p><img alt="Instruction Tuning Sample" src="_images/IT_sample1.png" /></p> +<p><img alt="Instruction Tuning Sample" src="_images/IT_sample2.png" /></p> +<p><img alt="Instruction Tuning Sample" src="_images/IT_sample3.png" /></p> +<p><img alt="Instruction Tuning Sample" src="_images/IT_sample4.png" /></p> +<p><img alt="Instruction Tuning Sample" src="_images/IT_sample5.png" /></p> +<p><img alt="Instruction Tuning Sample" src="_images/IT_sample6.png" /></p> +<p><img alt="Instruction Tuning Sample" src="_images/IT_sample7.png" /></p> +</section> +</section> +<section id="installation"> +<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">#</a></h2> +<p>This package can be be installed from sources with the following command:</p> +<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/OptimalScale/LMFlow.git + +conda<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>lmflow<span class="w"> </span><span class="nv">python</span><span class="o">=</span><span class="m">3</span>.9<span class="w"> </span>-y + +conda<span class="w"> </span>activate<span class="w"> </span>lmflow + +conda<span class="w"> </span>install<span class="w"> </span>mpi4py + +pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>. +</pre></div> +</div> +</section> +<section id="checkpoints"> +<h2>Checkpoints<a class="headerlink" href="#checkpoints" title="Permalink to this heading">#</a></h2> +<p>We have prepared tuned LLaMA model (both task and instruction tuning).</p> +<p>Refer to <a class="reference external" href="https://github.com/OptimalScale/LMFlow/blob/main/README.md">README</a>.</p> +</section> +<section id="content"> +<h2>Content<a class="headerlink" href="#content" title="Permalink to this heading">#</a></h2> +<div class="toctree-wrapper compound"> +<ul> +<li class="toctree-l1"><a class="reference internal" href="blogs/index.html">Blogs</a></li> +</ul> +</div> +<div class="toctree-wrapper compound"> +<ul> +<li class="toctree-l1"><a class="reference internal" href="examples/index.html">Examples</a><ul> +<li class="toctree-l2"><a class="reference internal" href="examples/index.html#data-preparation">Data preparation</a></li> +<li class="toctree-l2"><a class="reference internal" href="examples/index.html#finetuning">Finetuning</a></li> +<li class="toctree-l2"><a class="reference internal" href="examples/index.html#inference">Inference</a></li> +<li class="toctree-l2"><a class="reference internal" href="examples/index.html#evaluation">Evaluation</a></li> +</ul> +</li> +</ul> +</div> +<div class="toctree-wrapper compound"> +<ul> +<li class="toctree-l1"><a class="reference internal" href="autoapi/index.html">API Reference</a><ul> +<li class="toctree-l2"><a class="reference internal" href="autoapi/lmflow/index.html"><code class="xref py py-mod docutils literal notranslate"><span class="pre">lmflow</span></code></a></li> +</ul> +</li> +</ul> +</div> +<div class="toctree-wrapper compound"> +<ul> +<li class="toctree-l1"><a class="reference internal" href="about/index.html">About</a><ul> +<li class="toctree-l2"><a class="reference internal" href="about/changelog.html">Changelog</a></li> +<li class="toctree-l2"><a class="reference internal" href="about/authors.html">Contributors</a></li> +</ul> +</li> +</ul> +</div> +</section> +<section id="citation"> +<h2>Citation<a class="headerlink" href="#citation" title="Permalink to this heading">#</a></h2> +<div class="highlight-bibtex notranslate"><div class="highlight"><pre><span></span><span class="nc">@misc</span><span class="p">{</span><span class="nl">lmflow</span><span class="p">,</span> +<span class="w"> </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang}</span><span class="p">,</span> +<span class="w"> </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models}</span><span class="p">,</span> +<span class="w"> </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2023}</span><span class="p">,</span> +<span class="w"> </span><span class="na">publisher</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{GitHub}</span><span class="p">,</span> +<span class="w"> </span><span class="na">journal</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{GitHub repository}</span><span class="p">,</span> +<span class="w"> </span><span class="na">howpublished</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{\url{https://optimalscale.github.io/LMFlow/}}</span><span class="p">,</span> +<span class="p">}</span> +</pre></div> +</div> +</section> +<section id="disclaimer"> +<h2>Disclaimer<a class="headerlink" href="#disclaimer" title="Permalink to this heading">#</a></h2> +<p>This package aims to provide a streamlined and user-friendly pipeline for large model tuning. Its functionalities serve as a reference and are intended for use by the user. However, it is important to note that the responsibility for the preparation of the data and pretrained models lies solely with the user. This package does not guarantee the accuracy, completeness, applicability, or legality of the components from the user’s preparation. Users must be aware of and assume all risks and liabilities associated with the preparation of the models and data, and obtain legal, commercial, and technical advice before utilizing this package. The pipeline shall not be held responsible for any direct, indirect, special, incidental, or consequential damages resulting from the user’s improper preparation of the data and pretrained models.</p> +<p>It is also crucial to highlight that the results generated by the model are based on probabilistic models and not directly related to this pipeline. The accuracy, reliability, applicability, and legality of the results are not guaranteed by this pipeline. Therefore, users must also be aware of the risks and liabilities associated with the results and seek legal, commercial, and technical advice before relying on the model-generated outcomes. This pipeline shall not be accountable for any direct, indirect, special, incidental, or consequential damages resulting from the user’s reliance on the model-generated results.</p> +</section> +<section id="support"> +<h2>Support<a class="headerlink" href="#support" title="Permalink to this heading">#</a></h2> +<p>If you need any help, please submit a <a class="reference external" href="https://github.com/OptimalScale/LMFlow">Github</a> issue.</p> +</section> +<section id="indices-and-tables"> +<h2>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this heading">#</a></h2> +<ul class="simple"> +<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li> +<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li> +</ul> +</section> +</section> + + + </article> + + + + <footer class="bd-footer-article"> + <!-- Previous / next buttons --> +<div 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href="#task-tuning">Task Tuning</a></li> +<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#instruction-tuning">Instruction Tuning</a></li> +</ul> +</li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#installation">Installation</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#checkpoints">Checkpoints</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#content">Content</a><ul class="nav section-nav flex-column"> +</ul> +</li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#citation">Citation</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#disclaimer">Disclaimer</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#support">Support</a></li> +<li class="toc-h2 nav-item toc-entry"><a class="reference internal 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href="autoapi/lmflow/utils/constants/index.html#module-lmflow.utils.constants"><code class="xref">lmflow.utils.constants</code></a></td><td> + <em></em></td></tr> + <tr class="cg-1"> + <td></td> + <td>&#160;&#160;&#160; + <a href="autoapi/lmflow/utils/data_utils/index.html#module-lmflow.utils.data_utils"><code class="xref">lmflow.utils.data_utils</code></a></td><td> + <em></em></td></tr> + <tr class="cg-1"> + <td></td> + <td>&#160;&#160;&#160; + <a href="autoapi/lmflow/utils/flash_attention/index.html#module-lmflow.utils.flash_attention"><code class="xref">lmflow.utils.flash_attention</code></a></td><td> + <em></em></td></tr> + <tr class="cg-1"> + <td></td> + <td>&#160;&#160;&#160; + <a href="autoapi/lmflow/utils/flash_attention/bloom_flash_attention/index.html#module-lmflow.utils.flash_attention.bloom_flash_attention"><code class="xref">lmflow.utils.flash_attention.bloom_flash_attention</code></a></td><td> + <em></em></td></tr> + <tr class="cg-1"> + <td></td> + <td>&#160;&#160;&#160; 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