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| # Copyright 2024 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. | |
| from enum import Enum, unique | |
| from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Set, TypedDict, Union | |
| from datasets import DatasetDict, concatenate_datasets, interleave_datasets | |
| from ..extras.logging import get_logger | |
| if TYPE_CHECKING: | |
| from datasets import Dataset, IterableDataset | |
| from ..hparams import DataArguments | |
| logger = get_logger(__name__) | |
| SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]] | |
| class Role(str, Enum): | |
| USER = "user" | |
| ASSISTANT = "assistant" | |
| SYSTEM = "system" | |
| FUNCTION = "function" | |
| OBSERVATION = "observation" | |
| class DatasetModule(TypedDict): | |
| train_dataset: Optional[Union["Dataset", "IterableDataset"]] | |
| eval_dataset: Optional[Union["Dataset", "IterableDataset"]] | |
| def merge_dataset( | |
| all_datasets: List[Union["Dataset", "IterableDataset"]], data_args: "DataArguments", seed: int | |
| ) -> Union["Dataset", "IterableDataset"]: | |
| r""" | |
| Merges multiple datasets to a unified dataset. | |
| """ | |
| 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=seed, | |
| stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted", | |
| ) | |
| else: | |
| raise ValueError("Unknown mixing strategy: {}.".format(data_args.mix_strategy)) | |
| def split_dataset( | |
| dataset: Union["Dataset", "IterableDataset"], data_args: "DataArguments", seed: int | |
| ) -> "DatasetDict": | |
| r""" | |
| Splits the dataset and returns a dataset dict containing train set and validation set. | |
| Supports both map dataset and iterable dataset. | |
| """ | |
| if data_args.streaming: | |
| dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=seed) | |
| val_set = dataset.take(int(data_args.val_size)) | |
| train_set = dataset.skip(int(data_args.val_size)) | |
| return DatasetDict({"train": train_set, "validation": val_set}) | |
| else: | |
| val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size | |
| dataset = dataset.train_test_split(test_size=val_size, seed=seed) | |
| return DatasetDict({"train": dataset["train"], "validation": dataset["test"]}) | |