rename data_arguments to dataset_arguments

Author: George Ohashi

src/llmcompressor/arg_parser/dataset_arguments.py

@@ -0,0 +1,189 @@
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, Optional, Union
+
+from transformers import DefaultDataCollator
+
+
+@dataclass
+class DVCDatasetArguments:
+    """
+    Arguments for training using DVC
+    """
+
+    dvc_data_repository: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to repository used for dvc_dataset_path"},
+    )
+
+
+@dataclass
+class CustomDatasetArguments(DVCDatasetArguments):
+    """
+    Arguments for training using custom datasets
+    """
+
+    dataset_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Path to the custom dataset. Supports json, csv, dvc. "
+                "For DVC, the to dvc dataset to load, of format dvc://path. "
+                "For csv or json, the path containing the dataset. "
+            ),
+        },
+    )
+
+    text_column: str = field(
+        default="text",
+        metadata={
+            "help": (
+                "Optional key to be used as the `text` input to tokenizer/processor "
+                "after dataset preprocesssing"
+            )
+        },
+    )
+
+    remove_columns: Union[None, str, List] = field(
+        default=None,
+        metadata={"help": "Column names to remove after preprocessing (deprecated)"},
+    )
+
+    preprocessing_func: Union[None, str, Callable] = field(
+        default=None,
+        metadata={
+            "help": (
+                "Typically a function which applies a chat template. Can take the form "
+                "of either a function to apply to the dataset, a name defined in "
+                "src/llmcompressor/transformers/utils/preprocessing_functions.py, or "
+                "a path to a function definition of the form /path/to/file.py:func"
+            )
+        },
+    )
+
+    data_collator: Callable[[Any], Any] = field(
+        default_factory=lambda: DefaultDataCollator(),
+        metadata={"help": "The function to used to form a batch from the dataset"},
+    )
+
+
+@dataclass
+class DatasetArguments(CustomDatasetArguments):
+    """
+    Arguments pertaining to what data we are going to input our model for
+    calibration, training or eval
+
+    Using `HfArgumentParser` we can turn this class into argparse
+    arguments to be able to specify them on the command line
+    """
+
+    dataset: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "The name of the dataset to use (via the datasets library). "
+                "Supports input as a string or DatasetDict from HF"
+            )
+        },
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": ("The configuration name of the dataset to use"),
+        },
+    )
+    max_seq_length: int = field(
+        default=384,
+        metadata={
+            "help": "The maximum total input sequence length after tokenization. "
+            "Sequences longer  than this will be truncated, sequences shorter will "
+            "be padded."
+        },
+    )
+    concatenate_data: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether or not to concatenate datapoints to fill max_seq_length"
+        },
+    )
+    raw_kwargs: Dict = field(
+        default_factory=dict,
+        metadata={"help": "Additional keyboard args to pass to datasets load_data"},
+    )
+    splits: Union[None, str, List, Dict] = field(
+        default=None,
+        metadata={"help": "Optional percentages of each split to download"},
+    )
+    num_calibration_samples: Optional[int] = field(
+        default=512,
+        metadata={"help": "Number of samples to use for one-shot calibration"},
+    )
+    shuffle_calibration_samples: Optional[bool] = field(
+        default=True,
+        metadata={
+            "help": "whether to shuffle the dataset before selecting calibration data"
+        },
+    )
+    streaming: Optional[bool] = field(
+        default=False,
+        metadata={"help": "True to stream data from a cloud dataset"},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached preprocessed datasets or not."},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    pad_to_max_length: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to pad all samples to `max_seq_length`. If False, "
+            "will pad the samples dynamically when batching to the maximum length "
+            "in the batch (which can be faster on GPU but will be slower on TPU)."
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number "
+            "of training examples to this value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number "
+            "of evaluation examples to this value if set."
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of "
+                "prediction examples to this value if set."
+            ),
+        },
+    )
+    min_tokens_per_module: Optional[float] = field(
+        default=None,
+        metadata={
+            "help": (
+                "The minimum percentage of tokens (out of the total number) "
+                "that the module should 'receive' throughout the forward "
+                "pass of the calibration. If a module receives fewer tokens, "
+                "a warning will be logged. Defaults to 1/num_of_experts."
+                "note: this argument is only relevant for MoE models"
+            ),
+        },
+    )
+    trust_remote_code_data: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether or not to allow for datasets defined on the Hub using "
+            "a dataset script. This option should only be set to True for "
+            "repositories you trust and in which you have read the code, as it "
+            "will execute code present on the Hub on your local machine."
+        },
+    )