# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # 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. """ Classes to support Encoder-Decoder architectures """ import logging import os from torch import nn from .modeling_auto import AutoModel, AutoModelWithLMHead logger = logging.getLogger(__name__) class PreTrainedEncoderDecoder(nn.Module): r""" :class:`~transformers.PreTrainedEncoderDecoder` is a generic model class that will be instantiated as a transformer architecture with one of the base model classes of the library as encoder and (optionally) another one as decoder when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)` class method. """ def __init__(self, encoder, decoder): super().__init__() self.encoder = encoder self.decoder = decoder @classmethod def from_pretrained( cls, encoder_pretrained_model_name_or_path=None, decoder_pretrained_model_name_or_path=None, *model_args, **kwargs ): r""" Instantiates an encoder and a decoder from one or two base classes of the library from pre-trained model checkpoints. The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) To train the model, you need to first set it back in training mode with `model.train()` Params: encoder_pretrained_model_name_or_path: information necessary to initiate the encoder. Either: - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``. - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``. - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/encoder``. - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. decoder_pretrained_model_name_or_path: information necessary to initiate the decoder. Either: - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``. - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``. - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/decoder``. - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards. model_args: (`optional`) Sequence of positional arguments: All remaning positional arguments will be passed to the underlying model's ``__init__`` method config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`: Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when: - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory. - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory. state_dict: (`optional`) dict: an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file. This option can be used if you want to create a model from a pretrained configuration but load your own weights. In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option. cache_dir: (`optional`) string: Path to a directory in which a downloaded pre-trained model configuration should be cached if the standard cache should not be used. force_download: (`optional`) boolean, default False: Force to (re-)download the model weights and configuration files and override the cached versions if they exists. proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. output_loading_info: (`optional`) boolean: Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages. kwargs: (`optional`) Remaining dictionary of keyword arguments. Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded: - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done) - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function. You can specify kwargs sepcific for the encoder and decoder by prefixing the key with `encoder_` and `decoder_` respectively. (e.g. ``decoder_output_attention=True``). The remaining kwargs will be passed to both encoders and decoders. Examples:: # For example purposes. Not runnable. model = PreTrainedEncoderDecoder.from_pretained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert """ # keyword arguments come in 3 flavors: encoder-specific (prefixed by # `encoder_`), decoder-specific (prefixed by `decoder_`) and those # that apply to the model as a whole. # We let the specific kwargs override the common ones in case of conflict. kwargs_common = { argument: value for argument, value in kwargs.items() if not argument.startswith("encoder_") and not argument.startswith("decoder_") } kwargs_decoder = kwargs_common.copy() kwargs_encoder = kwargs_common.copy() kwargs_encoder.update( { argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_") } ) kwargs_decoder.update( { argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") } ) # Load and initialize the encoder and decoder # The distinction between encoder and decoder at the model level is made # by the value of the flag `is_decoder` that we need to set correctly. encoder = kwargs_encoder.pop("model", None) if encoder is None: encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder) encoder.config.is_decoder = False decoder = kwargs_decoder.pop("model", None) if decoder is None: decoder = AutoModelWithLMHead.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder) decoder.config.is_decoder = True model = cls(encoder, decoder) return model def save_pretrained(self, save_directory): """ Save a Seq2Seq model and its configuration file in a format such that it can be loaded using `:func:`~transformers.PreTrainedEncoderDecoder.from_pretrained` We save the encoder' and decoder's parameters in two separate directories. """ # If the root output directory does not exist, create it if not os.path.exists(save_directory): os.mkdir(save_directory) # Check whether the output directory is empty or not sub_directories = [ directory for directory in os.listdir(save_directory) if os.path.isdir(os.path.join(save_directory, directory)) ] if len(sub_directories) > 0: if "encoder" in sub_directories and "decoder" in sub_directories: print( "WARNING: there is an older version of encoder-decoder saved in" + " the output directory. The default behaviour is to overwrite them." ) # Empty the output directory for directory_to_remove in sub_directories: # Remove all files into the subdirectory files_to_remove = os.listdir(os.path.join(save_directory, directory_to_remove)) for file_to_remove in files_to_remove: os.remove(os.path.join(save_directory, directory_to_remove, file_to_remove)) # Remove the subdirectory itself os.rmdir(os.path.join(save_directory, directory_to_remove)) assert len(os.listdir(save_directory)) == 0 # sanity check # Create the "encoder" directory inside the output directory and save the encoder into it if not os.path.exists(os.path.join(save_directory, "encoder")): os.mkdir(os.path.join(save_directory, "encoder")) self.encoder.save_pretrained(os.path.join(save_directory, "encoder")) # Create the "encoder" directory inside the output directory and save the decoder into it if not os.path.exists(os.path.join(save_directory, "decoder")): os.mkdir(os.path.join(save_directory, "decoder")) self.decoder.save_pretrained(os.path.join(save_directory, "decoder")) def forward(self, encoder_input_ids, decoder_input_ids, **kwargs): """ The forward pass on a seq2eq depends what we are performing: - During training we perform one forward pass through both the encoder and decoder; - During prediction, we perform one forward pass through the encoder, and then perform several forward passes with the encoder's hidden state through the decoder to decode a full sequence. Therefore, we skip the forward pass on the encoder if an argument named `encoder_hidden_state` is passed to this function. Params: encoder_input_ids: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)`` Indices of encoder input sequence tokens in the vocabulary. decoder_input_ids: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)`` Indices of decoder input sequence tokens in the vocabulary. kwargs: (`optional`) Remaining dictionary of keyword arguments. """ kwargs_encoder, kwargs_decoder = self.prepare_model_kwargs(**kwargs) # Encode if needed (training, first prediction pass) encoder_hidden_states = kwargs_encoder.pop("hidden_states", None) if encoder_hidden_states is None: encoder_outputs = self.encoder(encoder_input_ids, **kwargs_encoder) encoder_hidden_states = encoder_outputs[0] else: encoder_outputs = () kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states decoder_outputs = self.decoder(decoder_input_ids, **kwargs_decoder) return decoder_outputs + encoder_outputs