Source code for modelzoo.transformers.pytorch.t5.input.T5HDF5DataProcessor

# Copyright 2022 Cerebras Systems.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""Pytorch T5/Transformer Dataloader"""

from modelzoo.transformers.data_processing.HDF5IterableDataProcessor import (
    HDF5IterableDataProcessor,
)
from modelzoo.transformers.data_processing.HDF5IterableDataset import (
    HDF5IterableDataset,
)


[docs]class T5HDF5DataProcessor(HDF5IterableDataProcessor): """ A HDF5 dataset processor for T5 training. Loads data from HDF5 files. :param dict params: dict containing training input parameters for creating dataset. Expects the following fields: - "data_dir" (str or list of str): Path to dataset HDF5 files - "batch_size" (int): Batch size. - "shuffle" (bool): Flag to enable data shuffling. - "shuffle_buffer" (int): Size of shuffle buffer in samples. - "shuffle_seed" (int): Shuffle seed. - "num_workers" (int): How many subprocesses to use for data loading. - "drop_last" (bool): If True and the dataset size is not divisible by the batch size, the last incomplete batch will be dropped. - "prefetch_factor" (int): Number of batches loaded in advance by each worker. - "persistent_workers" (bool): If True, the data loader will not shutdown the worker processes after a dataset has been consumed once. """
[docs] def __init__(self, params): self.dataset = HDF5IterableDataset(params) # The super class will take care of sharding the dataset and creating the dataloader super().__init__(params)