Source code for cerebras.modelzoo.data.nlp.gpt.HuggingFaceDataProcessorEli5

# 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 HuggingFace Eli5 map-style Dataloader"""

from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.data_preparation.huggingface.HuggingFace_Eli5 import (
    HuggingFace_Eli5,
)
from cerebras.modelzoo.data_preparation.huggingface.HuggingFaceDataProcessor import (
    HuggingFaceDataProcessor,
)


[docs]@registry.register_datasetprocessor("HuggingFaceDataProcessorEli5") class HuggingFaceDataProcessorEli5(HuggingFaceDataProcessor): """ A HuggingFace Eli5 map-style Data Processor. :param dict params: dict containing training input parameters for creating dataset. Expects the following fields: - "batch_size" (int): Batch size. - "shuffle" (bool): Flag to enable data shuffling. - "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. """ def __init__(self, params): num_workers = params.get("num_workers", 0) split = params["split"] self.dataset, self.data_collator = HuggingFace_Eli5( split=split, num_workers=num_workers ) # The super class will take care of sharding the dataset and creating the dataloader super().__init__(params)