Clean Encoder-Decoder models with Bart/T5-like API and add generate possibility (#3383)

* change encoder decoder style to bart & t5 style

* make encoder decoder generation dummy work for bert

* make style

* clean init config in encoder decoder

* add tests for encoder decoder models

* refactor and add last tests

* refactor and add last tests

* fix attn masks for bert encoder decoder

* make style

* refactor prepare inputs for Bert

* refactor

* finish encoder decoder

* correct typo

* add docstring to config

* finish

* add tests

* better naming

* make style

* fix flake8

* clean docstring

* make style

* rename

docs/source/index.rst

@@ -89,6 +89,7 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
     :caption: Package Reference
 
     model_doc/auto
+    model_doc/encoderdecoder
     model_doc/bert
     model_doc/gpt
     model_doc/transformerxl

docs/source/model_doc/encoderdecoder.rst

+Encoder Decoder Models
+-----------
+
+This class can wrap an encoder model, such as ``BertModel`` and a decoder modeling with a language modeling head, such as ``BertForMaskedLM`` into a encoder-decoder model.
+
+The ``EncoderDecoderModel`` class allows to instantiate a encoder decoder model using the ``from_encoder_decoder_pretrain`` class method taking a pretrained encoder and pretrained decoder model as an input. 
+The ``EncoderDecoderModel`` is saved using the standard ``save_pretrained()`` method and can also again be loaded using the standard ``from_pretrained()`` method. 
+
+An application of this architecture could be *summarization* using two pretrained Bert models as is shown in the paper: `Text Summarization with Pretrained Encoders <https://arxiv.org/abs/1910.13461>`_ by Yang Liu and Mirella Lapata. 
+
+
+``EncoderDecoderConfig``
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.EncoderDecoderConfig
+    :members:
+
+
+``EncoderDecoderModel``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.EncoderDecoderModel
+    :members:

src/transformers/__init__.py

@@ -41,6 +41,7 @@ from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, Ca
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
 from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig
+from .configuration_encoder_decoder import EncoderDecoderConfig
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
 from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
 from .configuration_mmbt import MMBTConfig
@@ -267,7 +268,7 @@ if is_torch_available():
         CamembertForQuestionAnswering,
         CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     )
-    from .modeling_encoder_decoder import PreTrainedEncoderDecoder
+    from .modeling_encoder_decoder import EncoderDecoderModel
     from .modeling_t5 import (
         T5PreTrainedModel,
         T5Model,

src/transformers/configuration_auto.py

@@ -25,6 +25,7 @@ from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, Ca
 from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
 from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig
+from .configuration_encoder_decoder import EncoderDecoderConfig
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
 from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
 from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
@@ -82,6 +83,7 @@ CONFIG_MAPPING = OrderedDict(
         ("xlm", XLMConfig,),
         ("ctrl", CTRLConfig,),
         ("electra", ElectraConfig,),
+        ("encoder-decoder", EncoderDecoderConfig,),
     ]
 )
 

src/transformers/configuration_encoder_decoder.py

+# 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__)
+
+
+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.
+
+
+        Arguments:
+            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.
+    """
+    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
+
+    @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())
+
+    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

src/transformers/modeling_auto.py

@@ -27,6 +27,7 @@ from .configuration_auto import (
     CTRLConfig,
     DistilBertConfig,
     ElectraConfig,
+    EncoderDecoderConfig,
     FlaubertConfig,
     GPT2Config,
     OpenAIGPTConfig,
@@ -86,6 +87,7 @@ from .modeling_electra import (
     ElectraForTokenClassification,
     ElectraModel,
 )
+from .modeling_encoder_decoder import EncoderDecoderModel
 from .modeling_flaubert import (
     FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     FlaubertForQuestionAnsweringSimple,
@@ -219,6 +221,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
         (XLMConfig, XLMWithLMHeadModel),
         (CTRLConfig, CTRLLMHeadModel),
         (ElectraConfig, ElectraForMaskedLM),
+        (EncoderDecoderConfig, EncoderDecoderModel),
     ]
 )
 

src/transformers/modeling_bert.py

@@ -959,6 +959,28 @@ class BertForMaskedLM(BertPreTrainedModel):
 
         return outputs  # (ltr_lm_loss), (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
 
+    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
+        input_shape = input_ids.shape
+        effective_batch_size = input_shape[0]
+
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_shape)
+
+        # if model is does not use a causal mask then add a dummy token
+        if self.config.is_decoder is False:
+            assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
+            attention_mask = torch.cat(
+                [attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1
+            )
+
+            dummy_token = torch.full(
+                (effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
+            )
+            input_ids = torch.cat([input_ids, dummy_token], dim=1)
+
+        return {"input_ids": input_ids, "attention_mask": attention_mask}
+
 
 @add_start_docstrings(
     """Bert Model with a `next sentence prediction (classification)` head on top. """, BERT_START_DOCSTRING,

src/transformers/modeling_encoder_decoder.py

@@ -16,53 +16,101 @@
 
 
 import logging
-import os
+from typing import Optional
 
-from torch import nn
-
-from .modeling_auto import AutoModel, AutoModelWithLMHead
+from .configuration_encoder_decoder import EncoderDecoderConfig
+from .configuration_utils import PretrainedConfig
+from .modeling_utils import PreTrainedModel
 
 
 logger = logging.getLogger(__name__)
 
 
-class PreTrainedEncoderDecoder(nn.Module):
+class EncoderDecoderModel(PreTrainedModel):
     r"""
-        :class:`~transformers.PreTrainedEncoderDecoder` is a generic model class that will be
+        :class:`~transformers.EncoderDecoder` 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
+        classes of the library as encoder and another one as
         decoder when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
-        class method.
+        class method for the encoder and `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` class method for the decoder.
     """
+    config_class = EncoderDecoderConfig
+
+    def __init__(
+        self,
+        config: Optional[PretrainedConfig] = None,
+        encoder: Optional[PreTrainedModel] = None,
+        decoder: Optional[PreTrainedModel] = None,
+    ):
+        assert config is not None or (
+            encoder is not None and decoder is not None
+        ), "Either a configuration or an Encoder and a decoder has to be provided"
+        if config is None:
+            config = EncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config)
+        else:
+            assert isinstance(config, self.config_class), "config: {} has to be of type {}".format(
+                config, self.config_class
+            )
+        # initialize with config
+        super().__init__(config)
+
+        if encoder is None:
+            from transformers import AutoModel
+
+            encoder = AutoModel.from_config(config.encoder)
+
+        if decoder is None:
+            from transformers import AutoModelWithLMHead
+
+            decoder = AutoModelWithLMHead.from_config(config.decoder)
 
-    def __init__(self, encoder, decoder):
-        super().__init__()
         self.encoder = encoder
         self.decoder = decoder
+        assert (
+            self.encoder.get_output_embeddings() is None
+        ), "The encoder {} should not have a LM Head. Please use a model without LM Head"
+
+    def tie_weights(self):
+        # for now no weights tying in encoder-decoder
+        pass
+
+    def get_encoder(self):
+        return self.encoder
+
+    def get_decoder(self):
+        return self.decoder
+
+    def get_input_embeddings(self):
+        return self.encoder.get_input_embeddings()
+
+    def get_output_embeddings(self):
+        return self.decoder.get_output_embeddings()
 
     @classmethod
-    def from_pretrained(
+    def from_encoder_decoder_pretrained(
         cls,
-        encoder_pretrained_model_name_or_path=None,
-        decoder_pretrained_model_name_or_path=None,
+        encoder_pretrained_model_name_or_path: str = None,
+        decoder_pretrained_model_name_or_path: str = None,
         *model_args,
         **kwargs
-    ):
+    ) -> PreTrainedModel:
         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()`
+        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:
+            encoder_pretrained_model_name_or_path (:obj: `str`, `optional`, defaults to `None`):
+                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:
+            decoder_pretrained_model_name_or_path (:obj: `str`, `optional`, defaults to `None`):
+                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``.
@@ -72,165 +120,169 @@ class PreTrainedEncoderDecoder(nn.Module):
             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_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert
+            model = EncoderDecoder.from_encoder_decoder_pretrained('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_encoder = {
+            argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")
+        }
+
+        kwargs_decoder = {
+            argument[len("decoder_") :]: value for argument, value in kwargs.items() if 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:
+            assert (
+                encoder_pretrained_model_name_or_path is not None
+            ), "If `model` is not defined as an argument, a `encoder_pretrained_model_name_or_path` has to be defined"
+            from .modeling_auto import AutoModel
+
             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:
+            assert (
+                decoder_pretrained_model_name_or_path is not None
+            ), "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has to be defined"
+            from .modeling_auto import AutoModelWithLMHead
+
             decoder = AutoModelWithLMHead.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
         decoder.config.is_decoder = True
 
-        model = cls(encoder, decoder)
+        model = cls(encoder=encoder, decoder=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`
+    def forward(
+        self,
+        input_ids=None,
+        inputs_embeds=None,
+        attention_mask=None,
+        head_mask=None,
+        encoder_outputs=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        decoder_head_mask=None,
+        decoder_inputs_embeds=None,
+        masked_lm_labels=None,
+        lm_labels=None,
+        **kwargs,
+    ):
 
-        We save the encoder' and decoder's parameters in two separate directories.
+        """
+        Args:
+            input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary for the encoder.
+                Indices can be obtained using :class:`transformers.PretrainedTokenizer`.
+                See :func:`transformers.PreTrainedTokenizer.encode` and
+                :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
+                Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+                Mask to avoid performing attention on padding token indices for the encoder.
+                Mask values selected in ``[0, 1]``:
+                ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
+            head_mask: (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
+                Mask to nullify selected heads of the self-attention modules for the encoder.
+                Mask values selected in ``[0, 1]``:
+                ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
+            encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`, defaults to :obj:`None`):
+                Tuple consists of (`last_hidden_state`, `optional`: `hidden_states`, `optional`: `attentions`)
+                `last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`) is a sequence of hidden-states at the output of the last layer of the encoder.
+                Used in the cross-attention of the decoder.
+            decoder_input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, target_sequence_length)`, `optional`, defaults to :obj:`None`):
+                Provide for sequence to sequence training to the decoder.
+                Indices can be obtained using :class:`transformers.PretrainedTokenizer`.
+                See :func:`transformers.PreTrainedTokenizer.encode` and
+                :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
+            decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
+                Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
+            decoder_head_mask: (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
+                Mask to nullify selected heads of the self-attention modules for the decoder.
+                Mask values selected in ``[0, 1]``:
+                ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
+            decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
+                Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            masked_lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+                Labels for computing the masked language modeling loss for the decoder.
+                Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
+                Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
+                in ``[0, ..., config.vocab_size]``
+            lm_labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
+                Labels for computing the left-to-right language modeling loss (next word prediction) for the decoder.
+                Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
+                Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
+                in ``[0, ..., config.vocab_size]``
+            kwargs: (`optional`) Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
+                - Without a prefix which will be input as `**encoder_kwargs` for the encoder forward function.
+                - With a `decoder_` prefix which will be input as `**decoder_kwargs` for the decoder forward function.
         """
 
-        # 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.
+        kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
 
-        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)
+        kwargs_decoder = {
+            argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
+        }
 
-        # 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]
+        if encoder_outputs is None:
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                inputs_embeds=inputs_embeds,
+                head_mask=head_mask,
+                **kwargs_encoder,
+            )
+
+        hidden_states = encoder_outputs[0]
+
+        # Decode
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            inputs_embeds=decoder_inputs_embeds,
+            attention_mask=decoder_attention_mask,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=attention_mask,
+            head_mask=decoder_head_mask,
+            lm_labels=lm_labels,
+            masked_lm_labels=masked_lm_labels,
+            **kwargs_decoder,
+        )
+
+        return decoder_outputs + encoder_outputs
+
+    def prepare_inputs_for_generation(self, input_ids, past, attention_mask, **kwargs):
+        assert past is not None, "past has to be defined for encoder_outputs"
+
+        # first step
+        if type(past) is tuple:
+            encoder_outputs = past
         else:
-            encoder_outputs = ()
+            encoder_outputs = (past,)
 
-        kwargs_decoder["encoder_hidden_states"] = encoder_hidden_states
-        decoder_outputs = self.decoder(decoder_input_ids, **kwargs_decoder)
+        decoder_inputs = self.decoder.prepare_inputs_for_generation(input_ids)
 
-        return decoder_outputs + encoder_outputs
+        return {
+            "attention_mask": attention_mask,
+            "decoder_attention_mask": decoder_inputs["attention_mask"],
+            "decoder_input_ids": decoder_inputs["input_ids"],
+            "encoder_outputs": encoder_outputs,
+        }
+
+    def _reorder_cache(self, past, beam_idx):
+        # as a default encoder-decoder models do not re-order the past.
+        # TODO(PVP): might have to be updated, e.g. if GPT2 is to be used as a decoder
+        return past

src/transformers/modeling_utils.py

@@ -1011,7 +1011,14 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             pad_token_id = eos_token_id
 
         # current position and vocab size
-        vocab_size = self.config.vocab_size
+        if hasattr(self.config, "vocab_size"):
+            vocab_size = self.config.vocab_size
+        elif (
+            self.config.is_encoder_decoder
+            and hasattr(self.config, "decoder")
+            and hasattr(self.config.decoder, "vocab_size")
+        ):
+            vocab_size = self.config.decoder.vocab_size
 
         # set effective batch size and effective batch multiplier according to do_sample
         if do_sample:

src/transformers/utils_encoder_decoder.py

-# coding=utf-8
-# Copyright 2020 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 """
-
-
-def prepare_encoder_decoder_model_kwargs(**kwargs):
-    """ Prepare the encoder and decoder's keyword arguments.
-
-    Keyword arguments come in 3 flavors:
-    - encoder-specific (prefixed by `encoder_`)
-    - decoder-specific (prefixed by `decoder_`)
-    - those that apply to the model as 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_")
-    }
-    if "input_ids" in kwargs_common:
-        kwargs["encoder_input_ids"] = kwargs_common.pop("input_ids")
-
-    decoder_kwargs = kwargs_common.copy()
-    encoder_kwargs = kwargs_common.copy()
-    encoder_kwargs.update(
-        {argument[len("encoder_") :]: value for argument, value in kwargs.items() if argument.startswith("encoder_")}
-    )
-    decoder_kwargs.update(
-        {argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")}
-    )
-    decoder_kwargs["encoder_attention_mask"] = encoder_kwargs.get("attention_mask", None)
-    return encoder_kwargs, decoder_kwargs

tests/test_modeling_encoder_decoder.py

+# coding=utf-8
+# Copyright 2020 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.
+
+
+import tempfile
+import unittest
+
+from transformers import is_torch_available
+
+# TODO(PVP): this line reruns all the tests in BertModelTest; not sure whether this can be prevented
+# for now only run module with pytest tests/test_modeling_encoder_decoder.py::EncoderDecoderModelTest
+from .test_modeling_bert import BertModelTest
+from .utils import require_torch, slow, torch_device
+
+
+if is_torch_available():
+    from transformers import BertModel, BertForMaskedLM, EncoderDecoderModel
+    import numpy as np
+    import torch
+
+
+@require_torch
+class EncoderDecoderModelTest(unittest.TestCase):
+    def prepare_config_and_inputs_bert(self):
+        bert_model_tester = BertModelTest.BertModelTester(self)
+        encoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
+        decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = encoder_config_and_inputs
+        (
+            decoder_config,
+            decoder_input_ids,
+            decoder_token_type_ids,
+            decoder_input_mask,
+            decoder_sequence_labels,
+            decoder_token_labels,
+            decoder_choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        ) = decoder_config_and_inputs
+        return {
+            "config": config,
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "decoder_config": decoder_config,
+            "decoder_input_ids": decoder_input_ids,
+            "decoder_token_type_ids": decoder_token_type_ids,
+            "decoder_attention_mask": decoder_input_mask,
+            "decoder_sequence_labels": decoder_sequence_labels,
+            "decoder_token_labels": decoder_token_labels,
+            "decoder_choice_labels": decoder_choice_labels,
+            "encoder_hidden_states": encoder_hidden_states,
+            "lm_labels": decoder_token_labels,
+            "masked_lm_labels": decoder_token_labels,
+        }
+
+    def create_and_check_bert_encoder_decoder_model(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        encoder_hidden_states,
+        decoder_config,
+        decoder_input_ids,
+        decoder_attention_mask,
+        **kwargs
+    ):
+        encoder_model = BertModel(config)
+        decoder_model = BertForMaskedLM(decoder_config)
+        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
+        enc_dec_model.to(torch_device)
+        outputs_encoder_decoder = enc_dec_model(
+            input_ids=input_ids,
+            decoder_input_ids=decoder_input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=decoder_attention_mask,
+        )
+
+        self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
+        self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
+        encoder_outputs = (encoder_hidden_states,)
+        outputs_encoder_decoder = enc_dec_model(
+            encoder_outputs=encoder_outputs,
+            decoder_input_ids=decoder_input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=decoder_attention_mask,
+        )
+
+        self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
+        self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
+
+    def create_and_check_bert_encoder_decoder_model_from_pretrained(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        encoder_hidden_states,
+        decoder_config,
+        decoder_input_ids,
+        decoder_attention_mask,
+        **kwargs
+    ):
+        encoder_model = BertModel(config)
+        decoder_model = BertForMaskedLM(decoder_config)
+        kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
+        enc_dec_model = EncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
+        enc_dec_model.to(torch_device)
+        outputs_encoder_decoder = enc_dec_model(
+            input_ids=input_ids,
+            decoder_input_ids=decoder_input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=decoder_attention_mask,
+        )
+
+        self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
+        self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
+
+    def create_and_check_save_and_load(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        encoder_hidden_states,
+        decoder_config,
+        decoder_input_ids,
+        decoder_attention_mask,
+        **kwargs
+    ):
+        encoder_model = BertModel(config)
+        decoder_model = BertForMaskedLM(decoder_config)
+        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
+        enc_dec_model.to(torch_device)
+        enc_dec_model.eval()
+        with torch.no_grad():
+            outputs = enc_dec_model(
+                input_ids=input_ids,
+                decoder_input_ids=decoder_input_ids,
+                attention_mask=attention_mask,
+                decoder_attention_mask=decoder_attention_mask,
+            )
+            out_2 = outputs[0].cpu().numpy()
+            out_2[np.isnan(out_2)] = 0
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                enc_dec_model.save_pretrained(tmpdirname)
+                EncoderDecoderModel.from_pretrained(tmpdirname)
+
+                after_outputs = enc_dec_model(
+                    input_ids=input_ids,
+                    decoder_input_ids=decoder_input_ids,
+                    attention_mask=attention_mask,
+                    decoder_attention_mask=decoder_attention_mask,
+                )
+                out_1 = after_outputs[0].cpu().numpy()
+                out_1[np.isnan(out_1)] = 0
+                max_diff = np.amax(np.abs(out_1 - out_2))
+                self.assertLessEqual(max_diff, 1e-5)
+
+    def create_and_check_save_and_load_encoder_decoder_model(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        encoder_hidden_states,
+        decoder_config,
+        decoder_input_ids,
+        decoder_attention_mask,
+        **kwargs
+    ):
+        encoder_model = BertModel(config)
+        decoder_model = BertForMaskedLM(decoder_config)
+        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
+        enc_dec_model.to(torch_device)
+        enc_dec_model.eval()
+        with torch.no_grad():
+            outputs = enc_dec_model(
+                input_ids=input_ids,
+                decoder_input_ids=decoder_input_ids,
+                attention_mask=attention_mask,
+                decoder_attention_mask=decoder_attention_mask,
+            )
+            out_2 = outputs[0].cpu().numpy()
+            out_2[np.isnan(out_2)] = 0
+
+            with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
+                enc_dec_model.encoder.save_pretrained(encoder_tmp_dirname)
+                enc_dec_model.decoder.save_pretrained(decoder_tmp_dirname)
+                EncoderDecoderModel.from_encoder_decoder_pretrained(
+                    encoder_pretrained_model_name_or_path=encoder_tmp_dirname,
+                    decoder_pretrained_model_name_or_path=decoder_tmp_dirname,
+                )
+
+                after_outputs = enc_dec_model(
+                    input_ids=input_ids,
+                    decoder_input_ids=decoder_input_ids,
+                    attention_mask=attention_mask,
+                    decoder_attention_mask=decoder_attention_mask,
+                )
+                out_1 = after_outputs[0].cpu().numpy()
+                out_1[np.isnan(out_1)] = 0
+                max_diff = np.amax(np.abs(out_1 - out_2))
+                self.assertLessEqual(max_diff, 1e-5)
+
+    def check_loss_output(self, loss):
+        self.assertEqual(loss.size(), ())
+
+    def create_and_check_bert_encoder_decoder_model_mlm_labels(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        encoder_hidden_states,
+        decoder_config,
+        decoder_input_ids,
+        decoder_attention_mask,
+        masked_lm_labels,
+        **kwargs
+    ):
+        encoder_model = BertModel(config)
+        decoder_model = BertForMaskedLM(decoder_config)
+        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
+        enc_dec_model.to(torch_device)
+        outputs_encoder_decoder = enc_dec_model(
+            input_ids=input_ids,
+            decoder_input_ids=decoder_input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=decoder_attention_mask,
+            masked_lm_labels=masked_lm_labels,
+        )
+
+        mlm_loss = outputs_encoder_decoder[0]
+        self.check_loss_output(mlm_loss)
+        # check that backprop works
+        mlm_loss.backward()
+
+        self.assertEqual(outputs_encoder_decoder[1].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
+        self.assertEqual(outputs_encoder_decoder[2].shape, (input_ids.shape + (config.hidden_size,)))
+
+    def create_and_check_bert_encoder_decoder_model_lm_labels(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        encoder_hidden_states,
+        decoder_config,
+        decoder_input_ids,
+        decoder_attention_mask,
+        lm_labels,
+        **kwargs
+    ):
+        encoder_model = BertModel(config)
+        decoder_model = BertForMaskedLM(decoder_config)
+        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
+        enc_dec_model.to(torch_device)
+        outputs_encoder_decoder = enc_dec_model(
+            input_ids=input_ids,
+            decoder_input_ids=decoder_input_ids,
+            attention_mask=attention_mask,
+            decoder_attention_mask=decoder_attention_mask,
+            lm_labels=lm_labels,
+        )
+
+        lm_loss = outputs_encoder_decoder[0]
+        self.check_loss_output(lm_loss)
+        # check that backprop works
+        lm_loss.backward()
+
+        self.assertEqual(outputs_encoder_decoder[1].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
+        self.assertEqual(outputs_encoder_decoder[2].shape, (input_ids.shape + (config.hidden_size,)))
+
+    def create_and_check_bert_encoder_decoder_model_generate(self, input_ids, config, decoder_config, **kwargs):
+        encoder_model = BertModel(config)
+        decoder_model = BertForMaskedLM(decoder_config)
+        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
+        enc_dec_model.to(torch_device)
+
+        # Bert does not have a bos token id, so use pad_token_id instead
+        generated_output = enc_dec_model.generate(
+            input_ids, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id
+        )
+        self.assertEqual(generated_output.shape, (input_ids.shape[0],) + (decoder_config.max_length,))
+
+    def test_bert_encoder_decoder_model(self):
+        input_ids_dict = self.prepare_config_and_inputs_bert()
+        self.create_and_check_bert_encoder_decoder_model(**input_ids_dict)
+
+    def test_bert_encoder_decoder_model_from_pretrained(self):
+        input_ids_dict = self.prepare_config_and_inputs_bert()
+        self.create_and_check_bert_encoder_decoder_model_from_pretrained(**input_ids_dict)
+
+    def test_save_and_load_from_pretrained(self):
+        input_ids_dict = self.prepare_config_and_inputs_bert()
+        self.create_and_check_save_and_load(**input_ids_dict)
+
+    def test_save_and_load_from_encoder_decoder_pretrained(self):
+        input_ids_dict = self.prepare_config_and_inputs_bert()
+        self.create_and_check_save_and_load_encoder_decoder_model(**input_ids_dict)
+
+    def test_bert_encoder_decoder_model_mlm_labels(self):
+        input_ids_dict = self.prepare_config_and_inputs_bert()
+        self.create_and_check_bert_encoder_decoder_model_mlm_labels(**input_ids_dict)
+
+    def test_bert_encoder_decoder_model_lm_labels(self):
+        input_ids_dict = self.prepare_config_and_inputs_bert()
+        self.create_and_check_bert_encoder_decoder_model_lm_labels(**input_ids_dict)
+
+    def test_bert_encoder_decoder_model_generate(self):
+        input_ids_dict = self.prepare_config_and_inputs_bert()
+        self.create_and_check_bert_encoder_decoder_model_generate(**input_ids_dict)
+
+    @slow
+    def test_real_bert_model_from_pretrained(self):
+        model = EncoderDecoderModel.from_pretrained("bert-base-uncased", "bert-base-uncased")
+        self.assertIsNotNone(model)