# 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,
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"""
Config classes of T5 data Configs
"""
from dataclasses import dataclass
from typing import List, Optional
from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.config_manager.config_classes.base.data_config import (
DataProcessorConfig,
)
[docs]@registry.register_data_config("GenericDataProcessor")
@dataclass
class GenericDataProcessorConfig(DataProcessorConfig):
shuffle_buffer: Optional[int] = None
"Size of shuffle buffer in samples."
drop_last: bool = True
"""
similar to the PyTorch drop_last setting
except that samples that when set to True, samples that would
have been dropped at the end of one epoch are yielded at the
start of the next epoch so that there is no data loss. This is
necessary for a data ordering that is independent of the
distributed setup being used.
"""
[docs]@registry.register_data_config("HDF5IterableDataProcessor")
@dataclass
class HDF5IterableDataProcessorConfig(DataProcessorConfig):
shuffle_buffer: Optional[int] = None
"Size of shuffle buffer in samples."
drop_last: bool = True
"""
similar to the PyTorch drop_last setting
except that samples that when set to True, samples that would
have been dropped at the end of one epoch are yielded at the
start of the next epoch so that there is no data loss. This is
necessary for a data ordering that is independent of the
distributed setup being used.
"""
prefetch_factor: int = 10
persistent_workers: int = True
[docs]@registry.register_data_config("SyntheticDataProcessor")
@dataclass
class SyntheticDataProcessorConfig(DataProcessorConfig):
num_examples: Optional[int] = None
sampler: Optional[str] = None
batch_sampler: Optional[List[List[int]]] = None
pin_memory: bool = False
drop_last: bool = False
"""
similar to the PyTorch drop_last setting
except that samples that when set to True, samples that would
have been dropped at the end of one epoch are yielded at the
start of the next epoch so that there is no data loss. This is
necessary for a data ordering that is independent of the
distributed setup being used.
"""
timeout: bool = False
synthetic_special_tokens_index: Optional[dict] = None
[docs]@registry.register_data_config("HuggingFaceDataProcessor")
@dataclass
class HuggingFaceDataProcessorConfig(DataProcessorConfig):
shuffle_buffer: Optional[int] = None
"Size of shuffle buffer in samples."
drop_last: bool = True
"""
similar to the PyTorch drop_last setting
except that samples that when set to True, samples that would
have been dropped at the end of one epoch are yielded at the
start of the next epoch so that there is no data loss. This is
necessary for a data ordering that is independent of the
distributed setup being used.
"""
prefetch_factor: int = 10
persistent_workers: bool = True