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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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"""Pytorch HuggingFace Dataloader"""
from typing import Literal, Optional
import torch
from datasets.distributed import split_dataset_by_node
from cerebras.modelzoo.common.input_utils import get_streaming_batch_size
from cerebras.modelzoo.config import DataConfig
from cerebras.modelzoo.data.common.input_utils import num_tasks, task_id
[docs]class HuggingFaceDataProcessorConfig(DataConfig):
data_processor: Literal["HuggingFaceDataProcessor"]
batch_size: int = ...
"Batch size."
shuffle: bool = False
"Flag to enable data shuffling."
shuffle_seed: Optional[int] = None
"Shuffle seed."
shuffle_buffer: Optional[int] = None
"Size of shuffle buffer in samples."
num_workers: int = 0
"How many subprocesses to use for data loading."
drop_last: bool = True
"""
If True and the dataset size is not divisible by the batch size, the last
incomplete batch will be dropped.
"""
prefetch_factor: Optional[int] = 10
"Number of batches loaded in advance by each worker."
persistent_workers: bool = True
"""
If True, the data loader will not shutdown the worker processes after a
dataset has been consumed once.
"""
[docs]class HuggingFaceDataProcessor:
"""
A HuggingFace map-style Data Processor.
:param dict params: dict containing training
input parameters for creating dataset.
Expects the following fields:
"""
def __init__(self, config: HuggingFaceDataProcessorConfig, dataset):
if isinstance(config, dict):
config = HuggingFaceDataProcessorConfig(**config)
super().__init__()
self.batch_size = get_streaming_batch_size(config.batch_size)
self.shuffle = config.shuffle
self.shuffle_seed = config.shuffle_seed
self.shuffle_buffer = config.shuffle_buffer
if self.shuffle_buffer is None:
self.shuffle_buffer = 10 * self.batch_size
self.num_workers = config.num_workers
self.drop_last = config.drop_last
self.prefetch_factor = config.prefetch_factor
self.persistent_workers = config.persistent_workers
assert self.batch_size > 0, "Batch size should be positive."
self.dataset = dataset
if isinstance(self.dataset, torch.utils.data.IterableDataset):
self.map_style_dataset = False
else:
self.map_style_dataset = True
if not hasattr(self, "data_collator"):
self.data_collator = None
self.dataset = split_dataset_by_node(
self.dataset, world_size=num_tasks(), rank=task_id()
)
if self.shuffle and not self.map_style_dataset:
self.dataset = self.dataset.shuffle(
buffer_size=self.shuffle_buffer, seed=self.shuffle_seed
)
else:
torch.manual_seed(self.shuffle_seed)
[docs] def create_dataloader(self):
"""
Classmethod to create the dataloader object.
"""
data_loader = torch.utils.data.DataLoader(
self.dataset,
shuffle=self.shuffle if self.map_style_dataset else False,
batch_size=self.batch_size,
drop_last=self.drop_last,
num_workers=self.num_workers,
prefetch_factor=(
self.prefetch_factor if self.num_workers > 0 else None
),
persistent_workers=(
self.persistent_workers if self.num_workers > 0 else False
),
collate_fn=self.data_collator,
)
return data_loader