Source code for transformers.configuration_encoder_decoder

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# 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.

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