Source code for cerebras.modelzoo.data.nlp.t5.T5HDF5DataProcessor

# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Pytorch T5/Transformer Dataloader"""

from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.data.common.HDF5IterableDataProcessor import (
    HDF5IterableDataProcessor,
)
from cerebras.modelzoo.data.common.HDF5IterableDataset import (
    HDF5IterableDataset,
)


[docs]@registry.register_datasetprocessor("T5HDF5DataProcessor") 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. """ 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)