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Source code for transformers.configuration_encoder_decoder
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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import copy
import logging
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
[docs]class EncoderDecoderConfig(PretrainedConfig):
r"""
:class:`~transformers.EncoderDecoderConfig` is the configuration class to store the configuration of a `EncoderDecoderModel`.
It is used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder configs.
Configuration objects inherit from :class:`~transformers.PretrainedConfig`
and can be used to control the model outputs.
See the documentation for :class:`~transformers.PretrainedConfig` for more information.
Args:
kwargs (`optional`):
Remaining dictionary of keyword arguments. Notably:
encoder (:class:`PretrainedConfig`, optional, defaults to `None`):
An instance of a configuration object that defines the encoder config.
encoder (:class:`PretrainedConfig`, optional, defaults to `None`):
An instance of a configuration object that defines the decoder config.
Example::
from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
# Initializing a BERT bert-base-uncased style configuration
config_encoder = BertConfig()
config_decoder = BertConfig()
config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
# Initializing a Bert2Bert model from the bert-base-uncased style configurations
model = EncoderDecoderModel(config=config)
# Accessing the model configuration
config_encoder = model.config.encoder
config_decoder = model.config.decoder
"""
model_type = "encoder_decoder"
def __init__(self, **kwargs):
super().__init__(**kwargs)
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
encoder_config = kwargs.pop("encoder")
encoder_model_type = encoder_config.pop("model_type")
decoder_config = kwargs.pop("decoder")
decoder_model_type = decoder_config.pop("model_type")
from transformers import AutoConfig
self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config)
self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config)
self.is_encoder_decoder = True
[docs] @classmethod
def from_encoder_decoder_configs(
cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig
) -> PretrainedConfig:
r"""
Instantiate a :class:`~transformers.EncoderDecoderConfig` (or a derived class) from a pre-trained encoder model configuration and decoder model configuration.
Returns:
:class:`EncoderDecoderConfig`: An instance of a configuration object
"""
return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict())
[docs] def to_dict(self):
"""
Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig`.
Returns:
:obj:`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
"""
output = copy.deepcopy(self.__dict__)
output["encoder"] = self.encoder.to_dict()
output["decoder"] = self.decoder.to_dict()
output["model_type"] = self.__class__.model_type
return output