[Cosmetic] Rename data_args to dataset_args

examples/trl_mixin/ex_trl_distillation.py

@@ -19,12 +19,12 @@
 max_seq_length = 512
 
 # Load gsm8k using SparseML dataset tools
-data_args = DatasetArguments(
+dataset_args = DatasetArguments(
     dataset="gsm8k", dataset_config_name="main", max_seq_length=max_seq_length
 )
 dataset_manager = TextGenerationDataset.load_from_registry(
-    data_args.dataset,
-    data_args=data_args,
+    dataset_args.dataset,
+    dataset_args=dataset_args,
     split="train",
     processor=tokenizer,
 )
@@ -69,7 +69,7 @@
     train_dataset=train_dataset,
     data_collator=data_collator,
     trl_sft_config_args=trl_sft_config_args,
-    data_args=data_args,
+    dataset_args=dataset_args,
     model_args=model_args,
 )
 trainer.train()

src/llmcompressor/entrypoints/oneshot.py

@@ -35,7 +35,7 @@ class Oneshot:
         `kwargs` are parsed into:
         - `model_args`: Arguments for loading and configuring a pretrained model
           (e.g., `AutoModelForCausalLM`).
-        - `data_args`: Arguments for dataset-related configurations, such as
+        - `dataset_args`: Arguments for dataset-related configurations, such as
           calibration dataloaders.
         - `recipe_args`: Arguments for defining and configuring recipes that specify
           optimization actions.
@@ -108,24 +108,23 @@ def __init__(
         """
         Initializes the `Oneshot` class with provided arguments.
 
-        Parses the input keyword arguments into `model_args`, `data_args`, and
+        Parses the input keyword arguments into `model_args`, `dataset_args`, and
         `recipe_args`. Performs preprocessing to initialize the model and
         tokenizer/processor.
 
         :param model_args: ModelArguments parameters, responsible for controlling
             model loading and saving logic
-        :param data_args: DatasetArguments parameters, responsible for controlling
+        :param dataset_args: DatasetArguments parameters, responsible for controlling
             dataset loading, preprocessing and dataloader loading
         :param recipe_args: RecipeArguments parameters, responsible for containing
             recipe-related parameters
         :param output_dir: Path to save the output model after carrying out oneshot
 
         """
-
         model_args, dataset_args, recipe_args, _, output_dir = parse_args(**kwargs)
 
         self.model_args = model_args
-        self.data_args = dataset_args
+        self.dataset_args = dataset_args
         self.recipe_args = recipe_args
         self.output_dir = output_dir
 
@@ -136,14 +135,19 @@ def __init__(
 
     @classmethod
     def from_args(
-        cls, model_args, data_args, recipe_args, output_dir, do_preprocess: bool = True
+        cls,
+        model_args,
+        dataset_args,
+        recipe_args,
+        output_dir,
+        do_preprocess: bool = True,
     ):
         """
         Used only for the stage runner to populate the args.
         """
         instance = super().__new__(cls)
         instance.model_args = model_args
-        instance.data_args = data_args
+        instance.dataset_args = dataset_args
         instance.recipe_args = recipe_args
         instance.output_dir = output_dir
 
@@ -176,7 +180,7 @@ def __call__(self):
         self.processor = self.model_args.processor
 
         calibration_dataloader = get_calibration_dataloader(
-            self.data_args, self.processor
+            self.dataset_args, self.processor
         )
         self.apply_recipe_modifiers(
             calibration_dataloader=calibration_dataloader,
@@ -242,7 +246,7 @@ def _pre_process(self):
         - Applies patches to fix tied tensor issues and modifies `save_pretrained`
           behavior.
         - Initializes the processor if specified as a path or `None`.
-        - Sets the minimum tokens per module if `data_args` are provided.
+        - Sets the minimum tokens per module if `dataset_args` are provided.
 
         Raises:
             FileNotFoundError: If the model or processor path is invalid.
@@ -265,8 +269,8 @@ def _pre_process(self):
             self.processor = self.model_args.processor
 
         # Set minimum tokens per module if data arguments are provided
-        if self.data_args:
-            self.min_tokens_per_module = self.data_args.min_tokens_per_module
+        if self.dataset_args:
+            self.min_tokens_per_module = self.dataset_args.min_tokens_per_module
 
     def check_tied_embeddings(self):
         """

src/llmcompressor/transformers/finetune/data/base.py

@@ -31,7 +31,7 @@ class TextGenerationDataset(RegistryMixin):
     3. Tokenize dataset using model tokenizer/processor
     4. Apply post processing such as grouping text and/or adding labels for finetuning
 
-    :param data_args: configuration settings for dataset loading
+    :param dataset_args: configuration settings for dataset loading
     :param split: split from dataset to load, for instance `test` or `train[:5%]`
     :param processor: processor or tokenizer to use on dataset
     """
@@ -41,11 +41,11 @@ class TextGenerationDataset(RegistryMixin):
 
     def __init__(
         self,
-        data_args: DatasetArguments,
+        dataset_args: DatasetArguments,
         split: str,
         processor: Processor,
     ):
-        self.data_args = data_args
+        self.dataset_args = dataset_args
         self.split = split
         self.processor = processor
 
@@ -58,23 +58,23 @@ def __init__(
                 self.tokenizer.pad_token = self.tokenizer.eos_token
 
             # configure sequence length
-            max_seq_length = data_args.max_seq_length
-            if data_args.max_seq_length > self.tokenizer.model_max_length:
+            max_seq_length = dataset_args.max_seq_length
+            if dataset_args.max_seq_length > self.tokenizer.model_max_length:
                 logger.warning(
                     f"The max_seq_length passed ({max_seq_length}) is larger than "
                     f"maximum length for model ({self.tokenizer.model_max_length}). "
                     f"Using max_seq_length={self.tokenizer.model_max_length}."
                 )
             self.max_seq_length = min(
-                data_args.max_seq_length, self.tokenizer.model_max_length
+                dataset_args.max_seq_length, self.tokenizer.model_max_length
             )
 
             # configure padding
             self.padding = (
                 False
-                if self.data_args.concatenate_data
+                if self.dataset_args.concatenate_data
                 else "max_length"
-                if self.data_args.pad_to_max_length
+                if self.dataset_args.pad_to_max_length
                 else False
             )
 
@@ -83,7 +83,7 @@ def __init__(
             self.padding = False
 
     def __call__(self, add_labels: bool = True) -> DatasetType:
-        dataset = self.data_args.dataset
+        dataset = self.dataset_args.dataset
 
         if isinstance(dataset, str):
             # load dataset: load from huggingface or disk
@@ -96,8 +96,8 @@ def __call__(self, add_labels: bool = True) -> DatasetType:
                 dataset,
                 self.preprocess,
                 batched=False,
-                num_proc=self.data_args.preprocessing_num_workers,
-                load_from_cache_file=not self.data_args.overwrite_cache,
+                num_proc=self.dataset_args.preprocessing_num_workers,
+                load_from_cache_file=not self.dataset_args.overwrite_cache,
                 desc="Preprocessing",
             )
             logger.debug(f"Dataset after preprocessing: {get_columns(dataset)}")
@@ -121,20 +121,20 @@ def __call__(self, add_labels: bool = True) -> DatasetType:
                 # regardless of `batched` argument
                 remove_columns=get_columns(dataset),  # assumes that input names
                 # and output names are disjoint
-                num_proc=self.data_args.preprocessing_num_workers,
-                load_from_cache_file=not self.data_args.overwrite_cache,
+                num_proc=self.dataset_args.preprocessing_num_workers,
+                load_from_cache_file=not self.dataset_args.overwrite_cache,
                 desc="Tokenizing",
             )
             logger.debug(f"Model kwargs after tokenizing: {get_columns(dataset)}")
 
-        if self.data_args.concatenate_data:
+        if self.dataset_args.concatenate_data:
             # postprocess: group text
             dataset = self.map(
                 dataset,
                 self.group_text,
                 batched=True,
-                num_proc=self.data_args.preprocessing_num_workers,
-                load_from_cache_file=not self.data_args.overwrite_cache,
+                num_proc=self.dataset_args.preprocessing_num_workers,
+                load_from_cache_file=not self.dataset_args.overwrite_cache,
                 desc="Concatenating data",
             )
             logger.debug(f"Model kwargs after concatenating: {get_columns(dataset)}")
@@ -145,8 +145,8 @@ def __call__(self, add_labels: bool = True) -> DatasetType:
                 dataset,
                 self.add_labels,
                 batched=False,  # not compatible with batching, need row lengths
-                num_proc=self.data_args.preprocessing_num_workers,
-                load_from_cache_file=not self.data_args.overwrite_cache,
+                num_proc=self.dataset_args.preprocessing_num_workers,
+                load_from_cache_file=not self.dataset_args.overwrite_cache,
                 desc="Adding labels",
             )
             logger.debug(f"Model kwargs after adding labels: {get_columns(dataset)}")
@@ -165,27 +165,31 @@ def load_dataset(self):
         :param cache_dir: disk location to search for cached dataset
         :return: the requested dataset
         """
-        if self.data_args.dataset_path is not None:
-            if self.data_args.dvc_data_repository is not None:
-                self.data_args.raw_kwargs["storage_options"] = {
-                    "url": self.data_args.dvc_data_repository
+        if self.dataset_args.dataset_path is not None:
+            if self.dataset_args.dvc_data_repository is not None:
+                self.dataset_args.raw_kwargs["storage_options"] = {
+                    "url": self.dataset_args.dvc_data_repository
                 }
-                self.data_args.raw_kwargs["data_files"] = self.data_args.dataset_path
+                self.dataset_args.raw_kwargs["data_files"] = (
+                    self.dataset_args.dataset_path
+                )
             else:
-                self.data_args.raw_kwargs["data_files"] = get_custom_datasets_from_path(
-                    self.data_args.dataset_path,
-                    self.data_args.dataset
-                    if hasattr(self.data_args, "dataset")
-                    else self.data_args.dataset_name,
+                self.dataset_args.raw_kwargs["data_files"] = (
+                    get_custom_datasets_from_path(
+                        self.dataset_args.dataset_path,
+                        self.dataset_args.dataset
+                        if hasattr(self.dataset_args, "dataset")
+                        else self.dataset_args.dataset_name,
+                    )
                 )
 
-        logger.debug(f"Loading dataset {self.data_args.dataset}")
+        logger.debug(f"Loading dataset {self.dataset_args.dataset}")
         return get_raw_dataset(
-            self.data_args,
+            self.dataset_args,
             None,
             split=self.split,
-            streaming=self.data_args.streaming,
-            **self.data_args.raw_kwargs,
+            streaming=self.dataset_args.streaming,
+            **self.dataset_args.raw_kwargs,
         )
 
     @cached_property
@@ -194,7 +198,7 @@ def preprocess(self) -> Union[Callable[[LazyRow], Any], None]:
         The function must return keys which correspond to processor/tokenizer kwargs,
         optionally including PROMPT_KEY
         """
-        preprocessing_func = self.data_args.preprocessing_func
+        preprocessing_func = self.dataset_args.preprocessing_func
 
         if callable(preprocessing_func):
             return preprocessing_func
@@ -218,9 +222,9 @@ def dataset_template(self) -> Union[Callable[[Any], Any], None]:
     def rename_columns(self, dataset: DatasetType) -> DatasetType:
         # rename columns to match processor/tokenizer kwargs
         column_names = get_columns(dataset)
-        if self.data_args.text_column in column_names and "text" not in column_names:
-            logger.debug(f"Renaming column `{self.data_args.text_column}` to `text`")
-            dataset = dataset.rename_column(self.data_args.text_column, "text")
+        if self.dataset_args.text_column in column_names and "text" not in column_names:
+            logger.debug(f"Renaming column `{self.dataset_args.text_column}` to `text`")
+            dataset = dataset.rename_column(self.dataset_args.text_column, "text")
 
         return dataset
 

src/llmcompressor/transformers/finetune/data/c4.py

@@ -13,14 +13,16 @@ class C4Dataset(TextGenerationDataset):
     """
     Child text generation class for the C4 dataset
 
-    :param data_args: configuration settings for dataset loading
+    :param dataset_args: configuration settings for dataset loading
     :param split: split from dataset to load, for instance `test` or `train[:5%]`
     :param processor: processor or tokenizer to use on dataset
     """
 
-    def __init__(self, data_args: "DatasetArguments", split: str, processor: Processor):
-        data_args = deepcopy(data_args)
-        data_args.dataset = "allenai/c4"
-        data_args.text_column = "text"
+    def __init__(
+        self, dataset_args: "DatasetArguments", split: str, processor: Processor
+    ):
+        dataset_args = deepcopy(dataset_args)
+        dataset_args.dataset = "allenai/c4"
+        dataset_args.text_column = "text"
 
-        super().__init__(data_args=data_args, split=split, processor=processor)
+        super().__init__(dataset_args=dataset_args, split=split, processor=processor)

src/llmcompressor/transformers/finetune/data/cnn_dailymail.py

@@ -13,19 +13,21 @@ class CNNDailyMailDataset(TextGenerationDataset):
     """
     Text generation class for the CNN/DailyMail dataset
 
-    :param data_args: configuration settings for dataset loading
+    :param dataset_args: configuration settings for dataset loading
     :param split: split from dataset to load, for instance `test` or `train[:5%]`
     :param processor: processor or tokenizer to use on dataset
     """
 
     SAMPLE_TEMPLATE = "Article:\n{article}\n\n### Summarization:\n{highlights}\n"
 
-    def __init__(self, data_args: "DatasetArguments", split: str, processor: Processor):
-        data_args = deepcopy(data_args)
-        data_args.dataset = "cnn_dailymail"
-        data_args.dataset_config_name = "3.0.0"
+    def __init__(
+        self, dataset_args: "DatasetArguments", split: str, processor: Processor
+    ):
+        dataset_args = deepcopy(dataset_args)
+        dataset_args.dataset = "cnn_dailymail"
+        dataset_args.dataset_config_name = "3.0.0"
 
-        super().__init__(data_args=data_args, split=split, processor=processor)
+        super().__init__(dataset_args=dataset_args, split=split, processor=processor)
 
     def dataset_template(self, sample):
         return {

src/llmcompressor/transformers/finetune/data/custom.py

@@ -7,7 +7,7 @@ class CustomDataset(TextGenerationDataset):
     Child text generation class for custom local dataset supporting load
     for csv and json
 
-    :param data_args: configuration settings for dataset loading
+    :param dataset_args: configuration settings for dataset loading
     :param split: split from dataset to load, for instance `test` or `train[:5%]`
         Can also be set to None to load all the splits
     :param processor: processor or tokenizer to use on dataset