LLaMA-Factory/src/llmtuner/data/loader.py

import inspect
import os
from typing import TYPE_CHECKING, List, Literal, Union

from datasets import concatenate_datasets, interleave_datasets, load_dataset, load_from_disk

from ..extras.constants import FILEEXT2TYPE
from ..extras.logging import get_logger
from .aligner import align_dataset
from .parser import get_dataset_list
from .preprocess import get_preprocess_and_print_func
from .template import get_template_and_fix_tokenizer
from .utils import checksum


if TYPE_CHECKING:
    from datasets import Dataset, IterableDataset
    from transformers import Seq2SeqTrainingArguments
    from transformers.tokenization_utils import PreTrainedTokenizer

    from ..hparams import DataArguments, ModelArguments
    from .parser import DatasetAttr


logger = get_logger(__name__)


def load_single_dataset(
    dataset_attr: "DatasetAttr",
    model_args: "ModelArguments",
    data_args: "DataArguments",
):
    data_path, data_name, data_dir, data_files = None, None, None, None
    if dataset_attr.load_from in ["hf_hub", "ms_hub"]:
        data_path = dataset_attr.dataset_name
        data_name = dataset_attr.subset
        data_dir = dataset_attr.folder

    elif dataset_attr.load_from == "script":
        data_path = os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)
        data_name = dataset_attr.subset
        data_dir = dataset_attr.folder

    elif dataset_attr.load_from == "file":
        data_files = []
        local_path: str = os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)
        if os.path.isdir(local_path):  # is directory
            for file_name in os.listdir(local_path):
                data_files.append(os.path.join(local_path, file_name))
                if data_path is None:
                    data_path = FILEEXT2TYPE.get(file_name.split(".")[-1], None)
                elif data_path != FILEEXT2TYPE.get(file_name.split(".")[-1], None):
                    raise ValueError("File types should be identical.")
        elif os.path.isfile(local_path):  # is file
            data_files.append(local_path)
            data_path = FILEEXT2TYPE.get(local_path.split(".")[-1], None)
        else:
            raise ValueError("File not found.")

        if data_path is None:
            raise ValueError("File extension must be txt, csv, json or jsonl.")

        checksum(data_files, dataset_attr.dataset_sha1)
    else:
        raise NotImplementedError

    if dataset_attr.load_from == "ms_hub":
        try:
            from modelscope import MsDataset
            from modelscope.utils.config_ds import MS_DATASETS_CACHE

            cache_dir = model_args.cache_dir or MS_DATASETS_CACHE
            dataset = MsDataset.load(
                dataset_name=data_path,
                subset_name=data_name,
                data_dir=data_dir,
                data_files=data_files,
                split=data_args.split,
                cache_dir=cache_dir,
                token=model_args.ms_hub_token,
                use_streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
            ).to_hf_dataset()
        except ImportError:
            raise ImportError("Please install modelscope via `pip install modelscope -U`")
    else:
        if "trust_remote_code" in inspect.signature(load_dataset).parameters:  # for datasets==2.16.0
            kwargs = {"trust_remote_code": True}
        else:
            kwargs = {}

        dataset = load_dataset(
            path=data_path,
            name=data_name,
            data_dir=data_dir,
            data_files=data_files,
            split=data_args.split,
            cache_dir=model_args.cache_dir,
            token=model_args.hf_hub_token,
            streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
            **kwargs,
        )

    if data_args.streaming and (dataset_attr.load_from == "file"):  # faster than specifying streaming=True
        dataset = dataset.to_iterable_dataset()  # TODO: add num shards parameter

    if data_args.max_samples is not None:  # truncate dataset
        num_samples = min(data_args.max_samples, len(dataset))
        dataset = dataset.select(range(num_samples))

    return align_dataset(dataset, dataset_attr, data_args)


def merge_dataset(
    all_datasets: List[Union["Dataset", "IterableDataset"]],
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
) -> Union["Dataset", "IterableDataset"]:
    if len(all_datasets) == 1:
        return all_datasets[0]
    elif data_args.mix_strategy == "concat":
        if data_args.streaming:
            logger.warning("The samples between different datasets will not be mixed in streaming mode.")
        return concatenate_datasets(all_datasets)
    elif data_args.mix_strategy.startswith("interleave"):
        if not data_args.streaming:
            logger.warning("We recommend using `mix_strategy=concat` in non-streaming mode.")
        return interleave_datasets(
            datasets=all_datasets,
            probabilities=data_args.interleave_probs,
            seed=training_args.seed,
            stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted",
        )
    else:
        raise ValueError("Unknown mixing strategy.")


def get_dataset(
    tokenizer: "PreTrainedTokenizer",
    model_args: "ModelArguments",
    data_args: "DataArguments",
    training_args: "Seq2SeqTrainingArguments",
    stage: Literal["pt", "sft", "rm", "ppo"],
    # split: Optional[str] = "train", # TODO: add split
) -> Union["Dataset", "IterableDataset"]:
    template = get_template_and_fix_tokenizer(data_args.template, tokenizer)
    if data_args.train_on_prompt and template.efficient_eos:
        raise ValueError("Current template does not support `train_on_prompt`.")

    # Load from cache
    if data_args.cache_path is not None:
        if os.path.exists(data_args.cache_path):
            logger.warning("Loading dataset from disk will ignore other data arguments.")
            dataset = load_from_disk(data_args.cache_path)
            if data_args.streaming:
                dataset = dataset.to_iterable_dataset()
            return dataset

        if data_args.streaming:
            raise ValueError("Turn off dataset streaming to save cache files.")

    with training_args.main_process_first(desc="load dataset"):
        all_datasets = []
        for dataset_attr in get_dataset_list(data_args):  # TODO: add split
            all_datasets.append(load_single_dataset(dataset_attr, model_args, data_args))
        dataset = merge_dataset(all_datasets, data_args, training_args)

    with training_args.main_process_first(desc="pre-process dataset"):
        preprocess_func, print_function = get_preprocess_and_print_func(
            tokenizer, template, data_args, training_args, stage
        )
        column_names = list(next(iter(dataset)).keys())
        kwargs = {}
        if not data_args.streaming:
            kwargs = dict(
                num_proc=data_args.preprocessing_num_workers,
                load_from_cache_file=(not data_args.overwrite_cache),
                desc="Running tokenizer on dataset",
            )

        dataset = dataset.map(preprocess_func, batched=True, remove_columns=column_names, **kwargs)

        if data_args.cache_path is not None and not os.path.exists(data_args.cache_path):
            if training_args.should_save:
                dataset.save_to_disk(data_args.cache_path)
                logger.info("Dataset cache saved at {}.".format(data_args.cache_path))

        if training_args.should_log:
            try:
                print_function(next(iter(dataset)))
            except StopIteration:
                raise RuntimeError("Empty dataset!")

        return dataset