Add formatting function alpaca

README.md

@@ -24,7 +24,10 @@ pip install -e ".[aim]"
 ```
 
 ## Data format
-The data format expectation is a single column text. The trainer is configured to expect a response template as a string. For example, if one wants to prepare the `alpaca` format data to feed into this trainer, it is quite easy and can be done with the following code.
+We support two data formats:
+
+1. #### Pre-process the JSON/JSONL dataset
+ Pre-process the JSON/JSONL dataset to contain a single sequence of each data instance containing input + Response. The trainer is configured to expect a response template as a string. For example, if one wants to prepare the `alpaca` format data to feed into this trainer, it is quite easy and can be done with the following code.
 
 ```python
 PROMPT_DICT = {
@@ -56,6 +59,24 @@ The `response template` corresponding to the above dataset and the `Llama` token
 
 The same way can be applied to any dataset, with more info can be found [here](https://huggingface.co/docs/trl/main/en/sft_trainer#format-your-input-prompts).
 
+Once the JSON is converted using the formatting function, pass the `dataset_text_field` containing the single sequence to the trainer. 
+
+2.  #### Format JSON/JSONL on the fly
+   Pass a JSON/JSONL and a `data_formatter_template` to use the formatting function on the fly while tuning. The template should specify fields of JSON with `{{field}}`. While tuning, the data will be converted to a single sequence using the template.  
+   JSON fields can contain alpha-numeric characters, spaces and the following special symbols - "." , "_", "-".  
+
+Example: Train.json
+`[{ "input" : <text>,
+    "output" : <text>,
+  },
+ ...
+]`  
+data_formatter_template: `### Input: {{input}} \n\n##Label: {{output}}`  
+
+Formatting will happen on the fly while tuning. The keys in template should match fields in JSON file. The `response template` corresponding to the above template will need to be supplied. in this case, `response template` = `\n## Label:`.
+
+
+##### In conclusion, either the `data_formatter_template` argument or `dataset_text_field` needs to be supplied to the trainer. 
 
 ## Supported Models
 
@@ -64,12 +85,16 @@ Current supported and tested models are `Llama2` (7 and 13B configurations have
 ## Training
 
 ### Single GPU
+
+1. Using pre-processed dataset for training. 
+
 ```bash
 # if you want to use one GPU on multi-gpu machine
 export CUDA_VISIBLE_DEVICES=0
 
 # MODEL_PATH=meta-llama/Llama-2-7b-hf # Huggingface model id or path to a checkpoint
 # TRAIN_DATA_PATH=twitter_complaints.json # Path to the dataset
+                  # contains data in single sequence {"output": "### Input: text \n\n### Response: text"}
 # OUTPUT_PATH=out # Path to the output folder where the checkpoints are saved
 
 python tuning/sft_trainer.py  \
@@ -94,6 +119,39 @@ python tuning/sft_trainer.py  \
 
 ```
 
+2. Using formatter with JSON/JSONL files
+
+```bash
+# if you want to use one GPU on multi-gpu machine
+export CUDA_VISIBLE_DEVICES=0
+
+# MODEL_PATH=meta-llama/Llama-2-7b-hf # Huggingface model id or path to a checkpoint
+# TRAIN_DATA_PATH=twitter_complaints.json # Path to the dataset
+                  # contains data in form of [{"input": text , "output": text}]
+# OUTPUT_PATH=out # Path to the output folder where the checkpoints are saved
+
+python tuning/sft_trainer.py  \
+--model_name_or_path $MODEL_PATH  \
+--training_data_path $TRAIN_DATA_PATH  \
+--output_dir $OUTPUT_PATH  \
+--num_train_epochs 5  \
+--per_device_train_batch_size 4  \
+--per_device_eval_batch_size 4  \
+--gradient_accumulation_steps 4  \
+--eval_strategy "no"  \
+--save_strategy "epoch"  \
+--learning_rate 1e-5  \
+--weight_decay 0.  \
+--warmup_ratio 0.03  \
+--lr_scheduler_type "cosine"  \
+--logging_steps 1  \
+--include_tokens_per_second  \
+--packing False  \
+--response_template "\n## Label:"  \
+--data_formatter_template: "### Input: {{input}} \n\n##Label: {{output}}"
+
+```
+
 ### Multiple GPUs with FSDP
 
 The recommendation is to use [huggingface accelerate](https://huggingface.co/docs/accelerate/en/index) to launch multi-gpu jobs, in particular when using FSDP:

tests/data/__init__.py

@@ -20,5 +20,6 @@
 ### Constants used for data
 DATA_DIR = os.path.join(os.path.dirname(__file__))
 TWITTER_COMPLAINTS_DATA = os.path.join(DATA_DIR, "twitter_complaints_small.json")
+TWITTER_COMPLAINTS_JSON_FORMAT = os.path.join(DATA_DIR, "twitter_complaints_json.json")
 EMPTY_DATA = os.path.join(DATA_DIR, "empty_data.json")
 MALFORMATTED_DATA = os.path.join(DATA_DIR, "malformatted_data.json")

tests/data/twitter_complaints_json.json

@@ -0,0 +1,12 @@
+[
+    {"Tweet text":"@HMRCcustomers No this is my first job","ID":0,"Label":2,"text_label":"no complaint","output":"### Text: @HMRCcustomers No this is my first job\n\n### Label: no complaint"},
+    {"Tweet text":"@KristaMariePark Thank you for your interest! If you decide to cancel, you can call Customer Care at 1-800-NYTIMES.","ID":1,"Label":2,"text_label":"no complaint","output":"### Text: @KristaMariePark Thank you for your interest! If you decide to cancel, you can call Customer Care at 1-800-NYTIMES.\n\n### Label: no complaint"},
+    {"Tweet text":"If I can't get my 3rd pair of @beatsbydre powerbeats to work today I'm doneski man. This is a slap in my balls. Your next @Bose @BoseService","ID":2,"Label":1,"text_label":"complaint","output":"### Text: If I can't get my 3rd pair of @beatsbydre powerbeats to work today I'm doneski man. This is a slap in my balls. Your next @Bose @BoseService\n\n### Label: complaint"},
+    {"Tweet text":"@EE On Rosneath Arial having good upload and download speeds but terrible latency 200ms. Why is this.","ID":3,"Label":1,"text_label":"complaint","output":"### Text: @EE On Rosneath Arial having good upload and download speeds but terrible latency 200ms. Why is this.\n\n### Label: complaint"},
+    {"Tweet text":"Couples wallpaper, so cute. :) #BrothersAtHome","ID":4,"Label":2,"text_label":"no complaint","output":"### Text: Couples wallpaper, so cute. :) #BrothersAtHome\n\n### Label: no complaint"},
+    {"Tweet text":"@mckelldogs This might just be me, but-- eyedrops? Artificial tears are so useful when you're sleep-deprived and sp\u2026 https:\/\/t.co\/WRtNsokblG","ID":5,"Label":2,"text_label":"no complaint","output":"### Text: @mckelldogs This might just be me, but-- eyedrops? Artificial tears are so useful when you're sleep-deprived and sp\u2026 https:\/\/t.co\/WRtNsokblG\n\n### Label: no complaint"},
+    {"Tweet text":"@Yelp can we get the exact calculations for a business rating (for example if its 4 stars but actually 4.2) or do we use a 3rd party site?","ID":6,"Label":2,"text_label":"no complaint","output":"### Text: @Yelp can we get the exact calculations for a business rating (for example if its 4 stars but actually 4.2) or do we use a 3rd party site?\n\n### Label: no complaint"},
+    {"Tweet text":"@nationalgridus I have no water and the bill is current and paid. Can you do something about this?","ID":7,"Label":1,"text_label":"complaint","output":"### Text: @nationalgridus I have no water and the bill is current and paid. Can you do something about this?\n\n### Label: complaint"},
+    {"Tweet text":"Never shopping at @MACcosmetics again. Every time I go in there, their employees are super rude\/condescending. I'll take my $$ to @Sephora","ID":8,"Label":1,"text_label":"complaint","output":"### Text: Never shopping at @MACcosmetics again. Every time I go in there, their employees are super rude\/condescending. I'll take my $$ to @Sephora\n\n### Label: complaint"},
+    {"Tweet text":"@JenniferTilly Merry Christmas to as well. You get more stunning every year \ufffd\ufffd","ID":9,"Label":2,"text_label":"no complaint","output":"### Text: @JenniferTilly Merry Christmas to as well. You get more stunning every year \ufffd\ufffd\n\n### Label: no complaint"}
+]

tests/test_sft_trainer.py

@@ -29,7 +29,12 @@
 
 # First Party
 from scripts.run_inference import TunedCausalLM
-from tests.data import EMPTY_DATA, MALFORMATTED_DATA, TWITTER_COMPLAINTS_DATA
+from tests.data import (
+    EMPTY_DATA,
+    MALFORMATTED_DATA,
+    TWITTER_COMPLAINTS_DATA,
+    TWITTER_COMPLAINTS_JSON_FORMAT,
+)
 
 # Local
 from tuning import sft_trainer
@@ -114,6 +119,70 @@ def test_run_causallm_pt_and_inference():
         assert "### Text: @NortonSupport Thanks much.\n\n### Label:" in output_inference
 
 
+def test_run_causallm_pt_and_inference_with_formatting_data():
+    """Check if we can bootstrap and peft tune causallm models
+    This test needs the trainer to format data to a single sequence internally.
+    """
+    with tempfile.TemporaryDirectory() as tempdir:
+        data_formatting_args = copy.deepcopy(DATA_ARGS)
+        data_formatting_args.dataset_text_field = None
+        data_formatting_args.data_formatter_template = (
+            "### Text: {{Tweet text}} \n\n### Label: {{text_label}}"
+        )
+
+        train_args = copy.deepcopy(TRAIN_ARGS)
+        train_args.output_dir = tempdir
+
+        sft_trainer.train(MODEL_ARGS, data_formatting_args, train_args, PEFT_PT_ARGS)
+
+        # validate peft tuning configs
+        _validate_training(tempdir)
+        checkpoint_path = _get_checkpoint_path(tempdir)
+        adapter_config = _get_adapter_config(checkpoint_path)
+        _validate_adapter_config(adapter_config, "PROMPT_TUNING", PEFT_PT_ARGS)
+
+        # Load the model
+        loaded_model = TunedCausalLM.load(checkpoint_path)
+
+        # Run inference on the text
+        output_inference = loaded_model.run(
+            "### Text: @NortonSupport Thanks much.\n\n### Label:", max_new_tokens=50
+        )
+        assert len(output_inference) > 0
+        assert "### Text: @NortonSupport Thanks much.\n\n### Label:" in output_inference
+
+
+def test_run_causallm_pt_and_inference_JSON_file_formatter():
+    """Check if we can bootstrap and peft tune causallm models with JSON train file format"""
+    with tempfile.TemporaryDirectory() as tempdir:
+        train_args = copy.deepcopy(TRAIN_ARGS)
+        train_args.output_dir = tempdir
+        data_args = copy.deepcopy(DATA_ARGS)
+        data_args.training_data_path = TWITTER_COMPLAINTS_JSON_FORMAT
+        data_args.dataset_text_field = None
+        data_args.data_formatter_template = (
+            "### Text: {{Tweet text}} \n\n### Label: {{text_label}}"
+        )
+
+        sft_trainer.train(MODEL_ARGS, data_args, train_args, PEFT_PT_ARGS)
+
+        # validate peft tuning configs
+        _validate_training(tempdir)
+        checkpoint_path = _get_checkpoint_path(tempdir)
+        adapter_config = _get_adapter_config(checkpoint_path)
+        _validate_adapter_config(adapter_config, "PROMPT_TUNING", PEFT_PT_ARGS)
+
+        # Load the model
+        loaded_model = TunedCausalLM.load(checkpoint_path)
+
+        # Run inference on the text
+        output_inference = loaded_model.run(
+            "### Text: @NortonSupport Thanks much.\n\n### Label:", max_new_tokens=50
+        )
+        assert len(output_inference) > 0
+        assert "### Text: @NortonSupport Thanks much.\n\n### Label:" in output_inference
+
+
 def test_run_causallm_pt_init_text():
     """Check if we can bootstrap and peft tune causallm models with init text as 'TEXT'"""
     with tempfile.TemporaryDirectory() as tempdir:
@@ -174,6 +243,23 @@ def test_run_causallm_pt_with_validation():
         _validate_training(tempdir, check_eval=True)
 
 
+def test_run_causallm_pt_with_validation_data_formatting():
+    """Check if we can bootstrap and peft tune causallm models with validation dataset"""
+    with tempfile.TemporaryDirectory() as tempdir:
+        train_args = copy.deepcopy(TRAIN_ARGS)
+        train_args.output_dir = tempdir
+        train_args.eval_strategy = "epoch"
+        data_args = copy.deepcopy(DATA_ARGS)
+        data_args.validation_data_path = TWITTER_COMPLAINTS_DATA
+        data_args.dataset_text_field = None
+        data_args.data_formatter_template = (
+            "### Text: {{Tweet text}} \n\n### Label: {{text_label}}"
+        )
+
+        sft_trainer.train(MODEL_ARGS, data_args, train_args, PEFT_PT_ARGS)
+        _validate_training(tempdir, check_eval=True)
+
+
 ############################# Lora Tests #############################
 
 target_modules_val_map = [
@@ -335,6 +421,30 @@ def test_invalid_dataset_text_field():
         sft_trainer.train(MODEL_ARGS, data_args, TRAIN_ARGS, PEFT_PT_ARGS)
 
 
+### Tests that giving dataset_text_field as well as formatter template gives error
+def test_invalid_dataset_text_field_and_formatter_template():
+    """Only one of dataset_text_field or formatter can be supplied"""
+    data_args = copy.deepcopy(DATA_ARGS)
+    data_args.data_formatter_template = (
+        "### Text: {{Tweet text}} \n\n### Label: {{text_label}}"
+    )
+
+    with pytest.raises(ValueError):
+        sft_trainer.train(MODEL_ARGS, data_args, TRAIN_ARGS, PEFT_PT_ARGS)
+
+
+### Tests passing formatter with invalid keys gives error
+def test_invalid_formatter_template():
+    data_args = copy.deepcopy(DATA_ARGS)
+    data_args.dataset_text_field = None
+    data_args.data_formatter_template = (
+        "### Text: {{not found}} \n\n### Label: {{text_label}}"
+    )
+
+    with pytest.raises(KeyError):
+        sft_trainer.train(MODEL_ARGS, data_args, TRAIN_ARGS, PEFT_PT_ARGS)
+
+
 ### Tests for bad training data (i.e., data_path is an unhappy value or points to an unhappy thing)
 def test_malformatted_data():
     """Ensure that malformatted data explodes due to failure to generate the dataset."""
@@ -382,13 +492,15 @@ def test_run_causallm_lora_with_invalid_modules():
 
 
 ### Direct validation tests based on whether or not packing is enabled
-def test_no_packing_needs_dataset_text_field():
+def test_no_packing_needs_dataset_text_field_or_data_formatter_template():
     """Ensure we need to set the dataset text field if packing is False"""
     with tempfile.TemporaryDirectory() as tempdir:
         train_args = copy.deepcopy(TRAIN_ARGS)
         train_args.output_dir = tempdir
         data_args = copy.deepcopy(DATA_ARGS)
+        # One of dataset_text_field or data_formatter_template should be set
         data_args.dataset_text_field = None
+        data_args.data_formatter_template = None
 
         with pytest.raises(ValueError):
             sft_trainer.train(MODEL_ARGS, data_args, train_args, PEFT_PT_ARGS)

tests/utils/test_data_utils.py

@@ -0,0 +1,66 @@
+# Copyright The FMS HF Tuning Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# SPDX-License-Identifier: Apache-2.0
+# https://spdx.dev/learn/handling-license-info/
+
+# Third Party
+import datasets
+import pytest
+
+# First Party
+from tests.data import TWITTER_COMPLAINTS_DATA
+
+# Local
+from tuning.utils import data_utils
+
+
+def test_apply_custom_formatting_template():
+    json_dataset = datasets.load_dataset("json", data_files=TWITTER_COMPLAINTS_DATA)
+    template = "### Input: {{Tweet text}} \n\n ### Response: {{text_label}}"
+    # First response from the data file that is read.
+    expected_response = (
+        "### Input: @HMRCcustomers No this is my first job"
+        + " \n\n ### Response: no complaint"
+    )
+    formatted_dataset, dataset_text_field = data_utils.apply_custom_formatting_template(
+        json_dataset, template
+    )
+    # a new dataset_text_field is created in Dataset
+    assert dataset_text_field in formatted_dataset["train"][0]
+    assert formatted_dataset["train"][0][dataset_text_field] == expected_response
+
+
+def test_apply_custom_formatting_template_adds_eos_token():
+    json_dataset = datasets.load_dataset("json", data_files=TWITTER_COMPLAINTS_DATA)
+    template = "### Input: {{Tweet text}} \n\n ### Response: {{text_label}}"
+    # First response from the data file that is read.
+    expected_response = (
+        "### Input: @HMRCcustomers No this is my first job"
+        + " \n\n ### Response: no complaintEOS"
+    )
+    formatted_dataset, dataset_text_field = data_utils.apply_custom_formatting_template(
+        json_dataset, template, "EOS"
+    )
+    # a new dataset_text_field is created in Dataset
+    assert dataset_text_field in formatted_dataset["train"][0]
+    assert formatted_dataset["train"][0][dataset_text_field] == expected_response
+
+
+def test_apply_custom_formatting_template_gives_error_with_wrong_keys():
+    """Tests that the formatting function will throw error if wrong keys are passed to template"""
+    json_dataset = datasets.load_dataset("json", data_files=TWITTER_COMPLAINTS_DATA)
+    template = "### Input: {{not found}} \n\n ### Response: {{text_label}}"
+    with pytest.raises(KeyError):
+        data_utils.apply_custom_formatting_template(json_dataset, template, "EOS")

tuning/config/configs.py

@@ -43,17 +43,34 @@ class ModelArguments:
 @dataclass
 class DataArguments:
     training_data_path: str = field(
-        default=None, metadata={"help": "Path to the training data in JSONL format."}
+        default=None,
+        metadata={"help": "Path to the training data in JSON/JSONL format."},
     )
     response_template: str = field(
         default=None,
         metadata={"help": "Response template, separator to train on completions only"},
     )
     dataset_text_field: str = field(
-        default=None, metadata={"help": "Training dataset text field"}
+        default=None,
+        metadata={
+            "help": "Training dataset text field containing single sequence. \
+                    Either the dataset_text_field \
+                    or data_formatter_template need to be supplied."
+        },
     )
     validation_data_path: str = field(
-        default=None, metadata={"help": "Path to the validation data in JSONL format."}
+        default=None,
+        metadata={"help": "Path to the validation data in JSON/JSONL format."},
+    )
+    data_formatter_template: str = field(
+        default=None,
+        metadata={
+            "help": "formatter template to format a single sequence \
+                         from each instance in JSONL files. \
+                         Keys of JSON can be referred to as {{key}} in template. \
+                         Either the dataset_text_field \
+                         or data_formatter_template needs to be supplied."
+        },
     )