[Deprecation] Remove Training Support

.github/workflows/test-check-transformers.yaml

@@ -93,10 +93,10 @@ jobs:
         if: (success() || failure()) && steps.install.outcome == 'success'
         run: |
           pytest -v tests/llmcompressor/transformers/compression
-      - name: Run Finetune Tests
+      - name: Run Data Tests
         if: (success() || failure()) && steps.install.outcome == 'success'
         run: |
-          pytest -v tests/llmcompressor/transformers/finetune
+          pytest -v tests/llmcompressor/transformers/data
       - name: Running GPTQ Tests
         if: (success() || failure()) && steps.install.outcome == 'success'
         run: |

src/llmcompressor/args/README.md

@@ -1,4 +1,4 @@
-# Input arguments for `oneshot`, `train`, `eval` entrypoints
+# Input arguments for `oneshot` and `eval` entrypoints
 
 Parsers in `llm-compressor` define the input arguments required for various entry points, including `oneshot`, `train`, and `eval`.
 
@@ -38,8 +38,4 @@ Handles model loading and saving. For example, `ModelArguments.model` can be a H
 Manages data loading and preprocessing. The dataset argument can specify a Hugging Face dataset stub or a local dataset compatible with [`load_dataset`](https://github.com/huggingface/datasets/blob/3a4e74a9ace62ecd5c9cde7dcb6bcabd65cc7857/src/datasets/load.py#L1905). The preprocessing_func is a callable function that applies custom logic, such as formatting the data using a chat template.
 
 ## RecipeArguments
-Defines the model recipe. A `recipe` consists of user-defined instructions for optimizing the model. Examples of recipes can be found in the `/examples` directory.
-
-## TrainingArguments
-Specifies training parameters based on Hugging Face's [TrainingArguments class](https://github.com/huggingface/transformers/blob/main/src/transformers/training_args.py). These parameters include settings like learning rate (`learning_rate`), and the optimizer to use (`optim`).
-
+Defines the model recipe. A `recipe` consists of user-defined instructions for optimizing the model. Examples of recipes can be found in the `/examples` directory.

src/llmcompressor/args/__init__.py

@@ -10,5 +10,4 @@
 from .dataset_arguments import DatasetArguments
 from .model_arguments import ModelArguments
 from .recipe_arguments import RecipeArguments
-from .training_arguments import TrainingArguments
 from .utils import parse_args

src/llmcompressor/args/dataset_arguments.py

@@ -16,7 +16,7 @@
 @dataclass
 class DVCDatasetArguments:
     """
-    Arguments for training using DVC
+    Arguments for calibration using DVC
     """
 
     dvc_data_repository: str | None = field(
@@ -28,7 +28,7 @@ class DVCDatasetArguments:
 @dataclass
 class CustomDatasetArguments(DVCDatasetArguments):
     """
-    Arguments for training using custom datasets
+    Arguments for calibration using custom datasets
     """
 
     dataset_path: str | None = field(
@@ -78,8 +78,8 @@ class CustomDatasetArguments(DVCDatasetArguments):
 @dataclass
 class DatasetArguments(CustomDatasetArguments):
     """
-    Arguments pertaining to what data we are going to input our model for
-    calibration, training
+    Arguments pertaining to what data we are going to use for
+    calibration
 
     Using `HfArgumentParser` we can turn this class into argparse
     arguments to be able to specify them on the command line
@@ -152,13 +152,6 @@ class DatasetArguments(CustomDatasetArguments):
             "in the batch (which can be faster on GPU but will be slower on TPU)."
         },
     )
-    max_train_samples: int | None = field(
-        default=None,
-        metadata={
-            "help": "For debugging purposes or quicker training, truncate the number "
-            "of training examples to this value if set."
-        },
-    )
     min_tokens_per_module: float | None = field(
         default=None,
         metadata={

src/llmcompressor/args/utils.py

@@ -14,18 +14,16 @@
     DatasetArguments,
     ModelArguments,
     RecipeArguments,
-    TrainingArguments,
 )
 from llmcompressor.transformers.utils.helpers import resolve_processor_from_model_args
 
 
 def parse_args(
-    include_training_args: bool = False, **kwargs
+    **kwargs,
 ) -> tuple[
     ModelArguments,
     DatasetArguments,
-    RecipeArguments,
-    TrainingArguments | None,
+    RecipeArguments | None,
     str | None,
 ]:
     """
@@ -38,31 +36,18 @@ def parse_args(
             src/llmcompressor/args/dataset_args.py
         * RecipeArguments in
             src/llmcompressor/args/recipe_args.py
-        * TrainingArguments in
-            src/llmcompressor/args/training_args.py
 
-    ModelArguments, DatasetArguments, and RecipeArguments are used for both
-    `oneshot` and `train`. TrainingArguments is only used for `train`.
+    ModelArguments, DatasetArguments, and RecipeArguments used for
+    oneshot.
 
     """
-
-    # pop output_dir, used as an attr in TrainingArguments, where oneshot is not used
     output_dir = kwargs.pop("output_dir", None)
 
     parser_args = (ModelArguments, DatasetArguments, RecipeArguments)
-    if include_training_args:
-        parser_args += (TrainingArguments,)
-
     parser = HfArgumentParser(parser_args)
     parsed_args = parser.parse_dict(kwargs)
 
-    training_args = None
-    if include_training_args:
-        model_args, dataset_args, recipe_args, training_args = parsed_args
-        if output_dir is not None:
-            training_args.output_dir = output_dir
-    else:
-        model_args, dataset_args, recipe_args = parsed_args
+    model_args, dataset_args, recipe_args = parsed_args
 
     if recipe_args.recipe_args is not None:
         if not isinstance(recipe_args.recipe_args, dict):
@@ -83,4 +68,4 @@ def parse_args(
     # silently assign tokenizer to processor
     resolve_processor_from_model_args(model_args)
 
-    return model_args, dataset_args, recipe_args, training_args, output_dir
+    return model_args, dataset_args, recipe_args, output_dir

src/llmcompressor/entrypoints/__init__.py

@@ -8,6 +8,5 @@
 """
 
 from .oneshot import Oneshot, oneshot
-from .train import train
 from .model_free import model_free_ptq
 from .utils import post_process, pre_process