mirror of
https://github.com/hiyouga/LlamaFactory.git
synced 2026-03-22 09:53:24 +08:00
120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
# 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.
|
|
|
|
|
|
import os
|
|
from dataclasses import dataclass
|
|
from typing import Any, Literal, Optional, Union
|
|
|
|
from datasets import load_dataset
|
|
|
|
from ...extras.types import DatasetInfo, HFDataset
|
|
|
|
|
|
@dataclass
|
|
class DataLoaderPlugin:
|
|
"""Plugin for loading dataset."""
|
|
|
|
def _get_builder_name(self, path: str) -> Literal["arrow", "csv", "json", "parquet", "text"]:
|
|
"""Get dataset builder name.
|
|
|
|
Args:
|
|
path (str): Dataset path.
|
|
|
|
Returns:
|
|
Literal["arrow", "csv", "json", "parquet", "text"]: Dataset builder name.
|
|
"""
|
|
return os.path.splitext(path)[-1][1:].replace("jsonl", "json").replace("txt", "text")
|
|
|
|
def auto_load_data(self, dataset_info: DatasetInfo) -> HFDataset:
|
|
dataset_dir = dataset_info.get("dataset_dir", ".")
|
|
split = dataset_info.get("split", "train")
|
|
streaming = dataset_info.get("streaming", False)
|
|
if "file_name" in dataset_info:
|
|
filepath = os.path.join(dataset_dir, dataset_info["file_name"])
|
|
return self.load_data_from_file(filepath, split, streaming)
|
|
else:
|
|
raise NotImplementedError()
|
|
|
|
def load_data_from_file(self, filepath: str, split: str, streaming: bool) -> HFDataset:
|
|
if os.path.isdir(filepath):
|
|
filetype = self._get_builder_name(os.listdir(filepath)[0])
|
|
dataset = load_dataset(filetype, data_dir=filepath, split=split)
|
|
elif os.path.isfile(filepath):
|
|
filetype = self._get_builder_name(filepath)
|
|
dataset = load_dataset(filetype, data_files=filepath, split=split)
|
|
else:
|
|
raise ValueError(f"Can not load dataset from {filepath}.")
|
|
|
|
if streaming:
|
|
dataset = dataset.to_iterable_dataset()
|
|
|
|
return dataset
|
|
|
|
|
|
@dataclass
|
|
class DataIndexPlugin:
|
|
"""Plugin for adjusting dataset index."""
|
|
|
|
def adjust_data_index(
|
|
self, data_index: list[tuple[str, int]], size: Optional[int], weight: Optional[float]
|
|
) -> list[tuple[str, int]]:
|
|
"""Adjust dataset index by size and weight.
|
|
|
|
Args:
|
|
data_index (list[tuple[str, int]]): List of (dataset_name, sample_index).
|
|
size (Optional[int]): Desired dataset size.
|
|
weight (Optional[float]): Desired dataset weight.
|
|
|
|
Returns:
|
|
list[tuple[str, int]]: Adjusted dataset index.
|
|
"""
|
|
if size is not None:
|
|
data_index = self.adjust_by_size(data_index, size)
|
|
|
|
if weight is not None:
|
|
data_index = self.adjust_by_weight(data_index, weight)
|
|
|
|
return data_index
|
|
|
|
def adjust_by_size(self, data_index: list[tuple[str, int]], size: int) -> list[tuple[str, int]]:
|
|
raise NotImplementedError()
|
|
|
|
def adjust_by_weight(self, data_index: list[tuple[str, int]], weight: float) -> list[tuple[str, int]]:
|
|
raise NotImplementedError()
|
|
|
|
|
|
@dataclass
|
|
class DataSelectorPlugin:
|
|
"""Plugin for selecting dataset samples."""
|
|
|
|
data_index: list[tuple[str, int]]
|
|
"""List of (dataset_name, sample_index)"""
|
|
|
|
def select(self, index: Union[slice, list[int], Any]) -> Union[tuple[str, int], list[tuple[str, int]]]:
|
|
"""Select dataset samples.
|
|
|
|
Args:
|
|
index (Union[slice, list[int], Any]): Index of dataset samples.
|
|
|
|
Returns:
|
|
Union[tuple[str, int], list[tuple[str, int]]]: Selected dataset samples.
|
|
"""
|
|
if isinstance(index, slice):
|
|
return [self.data_index[i] for i in range(*index.indices(len(self.data_index)))]
|
|
elif isinstance(index, list):
|
|
return [self.data_index[i] for i in index]
|
|
else:
|
|
raise ValueError(f"Invalid index type {type(index)}.")
|