File size: 6,790 Bytes
8ebda9e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
import os
import re
from pathlib import Path
import glob
from tqdm import tqdm
from contextlib import ExitStack
import datasets
import multiprocessing
from typing import cast, TextIO
from itertools import chain
import json
from concurrent.futures import ProcessPoolExecutor
from random import shuffle
from pytorch_lightning import LightningDataModule
from typing import Optional
from torch.utils.data import DataLoader
# _SPLIT_DATA_PATH = '/data1/datas/wudao_180g_split/test'
_SPLIT_DATA_PATH = '/data1/datas/wudao_180g_split'
_CACHE_SPLIT_DATA_PATH = '/data1/datas/wudao_180g_FSData'
# feats = datasets.Features({"text": datasets.Value('string')})
class BertDataGenerate(object):
def __init__(self,
data_files=_SPLIT_DATA_PATH,
save_path=_CACHE_SPLIT_DATA_PATH,
train_test_validation='950,49,1',
num_proc=1,
cache=True):
self.data_files = Path(data_files)
if save_path:
self.save_path = Path(save_path)
else:
self.save_path = self.file_check(
Path(self.data_files.parent, self.data_files.name+'_FSDataset'),
'save')
self.num_proc = num_proc
self.cache = cache
self.split_idx = self.split_train_test_validation_index(train_test_validation)
if cache:
self.cache_path = self.file_check(
Path(self.save_path.parent, 'FSDataCache', self.data_files.name), 'cache')
else:
self.cache_path = None
@staticmethod
def file_check(path, path_type):
print(path)
if not path.exists():
path.mkdir(parents=True)
print(f"Since no {path_type} directory is specified, the program will automatically create it in {path} directory.")
return str(path)
@staticmethod
def split_train_test_validation_index(train_test_validation):
split_idx_ = [int(i) for i in train_test_validation.split(',')]
idx_dict = {
'train_rate': split_idx_[0]/sum(split_idx_),
'test_rate': split_idx_[1]/sum(split_idx_[1:])
}
return idx_dict
def process(self, index, path):
print('saving dataset shard {}'.format(index))
ds = (datasets.load_dataset('json', data_files=str(path),
cache_dir=self.cache_path,
features=None))
# ds = ds.map(self.cut_sent,input_columns='text')
# print(d)
# print('!!!',ds)
ds = ds['train'].train_test_split(train_size=self.split_idx['train_rate'])
ds_ = ds['test'].train_test_split(train_size=self.split_idx['test_rate'])
ds = datasets.DatasetDict({
'train': ds['train'],
'test': ds_['train'],
'validation': ds_['test']
})
# print('!!!!',ds)
ds.save_to_disk(Path(self.save_path, path.name))
return 'saving dataset shard {} done'.format(index)
def generate_cache_arrow(self) -> None:
'''
生成HF支持的缓存文件，加速后续的加载
'''
data_dict_paths = self.data_files.rglob('*')
p = ProcessPoolExecutor(max_workers=self.num_proc)
res = list()
for index, path in enumerate(data_dict_paths):
res.append(p.submit(self.process, index, path))
p.shutdown(wait=True)
for future in res:
print(future.result(), flush=True)
def load_dataset(num_proc=4, **kargs):
cache_dict_paths = Path(_CACHE_SPLIT_DATA_PATH).glob('*')
ds = []
res = []
p = ProcessPoolExecutor(max_workers=num_proc)
for path in cache_dict_paths:
res.append(p.submit(datasets.load_from_disk,
str(path), **kargs))
p.shutdown(wait=True)
for future in res:
ds.append(future.result())
# print(future.result())
train = []
test = []
validation = []
for ds_ in ds:
train.append(ds_['train'])
test.append(ds_['test'])
validation.append(ds_['validation'])
# ds = datasets.concatenate_datasets(ds)
# print(ds)
return datasets.DatasetDict({
'train': datasets.concatenate_datasets(train),
'test': datasets.concatenate_datasets(test),
'validation': datasets.concatenate_datasets(validation)
})
class BertDataModule(LightningDataModule):
@ staticmethod
def add_data_specific_args(parent_args):
parser = parent_args.add_argument_group('Universal DataModule')
parser.add_argument('--num_workers', default=8, type=int)
parser.add_argument('--train_batchsize', default=32, type=int)
parser.add_argument('--val_batchsize', default=32, type=int)
parser.add_argument('--test_batchsize', default=32, type=int)
parser.add_argument('--datasets_name', type=str)
# parser.add_argument('--datasets_name', type=str)
parser.add_argument('--train_datasets_field', type=str, default='train')
parser.add_argument('--val_datasets_field', type=str, default='validation')
parser.add_argument('--test_datasets_field', type=str, default='test')
return parent_args
def __init__(
self,
tokenizer,
collate_fn,
args,
**kwargs,
):
super().__init__()
self.datasets = load_dataset(num_proc=args.num_workers)
self.tokenizer = tokenizer
self.collate_fn = collate_fn
self.save_hyperparameters(args)
def setup(self, stage: Optional[str] = None) -> None:
self.train = DataLoader(
self.datasets[self.hparams.train_datasets_field],
batch_size=self.hparams.train_batchsize,
shuffle=True,
num_workers=self.hparams.num_workers,
collate_fn=self.collate_fn,
)
self.val = DataLoader(
self.datasets[self.hparams.val_datasets_field],
batch_size=self.hparams.val_batchsize,
shuffle=False,
num_workers=self.hparams.num_workers,
collate_fn=self.collate_fn,
)
self.test = DataLoader(
self.datasets[self.hparams.test_datasets_field],
batch_size=self.hparams.test_batchsize,
shuffle=False,
num_workers=self.hparams.num_workers,
collate_fn=self.collate_fn,
)
return
def train_dataloader(self):
return self.train
def val_dataloader(self):
return self.val
def test_dataloader(self):
return self.test
if __name__ == '__main__':
# pre = PreProcessing(_SPLIT_DATA_PATH)
# pre.processing()
dataset = BertDataGenerate(_SPLIT_DATA_PATH, num_proc=16)
dataset.generate_cache_arrow()
|