transformers/transformers/modeling_seq2seq.py

# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
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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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" Auto Model class. """

from __future__ import absolute_import, division, print_function, unicode_literals

import logging

import torch
from torch import nn

from .modeling_bert import BertModel, BertForMaskedLM, BertForSequenceClassification, BertForQuestionAnswering
from .modeling_openai import OpenAIGPTModel, OpenAIGPTLMHeadModel
from .modeling_gpt2 import GPT2Model, GPT2LMHeadModel
from .modeling_transfo_xl import TransfoXLModel, TransfoXLLMHeadModel
from .modeling_xlnet import XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering
from .modeling_xlm import XLMModel, XLMWithLMHeadModel, XLMForSequenceClassification, XLMForQuestionAnswering
from .modeling_roberta import RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification
from .modeling_distilbert import DistilBertModel, DistilBertForQuestionAnswering, DistilBertForMaskedLM, DistilBertForSequenceClassification

from .modeling_utils import PreTrainedModel, SequenceSummary

from .file_utils import add_start_docstrings

logger = logging.getLogger(__name__)


class PreTrainedSeq2seq(nn.Module):
    r"""
        :class:`~transformers.Seq2seq` is a generic model class
        that will be instantiated as a Seq2seq model with one of the base model classes of the library
        as encoder and (optionally) as decoder when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
        class method.

        The `from_pretrained()` method takes care of returning the correct model class instance
        using pattern matching on the `pretrained_model_name_or_path` string.

        The base model class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
            - contains `distilbert`: DistilBertModel (DistilBERT model)
            - contains `roberta`: RobertaModel (RoBERTa model)
            - contains `bert`: BertModel (Bert model)
            - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
            - contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
            - contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
            - contains `xlnet`: XLNetModel (XLNet model)
            - contains `xlm`: XLMModel (XLM model)

        This class cannot be instantiated using `__init__()` (throws an error).
    """
    def __init__(self, encoder, decoder):
        super(PreTrainedSeq2seq, self).__init__()
        self.encoder = encoder
        self.decoder = decoder

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r""" Instantiates one of the base model classes of the library
        from a pre-trained model configuration.

        The model class to instantiate is selected as the first pattern matching
        in the `pretrained_model_name_or_path` string (in the following order):
            - contains `distilbert`: DistilBertModel (DistilBERT model)
            - contains `roberta`: RobertaModel (RoBERTa model)
            - contains `bert`: BertModel (Bert model)
            - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
            - contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
            - contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
            - contains `xlnet`: XLNetModel (XLNet model)
            - contains `xlm`: XLMModel (XLM model)

            The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
            To train the model, you should first set it back in training mode with `model.train()`

        Params:
            pretrained_model_name_or_path: either:

                - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                - 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.

        Examples::

            model = AutoModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
            model = AutoModel.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
            model = AutoModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
            assert model.config.output_attention == True
            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)

        """
        # Extract encoder and decoder model if provided
        encoder_model = kwargs.pop('encoder_model', None)
        decoder_model = kwargs.pop('decoder_model', None)

        # Extract decoder kwargs so we only have encoder kwargs for now
        if decoder_model is None:
            decoder_pretrained_model_name_or_path = kwargs.pop('decoder_pretrained_model_name_or_path', pretrained_model_name_or_path)
        decoder_kwargs = {}
        for key in kwargs.keys():
            if key.startswith('decoder_'):
                decoder_kwargs[key.replace('decoder_', '')] = kwargs.pop(key)

        # Load and initialize the decoder
        if encoder_model:
            encoder = encoder_model
        else:
            # Load and initialize the encoder
            kwargs['is_decoder'] = False  # Make sure the encoder will be an encoder
            if 'distilbert' in pretrained_model_name_or_path:
                encoder = DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
            elif 'roberta' in pretrained_model_name_or_path:
                encoder = RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
            elif 'bert' in pretrained_model_name_or_path:
                encoder = BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
            elif 'openai-gpt' in pretrained_model_name_or_path:
                encoder = OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
            elif 'gpt2' in pretrained_model_name_or_path:
                encoder = GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
            elif 'transfo-xl' in pretrained_model_name_or_path:
                encoder = TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
            elif 'xlnet' in pretrained_model_name_or_path:
                encoder = XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
            elif 'xlm' in pretrained_model_name_or_path:
                encoder = XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
            else:
                raise ValueError("Unrecognized model identifier in {}. Should contains one of "
                                "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
                                "'xlm', 'roberta'".format(pretrained_model_name_or_path))

        # Load and initialize the decoder
        if decoder_model:
            decoder = decoder_model
        else:
            kwargs.update(decoder_kwargs)  # Replace encoder kwargs with decoder specific kwargs like config, state_dict, etc...
            kwargs['is_decoder'] = True  # Make sure the decoder will be an decoder
            if 'distilbert' in decoder_pretrained_model_name_or_path:
                decoder = DistilBertModel.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs)
            elif 'roberta' in decoder_pretrained_model_name_or_path:
                decoder = RobertaModel.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs)
            elif 'bert' in decoder_pretrained_model_name_or_path:
                decoder = BertModel.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs)
            elif 'openai-gpt' in decoder_pretrained_model_name_or_path:
                decoder = OpenAIGPTModel.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs)
            elif 'gpt2' in decoder_pretrained_model_name_or_path:
                decoder = GPT2Model.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs)
            elif 'transfo-xl' in decoder_pretrained_model_name_or_path:
                decoder = TransfoXLModel.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs)
            elif 'xlnet' in decoder_pretrained_model_name_or_path:
                decoder = XLNetModel.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs)
            elif 'xlm' in decoder_pretrained_model_name_or_path:
                decoder = XLMModel.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs)
            else:
                raise ValueError("Unrecognized model identifier in {}. Should contains one of "
                                "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
                                "'xlm', 'roberta'".format(decoder_pretrained_model_name_or_path))

        model = cls(encoder, decoder)
        return model

    def forward(self, *inputs, *kwargs):
        # Extract decoder inputs
        decoder_kwargs = {}
        for key in kwargs.keys():
            if key.startswith('decoder_'):
                decoder_kwargs[key.replace('decoder_', '')] = kwargs.pop(key)

        # Compute encoder hidden states if needed
        encoder_hidden_states = kwargs.pop('encoder_hidden_states', None)
        if encoder_hidden_states is None:
            encoder_outputs = self.encoder(*inputs, *kwargs)
            encoder_hidden_states = encoder_outputs[0]
        else:
            encoder_outputs = (,)

        # Decode
        decoder_kwargs['encoder_hidden_states'] = encoder_hidden_states
        decoder_outputs = self.decoder(**decoder_kwargs)

        return decoder_outputs + encoder_outputs


class Model2Model(PreTrainedSeq2seq):
    def tie_weights():
        # We should tie encoder and decoder embeddings if possible here
        pass


class Model2LSTM(PreTrainedSeq2seq):
    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        if kwargs.get('decoder_model', None) is None:
            # We will create a randomly initilized LSTM model as decoder
            if 'decoder_config' not in kwargs:
                raise ValueError("To load an LSTM in Seq2seq model, please supply either: "
                                "    - a torch.nn.LSTM model as `decoder_model` parameter (`decoder_model=lstm_model`), or "
                                "    - a dictionary of configuration parameters that will be used to initialize a
                                "        torch.nn.LSTM model as `decoder_config` keyword argument. "
                                "        E.g. `decoder_config=\{'input_size': 768, 'hidden_size': 768, 'num_layers': 2\}`")
            kwargs['decoder_model'] = torch.nn.LSTM(kwarg.pop('decoder_config'))
        model = super(Model2LSTM, cls).from_pretrained(*args, **kwargs)
        return model