# Copyright (c) ModelScope Contributors. All rights reserved. from dataclasses import dataclass, field from typing import List, Literal, Optional, Union from swift.dataset import register_dataset_info from swift.utils import get_logger, json_parse_to_dict logger = get_logger() @dataclass class DataArguments: """Holds arguments related to dataset handling and processing. Args: dataset (List[str]): A list of dataset IDs or paths. Defaults to []. Format for each dataset: 'dataset_id_or_path:subset#count'. Both subset and count are optional. - Subsets: Only effective for dataset IDs or folders. Use '/' to select multiple subsets (e.g., 'dataset_id:subset1/subset2') or 'all' to select all registered subsets. If only one subset is registered, it will be used by default; otherwise, 'default' is the default. - Sampling Count: By default, the full dataset is used. Use '#count' to sample. If count < total samples, it performs random sampling without replacement. If count > total, it repeats the full dataset `count // total` times and then randomly samples an additional `count % total` samples. Note: Streaming datasets or setting `--dataset_shuffle false` will result in sequential sampling. - Local datasets: Supports formats like jsonl, csv, json, and folders. val_dataset (List[str]): A list of validation dataset IDs or paths. Defaults to []. cached_dataset (List[str]): Use cached datasets to avoid GPU time being occupied by tokenization during training/inference on large datasets. This parameter is used to set the folder path(s) of cached training datasets, and defaults to `[]`. This is generated by the `swift export --to_cached_dataset true ...` command. ms-swift only stores an extra 'length' field and filters out erroneous samples to reduce storage. Actual preprocessing happens concurrently with training. cached_val_dataset (List[str]): Folder path(s) for cached validation datasets, default is []. split_dataset_ratio (float): The ratio to split from the training set for validation if `val_dataset` is not provided. Defaults to 0.0. Note: The default was 0.01 in `ms-swift<3.6`. data_seed (int): The random seed for dataset shuffling. Defaults to 42. dataset_num_proc (int): The number of processes to use for dataset preprocessing. Defaults to 1. load_from_cache_file (bool): Whether to load the dataset from cache files. Recommended to set to `True` during actual runs and `False` during debugging. Defaults to False. Note: The default was `True` in `ms-swift<3.9`. dataset_shuffle (bool): Whether to shuffle the training dataset. Defaults to True. Note: For CPT/SFT, shuffling occurs at both the dataset level (controlled by this flag) and the dataloader level. val_dataset_shuffle (bool): Whether to shuffle the validation dataset. Defaults to False. streaming (bool): Enables streaming to read and process the dataset on-the-fly. `--max_steps` must be set as the dataset length is unknown. This allows preprocessing to overlap with training but can become a bottleneck with a large `world_size` as preprocessing only runs on rank 0. Defaults to False. interleave_prob (Optional[List[float]]): If set, combines datasets using `interleave_datasets` with the provided probabilities instead of `concatenate_datasets`. Typically used for streaming. Defaults to None. stopping_strategy (str): The stopping strategy for `interleave_datasets`. Can be "first_exhausted" or "all_exhausted". Defaults to "first_exhausted". shuffle_buffer_size (int): The buffer size for shuffling in streaming mode. Only effective if `dataset_shuffle` is `True`. Defaults to 1000. download_mode (str): The dataset download mode. Options are 'reuse_dataset_if_exists' and 'force_redownload'. Defaults to 'reuse_dataset_if_exists'. columns (Optional[str]): A JSON string for column mapping to fit the format required by `AutoPreprocessor`. Example: '{"text1": "query", "text2": "response"}'. Defaults to None. strict (bool): If `True`, raises an error on any problematic data row. If `False`, discards the problematic sample and continues. Typically used for debugging. Defaults to False. remove_unused_columns (bool): Whether to remove columns not used by the model. If `False`, extra columns are passed to the trainer's `compute_loss` function, which is useful for custom loss calculations. Defaults to True. Note: The default is `False` for GPRO. disable_auto_column_mapping (bool): By default, column names in the dataset are automatically mapped. This parameter disables that behavior (the `columns` parameter remains effective), defaulting to `False`. model_name (Optional[List[str]]): For self-cognition tasks, replaces the `{{NAME}}` placeholder in the `swift/self-cognition` dataset. Pass Chinese and English names. Example: `--model_name 小黄 'Xiao Huang'`. Defaults to None. model_author (Optional[List[str]]): For self-cognition tasks, replaces the `{{AUTHOR}}` placeholder in the `swift/self-cognition` dataset. Pass author's Chinese and English names. Example: `--model_author '魔搭' 'ModelScope'`. Defaults to None. custom_dataset_info (List[str]): Path to a custom dataset registration JSON file. Defaults to []. """ # dataset_id or dataset_dir or dataset_path dataset: List[str] = field(default_factory=list) val_dataset: List[str] = field(default_factory=list) cached_dataset: List[str] = field(default_factory=list) cached_val_dataset: List[str] = field(default_factory=list) split_dataset_ratio: float = 0. data_seed: int = 42 dataset_num_proc: int = 1 load_from_cache_file: bool = False dataset_shuffle: bool = True val_dataset_shuffle: bool = False streaming: bool = False interleave_prob: Optional[List[float]] = None stopping_strategy: Literal['first_exhausted', 'all_exhausted'] = 'first_exhausted' shuffle_buffer_size: int = 1000 download_mode: Literal['force_redownload', 'reuse_dataset_if_exists'] = 'reuse_dataset_if_exists' columns: Optional[Union[dict, str]] = None strict: bool = False remove_unused_columns: bool = True disable_auto_column_mapping: bool = False # Chinese name and English name model_name: Optional[List[str]] = field(default=None, metadata={'help': "e.g. ['小黄', 'Xiao Huang']"}) model_author: Optional[List[str]] = field(default=None, metadata={'help': "e.g. ['魔搭', 'ModelScope']"}) custom_dataset_info: List[str] = field(default_factory=list) # .json def _init_custom_dataset_info(self): """register custom dataset_info.json to datasets""" if isinstance(self.custom_dataset_info, str): self.custom_dataset_info = [self.custom_dataset_info] for path in self.custom_dataset_info: register_dataset_info(path) def __post_init__(self): self.columns = json_parse_to_dict(self.columns) if len(self.val_dataset) > 0 or self.streaming and self.split_dataset_ratio > 0: self.split_dataset_ratio = 0. if len(self.val_dataset) > 0: msg = 'len(args.val_dataset) > 0' else: msg = 'args.streaming is True' logger.info(f'Because {msg}, setting split_dataset_ratio: {self.split_dataset_ratio}') self._init_custom_dataset_info() if isinstance(self.cached_dataset, str): self.cached_dataset = [self.cached_dataset] self._init_val_dataset_exists() def _init_val_dataset_exists(self): self._val_dataset_exists = bool(self.dataset and self.split_dataset_ratio > 0 or self.val_dataset or self.cached_val_dataset) def get_dataset_kwargs(self): return { 'seed': self.data_seed, 'num_proc': self.dataset_num_proc, 'load_from_cache_file': self.load_from_cache_file, 'streaming': self.streaming, 'interleave_prob': self.interleave_prob, 'stopping_strategy': self.stopping_strategy, 'shuffle_buffer_size': self.shuffle_buffer_size, 'use_hf': self.use_hf, 'hub_token': self.hub_token, 'download_mode': self.download_mode, 'columns': self.columns, 'strict': self.strict, 'model_name': self.model_name, 'model_author': self.model_author, 'remove_unused_columns': self.remove_unused_columns, 'disable_auto_column_mapping': self.disable_auto_column_mapping, }