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src/llmfactory/hparams/data_args.py

+from dataclasses import dataclass, field
+from typing import Literal, Optional
+
+
+@dataclass
+class DataArguments:
+    r"""
+    Arguments pertaining to what data we are going to input our model for training and evaluation.
+    """
+
+    template: Optional[str] = field(
+        default=None,
+        metadata={"help": "Which template to use for constructing prompts in training and inference."},
+    )
+    dataset: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of provided dataset(s) to use. Use commas to separate multiple datasets."},
+    )
+    dataset_dir: str = field(
+        default="data",
+        metadata={"help": "Path to the folder containing the datasets."},
+    )
+    split: str = field(
+        default="train",
+        metadata={"help": "Which dataset split to use for training and evaluation."},
+    )
+    cutoff_len: int = field(
+        default=1024,
+        metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
+    )
+    reserved_label_len: int = field(
+        default=1,
+        metadata={"help": "The minimum cutoff length reserved for the tokenized labels in the dataset."},
+    )
+    train_on_prompt: bool = field(
+        default=False,
+        metadata={"help": "Whether to disable the mask on the prompt or not."},
+    )
+    streaming: bool = field(
+        default=False,
+        metadata={"help": "Enable dataset streaming."},
+    )
+    buffer_size: int = field(
+        default=16384,
+        metadata={"help": "Size of the buffer to randomly sample examples from in dataset streaming."},
+    )
+    mix_strategy: Literal["concat", "interleave_under", "interleave_over"] = field(
+        default="concat",
+        metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling)."},
+    )
+    interleave_probs: Optional[str] = field(
+        default=None,
+        metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."},
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={"help": "Overwrite the cached training and evaluation sets."},
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the pre-processing."},
+    )
+    max_samples: Optional[int] = field(
+        default=None,
+        metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."},
+    )
+    eval_num_beams: Optional[int] = field(
+        default=None,
+        metadata={"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`"},
+    )
+    ignore_pad_token_for_loss: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether or not to ignore the tokens corresponding to padded labels in the loss computation."
+        },
+    )
+    val_size: float = field(
+        default=0.0,
+        metadata={"help": "Size of the development set, should be an integer or a float in range `[0,1)`."},
+    )
+    packing: Optional[bool] = field(
+        default=None,
+        metadata={
+            "help": "Whether or not to pack the sequences in training. Will automatically enable in pre-training."
+        },
+    )
+    tokenized_path: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to save or load the tokenized datasets."},
+    )
+
+    def __post_init__(self):
+        if self.reserved_label_len >= self.cutoff_len:
+            raise ValueError("`reserved_label_len` must be smaller than `cutoff_len`.")
+
+        if self.streaming and self.val_size > 1e-6 and self.val_size < 1:
+            raise ValueError("Streaming mode should have an integer val size.")
+
+        if self.streaming and self.max_samples is not None:
+            raise ValueError("`max_samples` is incompatible with `streaming`.")