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import torchdata.datapipes as dp |
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import json |
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from PIL import Image |
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import functools |
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import numpy as np |
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import torch |
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import pickle |
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import random |
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import os |
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from braceexpand import braceexpand |
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from torch.utils.data.datapipes.iter.sharding import SHARDING_PRIORITIES |
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from torch.utils.data.dataloader import default_collate |
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import base64 |
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import io |
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import tarfile |
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from torchdata.datapipes.iter import TarArchiveLoader |
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from typing import cast, IO, Iterable, Iterator, Optional, Tuple, Dict |
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from torchdata.datapipes import functional_datapipe |
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from io import BufferedIOBase |
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from torchdata.datapipes.utils import StreamWrapper |
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from torchdata.datapipes.utils.common import validate_pathname_binary_tuple |
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import warnings |
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from torchdata.datapipes.iter import IterDataPipe |
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import hydra |
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from omegaconf import OmegaConf |
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import pyrootutils |
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pyrootutils.setup_root(__file__, indicator='.project-root', pythonpath=True) |
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BOI_TOKEN = '<img>' |
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EOI_TOKEN = '</img>' |
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IMG_TOKEN = '<img_{:05d}>' |
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@functional_datapipe("load_from_tar_wo_exception") |
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class TarArchiveLoaderWoException(TarArchiveLoader): |
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def __iter__(self) -> Iterator[Tuple[str, BufferedIOBase]]: |
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for data in self.datapipe: |
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validate_pathname_binary_tuple(data) |
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pathname, data_stream = data |
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try: |
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if isinstance(data_stream, StreamWrapper) and isinstance(data_stream.file_obj, tarfile.TarFile): |
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tar = data_stream.file_obj |
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else: |
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reading_mode = (self.mode if hasattr(data_stream, "seekable") and data_stream.seekable() else |
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self.mode.replace(":", "|")) |
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tar = tarfile.open(fileobj=cast( |
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Optional[IO[bytes]], data_stream), mode=reading_mode) |
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for tarinfo in tar: |
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if not tarinfo.isfile(): |
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continue |
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extracted_fobj = tar.extractfile(tarinfo) |
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if extracted_fobj is None: |
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warnings.warn( |
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f"failed to extract file {tarinfo.name} from source tarfile {pathname}") |
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raise tarfile.ExtractError |
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inner_pathname = os.path.normpath( |
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os.path.join(pathname, tarinfo.name)) |
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yield inner_pathname, StreamWrapper(extracted_fobj, data_stream, name=inner_pathname) |
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except Exception as e: |
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warnings.warn( |
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f"Unable to extract files from corrupted tarfile stream {pathname} due to: {e}, abort!") |
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finally: |
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if isinstance(data_stream, StreamWrapper): |
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data_stream.autoclose() |
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def decode_obelisc_data_for_llm(item, tokenizer=None, max_length=512, reverse_ratio=0.5, max_images=None): |
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key, value = item |
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sample = {} |
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if key.endswith(".pkl"): |
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try: |
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value = pickle.load(value) |
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except Exception as e: |
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print(f'Error occured when load pkl: {e}') |
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return key, sample |
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image_list = value['image_ids'] |
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text_list = value['texts'] |
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metadata = value['metadata'] |
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reverse_flag = np.random.uniform(0, 1) < reverse_ratio |
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if reverse_flag: |
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idx = 0 |
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while idx < len(image_list) - 1: |
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if image_list[idx] is not None: |
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image_list[idx], image_list[idx + |
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1] = image_list[idx + 1], image_list[idx] |
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text_list[idx], text_list[idx + |
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1] = text_list[idx + 1], text_list[idx] |
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idx += 2 |
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else: |
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idx += 1 |
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unified_tokens = tokenizer.bos_token |
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cur_images = 0 |
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for image_id, text in zip(image_list, text_list): |
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if (image_id is None) + (text is None) != 1: |
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print('Incorrect data format, skip.') |
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return key, {} |
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if image_id is not None: |
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if max_images is not None and cur_images >= max_images: |
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break |
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image_tokens = BOI_TOKEN + \ |
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''.join([IMG_TOKEN.format(int(item)) |
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for item in image_id]) + EOI_TOKEN |
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unified_tokens += image_tokens |
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cur_images += 1 |
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else: |
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unified_tokens += text |
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if max_images is not None and cur_images >= max_images: |
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break |
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unified_tokens += tokenizer.eos_token |
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tokenized = tokenizer(unified_tokens, |
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max_length=max_length, |
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add_special_tokens=False, |
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truncation=True, |
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padding='max_length', |
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return_tensors='pt') |
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input_ids = tokenized['input_ids'][0] |
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attention_mask = tokenized['attention_mask'][0] |
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labels = torch.clone(input_ids) |
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labels[labels == tokenizer.pad_token_id] = -100 |
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filter_flag = True |
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return key, { |
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'input_ids': input_ids, |
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'attention_mask': attention_mask, |
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'labels': labels, |
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'filter_flag': filter_flag, |
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} |
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else: |
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return key, None |
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def unwarp_data(item): |
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unwarpped = {} |
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for key, value in item.items(): |
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if isinstance(value, dict): |
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unwarpped.update(value) |
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elif value is not None: |
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unwarpped[key] = value |
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if 'metadata' not in unwarpped: |
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unwarpped['metadata'] = '{}' |
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if '__key__' in unwarpped: |
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unwarpped['__key__'] = unwarpped['__key__'].split('/')[-1] |
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return unwarpped |
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def filter_data_for_llm(item): |
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if 'input_ids' in item and item.get('filter_flag', True): |
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return True |
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else: |
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print('A sample has been filtered out.') |
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return False |
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def build_obelisc_datapipes_for_llm(data_dir, |
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tokenizer=None, |
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max_length=512, |
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reverse_ratio=0.5, |
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max_images=None, |
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recursive=True, |
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batch_size=None, |
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cycle_count=None): |
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""" |
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datapipe of image interleaved dataset (such as mmc4...) with webdataset format |
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""" |
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decode_partial = functools.partial(decode_obelisc_data_for_llm, |
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tokenizer=tokenizer, |
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max_length=max_length, |
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reverse_ratio=reverse_ratio, |
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max_images=max_images) |
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if isinstance(data_dir, str): |
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data_dir = list(braceexpand(data_dir)) |
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datapipe = dp.iter.FileLister( |
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root=data_dir, masks='*.tar', recursive=recursive) |
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datapipe = datapipe.cycle(count=cycle_count) |
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datapipe = datapipe.shuffle() |
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datapipe = datapipe.sharding_filter() |
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datapipe = datapipe.open_files(mode='b') |
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datapipe = datapipe.load_from_tar_wo_exception() |
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datapipe = datapipe.map(decode_partial) |
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datapipe = datapipe.webdataset() |
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datapipe = datapipe.map(unwarp_data) |
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datapipe = datapipe.filter(filter_data_for_llm) |
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datapipe = datapipe.shuffle(buffer_size=4096) |
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if batch_size is not None: |
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datapipe = datapipe.batch(batch_size) |
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datapipe = datapipe.collate() |
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return datapipe |
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if __name__ == '__main__': |
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from torchdata.dataloader2 import DataLoader2, MultiProcessingReadingService, DistributedReadingService, SequentialReadingService |
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data_dir = './obelisc' |
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seed_tokenizer_cfg_path = '' |
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dataloader_num_workers = 2 |
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seed_tokenizer_cfg = OmegaConf.load(seed_tokenizer_cfg_path) |
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seed_tokenizer = hydra.utils.instantiate(seed_tokenizer_cfg) |
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dataset = build_obelisc_datapipes_for_llm( |
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data_dir=data_dir, tokenizer=seed_tokenizer, max_length=1024, cycle_count=1) |
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mp_rs = MultiProcessingReadingService( |
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num_workers=dataloader_num_workers) |
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dist_rs = DistributedReadingService() |
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rs = SequentialReadingService(dist_rs, mp_rs) |
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dataloader = DataLoader2(dataset, reading_service=rs) |
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