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

# 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
Config classes of T5 data Configs

"""

from dataclasses import dataclass
from typing import List, Optional, Union

from cerebras.modelzoo.common.registry import registry
from cerebras.modelzoo.config_manager.config_classes.base.base_config import (
    required,
)
from cerebras.modelzoo.config_manager.config_classes.base.data_config import (
    DataProcessorConfig,
)
from cerebras.modelzoo.data.common.config import HDF5IterableDataProcessorConfig


[docs]@registry.register_data_config("T5DynamicDataProcessor") @dataclass class T5DynamicDataProcessorConfig(DataProcessorConfig): src_data_dir: str = required src_vocab_file: str = required src_max_sequence_length: int = required tgt_max_sequence_length: int = required shuffle_buffer: Optional[int] = None do_lower: bool = False buckets: Optional[List[int]] = None dynamic_loss_weight: Optional[bool] = None pack_sequences: Optional[bool] = False num_documents_to_concatenate: int = 128 num_workers: int = 0 drop_last: bool = True prefetch_factor: int = 10 persistent_workers: bool = True oov_token: str = "<unk>" sos_token: str = "<s>" eos_token: str = "</s>" pad_token: str = "<pad>" extra_ids: Union[int, List[int]] = 0 labels_pad_id: int = 0 input_pad_id: int = 0
[docs]@registry.register_data_config("T5HDF5DataProcessor") @dataclass class T5HDF5DataProcessorConfig(HDF5IterableDataProcessorConfig): data_dir: Union[str, List[str]] = required "The path to the HDF5 files." num_workers: int = 0 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. """ use_vsl: bool = True """ Flag to enable variable sequence length training. It requires the dataset to have two extra features"""