mlx-examples/llms/mlx_lm/tuner/datasets.py

import json
from pathlib import Path
from typing import Dict, List

from transformers import PreTrainedTokenizer


class Dataset:
    """
    Light-weight wrapper to hold a dataset.
    """

    def __init__(self, data: List[Dict[str, str]], text_key: str = "text"):
        self._text_key = text_key
        self._data = data

    def __getitem__(self, idx: int):
        return self._data[idx][self._text_key]

    def __len__(self):
        if self._data is None:
            return 0
        return len(self._data)


class ChatDataset(Dataset):
    """
    A dataset for chat data in the format of {"messages": [...]}
    https://platform.openai.com/docs/guides/fine-tuning/example-format
    """

    def __init__(self, data: List[Dict[str, str]], tokenizer: PreTrainedTokenizer):
        super().__init__(data)
        self._tokenizer = tokenizer

    def __getitem__(self, idx: int):
        messages = self._data[idx]["messages"]
        text = self._tokenizer.apply_chat_template(
            messages,
            tools=self._data[idx].get("tools", None),
            tokenize=False,
            add_generation_prompt=True,
        )
        return text


class CompletionsDataset(Dataset):
    """
    A dataset for prompt-completion data in the format of {"prompt": ..., "completion": ...}
    or using user-provided keys for prompt and completion values
    https://platform.openai.com/docs/guides/fine-tuning/example-format
    """

    def __init__(
        self,
        data: List[Dict[str, str]],
        tokenizer: PreTrainedTokenizer,
        prompt_key: str = "prompt",
        completion_key: str = "completion",
    ):
        super().__init__(data)
        self._tokenizer = tokenizer
        self._prompt_key = prompt_key
        self._completion_key = completion_key

    def __getitem__(self, idx: int):
        data = self._data[idx]
        text = self._tokenizer.apply_chat_template(
            [
                {"role": "user", "content": data[self._prompt_key]},
                {"role": "assistant", "content": data[self._completion_key]},
            ],
            tokenize=False,
            add_generation_prompt=True,
        )
        return text


def create_dataset(path: Path, tokenizer: PreTrainedTokenizer = None):
    # Return empty dataset for non-existent paths
    if not path.exists():
        return []
    with open(path, "r") as fid:
        data = [json.loads(l) for l in fid]
    if "messages" in data[0]:
        return ChatDataset(data, tokenizer)
    elif "prompt" in data[0] and "completion" in data[0]:
        return CompletionsDataset(data, tokenizer)
    elif "text" in data[0]:
        return Dataset(data)
    else:
        raise ValueError(
            "Unsupported data format, check the supported formats here:\n"
            "https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/LORA.md#data."
        )


def load_dataset(args, tokenizer: PreTrainedTokenizer):
    if getattr(args, "hf_dataset", None) is not None:
        import datasets

        hf_args = args.hf_dataset
        dataset_name = hf_args["name"]
        print(f"Loading Hugging Face dataset {dataset_name}.")
        text_feature = hf_args.get("text_feature")
        prompt_feature = hf_args.get("prompt_feature")
        completion_feature = hf_args.get("completion_feature")

        def create_hf_dataset(split: str = None):
            ds = datasets.load_dataset(
                dataset_name,
                split=split,
                **hf_args.get("config", {}),
            )
            if prompt_feature and completion_feature:
                return CompletionsDataset(
                    ds, tokenizer, prompt_feature, completion_feature
                )
            elif text_feature:
                return Dataset(train_ds, text_key=text_feature)
            else:
                raise ValueError(
                    "Specify either a prompt and completion feature or a text "
                    "feature for the Hugging Face dataset."
                )

        if args.train:
            train_split = hf_args.get("train_split", "train[:80%]")
            valid_split = hf_args.get("valid_split", "train[-10%:]")
            train = create_hf_dataset(split=train_split)
            valid = create_hf_dataset(split=valid_split)
        else:
            train, valid = [], []
        if args.test:
            test = create_hf_dataset(split=hf_args.get("test_split"))
        else:
            test = []

    else:
        names = ("train", "valid", "test")
        data_path = Path(args.data)

        train, valid, test = [
            create_dataset(data_path / f"{n}.jsonl", tokenizer) for n in names
        ]
    if args.train and len(train) == 0:
        raise ValueError(
            "Training set not found or empty. Must provide training set for fine-tuning."
        )
    if args.train and len(valid) == 0:
        raise ValueError(
            "Validation set not found or empty. Must provide validation set for fine-tuning."
        )
    if args.test and len(test) == 0:
        raise ValueError(
            "Test set not found or empty. Must provide test set for evaluation."
        )
    return train, valid, test