Update Examples to New UX

examples/llama7b_quantize_sparse_cnn.py

@@ -0,0 +1,74 @@
+import torch
+from datasets import load_dataset
+
+from sparseml.transformers import (
+    SparseAutoModelForCausalLM,
+    SparseAutoTokenizer,
+    oneshot,
+)
+
+
+# define a sparseml recipe for GPTQ W4A16 quantization
+recipe = """
+quant_stage:
+    quant_modifiers:
+        GPTQModifier:
+            sequential_update: false
+            ignore: ["lm_head"]
+            config_groups:
+                group_0:
+                    weights:
+                        num_bits: 4
+                        type: "int"
+                        symmetric: true
+                        strategy: "channel"
+                    targets: ["Linear"]
+"""
+
+# load in a 50% sparse model with 2:4 sparsity structure
+# setting device_map to auto to spread the model evenly across all available GPUs
+model_stub = "neuralmagic/SparseLlama-2-7b-cnn-daily-mail-pruned_50.2of4"
+model = SparseAutoModelForCausalLM.from_pretrained(
+    model_stub, torch_dtype=torch.bfloat16, device_map="auto"
+)
+tokenizer = SparseAutoTokenizer.from_pretrained(model_stub)
+
+# for quantization calibration, we will use a subset of the dataset that was used to
+# sparsify and finetune the model
+dataset = load_dataset("abisee/cnn_dailymail", "1.0.0", split="train[:5%]")
+
+# set dataset config parameters
+max_seq_length = 4096
+pad_to_max_length = False
+num_calibration_samples = 1024
+
+
+# preprocess the data into a single text entry, then tokenize the dataset
+def process_sample(sample):
+    formatted = "Article:\n{}\n\n### Summarization:\n{}".format(
+        sample["article"], sample["highlights"]
+    )
+    return tokenizer(
+        formatted, padding=pad_to_max_length, max_length=max_seq_length, truncation=True
+    )
+
+
+tokenized_dataset = dataset.map(
+    process_sample, remove_columns=["article", "highlights", "id"]
+)
+
+# save location of quantized model out
+output_dir = "./llama7b_sparse_24_w4a16_channel_compressed"
+
+# apply quantization recipe to the model and save quantized output int4 packed format
+# the sparsity structure of the original model will be maintained
+oneshot(
+    model=model,
+    dataset=tokenized_dataset,
+    recipe=recipe,
+    output_dir=output_dir,
+    max_seq_length=max_seq_length,
+    pad_to_max_length=pad_to_max_length,
+    num_calibration_samples=num_calibration_samples,
+    save_compressed=True,
+)

examples/llama7b_sparse_quantized/2:4_w4a16_recipe.yaml

@@ -22,7 +22,8 @@ finetuning_stage:
 quantization_stage:
   run_type: oneshot
   quantization_modifiers:
-    vLLMQuantizationModifier:
+    GPTQModifier:
+      sequential_update: false
       ignore: ["lm_head"]
       config_groups:
         group_0:
@@ -32,7 +33,3 @@ quantization_stage:
             symmetric: true
             strategy: "channel"
           targets: ["Linear"]
-    SparseGPTModifier:
-      sparsity: 0.0
-      quantize: True
-      sequential_update: false

examples/llama7b_w4a16_quantization.ipynb

@@ -25,10 +25,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "SparseML uses recipes to define configurations for different oneshot algorithms. Recipes can be defined as a string or a yaml file. Below we create a sample recipe for GPTQ quantization. The recipe is made up of two different algorithms, called modifiers.\n",
+    "SparseML uses recipes to define configurations for different oneshot algorithms. Recipes can be defined as a string or a yaml file. A recipe consists of one or more sparsification or quantization algorithms, called modifiers in SparseML. Below we create a sample recipe for GPTQ quantization that only requires a single modifier.\n",
     "\n",
-    "1. **vLLMQuantizationModifier**: calibrates the model for quantization by calculating scale and zero points from a small amount of calibration data\n",
-    "2. **SparseGPTModifier**: applies the GPTQ algorithm, using the result of the vLLMQuantizationModifier to determine the best quantization bin to place each linear weight into"
+    "This modifier specifies that we should quantize the weights of each linear layer to 4 bits, using a symmetric channelwise quantization pattern. The lm-head will not be quantized even though it is a Linear layer, because it is included in the ignore list."
    ]
   },
   {
@@ -37,10 +36,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "recipe=\"\"\"\n",
+    "recipe = \"\"\"\n",
     "quant_stage:\n",
     "    quant_modifiers:\n",
-    "        vLLMQuantizationModifier:\n",
+    "        GPTQModifier:\n",
+    "            sequential_update: false\n",
     "            ignore: [\"lm_head\"]\n",
     "            config_groups:\n",
     "                group_0:\n",
@@ -50,10 +50,6 @@
     "                        symmetric: true\n",
     "                        strategy: \"channel\"\n",
     "                    targets: [\"Linear\"]\n",
-    "        SparseGPTModifier:\n",
-    "            sparsity: 0.0\n",
-    "            quantize: True\n",
-    "            sequential_update: false\n",
     "\"\"\""
    ]
   },

examples/llama7b_w4a16_quantization.py

@@ -7,7 +7,8 @@
 recipe = """
 quant_stage:
     quant_modifiers:
-        vLLMQuantizationModifier:
+        GPTQModifier:
+            sequential_update: false
             ignore: ["lm_head"]
             config_groups:
                 group_0:
@@ -17,10 +18,6 @@
                         symmetric: true
                         strategy: "channel"
                     targets: ["Linear"]
-        SparseGPTModifier:
-            sparsity: 0.0
-            quantize: true
-            sequential_update: false
 """
 
 # setting device_map to auto to spread the model evenly across all available GPUs

examples/llama7b_w8a8_quantization.py

@@ -7,7 +7,8 @@
 recipe = """
 quant_stage:
     quant_modifiers:
-        vLLMQuantizationModifier:
+        GPTQModifier:
+            sequential_update: false
             ignore: ["lm_head"]
             config_groups:
                 group_0:
@@ -23,10 +24,6 @@
                         dynamic: True
                         strategy: "token"
                     targets: ["Linear"]
-        SparseGPTModifier:
-            sparsity: 0.0
-            quantize: true
-            sequential_update: false
 """
 
 # setting device_map to auto to spread the model evenly across all available GPUs
@@ -40,7 +37,7 @@
 dataset = "ultrachat-200k"
 
 # save location of quantized model out
-output_dir = "./output_llama7b_w8a8_channel_compressed"
+output_dir = "./output_llama7b_w8a8_channel_dynamic_compressed"
 
 # set dataset config parameters
 splits = {"calibration": "train_gen[:5%]"}