File size: 3,682 Bytes
db704cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | # 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
import random
from typing import Any, Literal
from datasets import load_dataset
from ...utils.plugin import BasePlugin
from ...utils.types import DatasetInfo, HFDataset
class DataLoaderPlugin(BasePlugin):
"""Plugin for loading dataset."""
def load(self, dataset_info: DatasetInfo) -> HFDataset:
path = dataset_info["path"]
split = dataset_info.get("split", "train")
streaming = dataset_info.get("streaming", False)
return super().__call__(path, split, streaming)
def _get_builder_name(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.
"""
filetype = os.path.splitext(path)[-1][1:]
if filetype in ["arrow", "csv", "json", "jsonl", "parquet", "txt"]:
return filetype.replace("jsonl", "json").replace("txt", "text")
else:
raise ValueError(f"Unknown dataset filetype: {filetype}.")
@DataLoaderPlugin("local").register()
def load_data_from_file(filepath: str, split: str, streaming: bool) -> HFDataset:
if os.path.isdir(filepath):
filetype = _get_builder_name(os.listdir(filepath)[0])
dataset = load_dataset(filetype, data_dir=filepath, split=split)
elif os.path.isfile(filepath):
filetype = _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: # faster when data is streamed from local files
dataset = dataset.to_iterable_dataset()
return dataset
def adjust_data_index(
data_index: list[tuple[str, int]], size: int | None, weight: float | None
) -> 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 = random.choices(data_index, k=size)
if weight is not None:
data_index = random.choices(data_index, k=int(len(data_index) * weight))
return data_index
def select_data_sample(
data_index: list[tuple[str, int]], index: slice | list[int] | Any
) -> tuple[str, int] | list[tuple[str, int]]:
"""Select dataset samples.
Args:
data_index (list[tuple[str, int]]): List of (dataset_name, sample_index).
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 [data_index[i] for i in range(*index.indices(len(data_index)))]
elif isinstance(index, list):
return [data_index[i] for i in index]
else:
raise ValueError(f"Invalid index type {type(index)}.")
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