Source code for transformers.models.encoder_decoder.configuration_encoder_decoder

# coding=utf-8
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# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
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import copy

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

logger = logging.get_logger(__name__)

[docs]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 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, **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 """"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)
[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