# Copyright 2025 the LlamaFactory team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """The definition of data engine. How to use: data_engine = DataEngine(data_args.train_dataset) data_engine[i]: Get the sample via index. Init workflow: 1. Parse dataset info from arguments. 2. Load datasets according to dataset info. 3. Build data index (and reweight samples if necessary). Get data sample: 1. Get sample from data index. 2. Convert sample to standard format. 3. Return sample. Note: 1. The data engine is equivalent to the torch dataset. 2. The data engine is agnostic to the model used. """ import os from collections.abc import Iterable from typing import Any from huggingface_hub import hf_hub_download from omegaconf import OmegaConf from torch.utils.data import Dataset from ..utils.types import DatasetInfo, HFDataset, Sample class DataEngine(Dataset): """Data engine. Args: data_args: Data arguments. """ def __init__(self, dataset_path: str) -> None: self.path = dataset_path """Dataset path.""" self.datasets: dict[str, HFDataset] = {} """Dict of (dataset_name, dataset)""" self.dataset_infos: dict[str, DatasetInfo] = {} """Dict of (dataset_name, dataset_info)""" self.data_index: list[tuple[str, int]] = [] """List of (dataset_name, sample_index)""" self.streaming: bool = False """Whether dataset is streaming.""" self._get_dataset_info() self._load_dataset() self._build_data_index() def _get_dataset_info(self) -> None: """Get dataset info from data arguments.""" if self.path.endswith(".yaml") and os.path.isfile(self.path): # local file self.dataset_infos = OmegaConf.load(self.path) elif self.path.endswith(".yaml"): # hf hub uri, e.g. llamafactory/v1-sft-demo/dataset_info.yaml repo_id, filename = os.path.split(self.path) filepath = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset") self.dataset_infos = OmegaConf.load(filepath) elif os.path.exists(self.path): # local file(s) self.dataset_infos = {"default": {"path": self.path, "source": "local"}} else: # hf hub dataset, e.g. llamafactory/v1-sft-demo self.dataset_infos = {"default": {"path": self.path}} def _load_dataset(self) -> None: """Load datasets according to dataset info.""" is_streaming = [dataset_info.get("streaming", False) for dataset_info in self.dataset_infos.values()] self.streaming = any(is_streaming) if all(is_streaming) != any(is_streaming): raise ValueError("All datasets must be streaming or non-streaming.") for dataset_name, dataset_info in self.dataset_infos.items(): split = dataset_info.get("split", "train") if dataset_info.get("source", "hf_hub") == "hf_hub": from datasets import load_dataset self.datasets[dataset_name] = load_dataset(dataset_info["path"], split=split, streaming=self.streaming) else: # data loader plugin from ..plugins.data_plugins.loader import DataLoaderPlugin self.datasets[dataset_name] = DataLoaderPlugin(dataset_info["source"]).load(dataset_info) def _build_data_index(self) -> None: """Build dataset index.""" for dataset_name, dataset in self.datasets.items(): if self.streaming: data_index = [(dataset_name, -1) for _ in range(1000)] else: data_index = [(dataset_name, sample_index) for sample_index in range(len(dataset))] size = self.dataset_infos[dataset_name].get("size") weight = self.dataset_infos[dataset_name].get("weight") if size or weight: from ..plugins.data_plugins.loader import adjust_data_index data_index = adjust_data_index(data_index, size, weight) self.data_index.extend(data_index) def _convert_data_sample(self, raw_sample: dict[str, Any], dataset_name: str) -> Sample: """Convert dataset sample. Args: raw_sample (dict[str, Any]): Raw dataset sample. dataset_name (str): Dataset name. Returns: Sample: Dataset sample. """ converter = self.dataset_infos[dataset_name].get("converter") if converter is not None: from ..plugins.data_plugins.converter import DataConverterPlugin return {"_dataset_name": dataset_name, **DataConverterPlugin(converter)(raw_sample)} else: return {"_dataset_name": dataset_name, **raw_sample} def __len__(self) -> int: """Get dataset length. Returns: int: Dataset length. """ if self.streaming: return -1 else: return len(self.data_index) def __getitem__(self, index: int | Any) -> Sample | list[Sample]: """Get dataset item. Args: index (int): Dataset index. Returns: Sample: Dataset item. """ if self.streaming: raise ValueError("Streaming dataset does not support index access.") if isinstance(index, int): dataset_name, sample_index = self.data_index[index] return self._convert_data_sample(self.datasets[dataset_name][sample_index], dataset_name) else: # data selector plugin from ..plugins.data_plugins.loader import select_data_sample selected_index = select_data_sample(self.data_index, index) if isinstance(selected_index, list): return [ self._convert_data_sample(self.datasets[dataset_name][sample_index], dataset_name) for dataset_name, sample_index in selected_index ] else: dataset_name, sample_index = selected_index return self._convert_data_sample(self.datasets[dataset_name][sample_index], dataset_name) def __iter__(self) -> Iterable[Sample]: """Get dataset iterator. Returns: Iterable[Sample]: Dataset iterator. """ # NOTE: hf iterable dataset uses worker ids while map dataset does not # NOTE: add worker id and shuffle to the map dataset # https://github.com/huggingface/datasets/blob/4.0.0/src/datasets/iterable_dataset.py#L2214 raise NotImplementedError() if __name__ == "__main__": """ python -m llamafactory.v1.core.data_engine --train_dataset data/v1_sft_demo.yaml python -m llamafactory.v1.core.data_engine --train_dataset data/v1_dpo_demo.yaml """ from ..config.arg_parser import get_args _, data_args, *_ = get_args() data_engine = DataEngine(data_args.train_dataset) print(data_engine[0])