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"""Pytorch HuggingFace Eli5 Iterable Dataloader"""
from modelzoo.transformers.data_processing.huggingface.HuggingFace_Eli5 import (
HuggingFace_Eli5,
)
from modelzoo.transformers.data_processing.huggingface.HuggingFaceDataProcessor import (
HuggingFaceDataProcessor,
)
from modelzoo.transformers.pytorch.input_utils import num_tasks
[docs]class HuggingFaceIterableDataProcessorEli5(HuggingFaceDataProcessor):
"""
A HuggingFace Eli5 Iterable 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_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):
num_workers = params.get("num_workers", 0)
split = params["split"]
self.dataset, self.data_collator = HuggingFace_Eli5(
split=split, num_workers=num_workers
)
# Convert to an IterableDataset
self.dataset = self.dataset.to_iterable_dataset(
num_shards=(num_tasks() * num_workers)
)
# The super class will take care of sharding the dataset and creating the dataloader
super().__init__(params)