Source code for transformers.models.encoder_decoder.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");
# 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
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# 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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import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)


class EncoderDecoderConfig(PretrainedConfig):
    r"""
    :class:`~transformers.EncoderDecoderConfig` is the configuration class to store the configuration of a
    :class:`~transformers.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. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.

    Args:
        kwargs (`optional`):
            Dictionary of keyword arguments. Notably:

                - **encoder** (:class:`~transformers.PretrainedConfig`, `optional`) -- An instance of a configuration
                  object that defines the encoder config.
                - **decoder** (:class:`~transformers.PretrainedConfig`, `optional`) -- An instance of a configuration
                  object that defines the decoder config.

    Examples::

        >>> 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
        >>> # set decoder config to causal lm
        >>> config_decoder.is_decoder = True
        >>> config_decoder.add_cross_attention = True

        >>> # Saving the model, including its configuration
        >>> model.save_pretrained('my-model')

        >>> # loading model and config from pretrained folder
        >>> encoder_decoder_config = EncoderDecoderConfig.from_pretrained('my-model')
        >>> model = EncoderDecoderModel.from_pretrained('my-model', config=encoder_decoder_config)
    """
    model_type = "encoder-decoder"
    is_composition = True

    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 ..auto.configuration_auto 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

    @classmethod
    def from_encoder_decoder_configs(
        cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
    ) -> 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
        """
        logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
        decoder_config.is_decoder = True
        decoder_config.add_cross_attention = True

        return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs)

    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