# 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 HuggingFace Eli5 Iterable Dataloader"""
from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.data.common.input_utils import num_tasks
from cerebras.modelzoo.data_preparation.huggingface.HuggingFace_Eli5 import (
HuggingFace_Eli5,
)
from cerebras.modelzoo.data_preparation.huggingface.HuggingFaceDataProcessor import (
HuggingFaceDataProcessor,
)
[docs]@registry.register_datasetprocessor("HuggingFaceIterableDataProcessorEli5")
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.
"""
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)