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

src/transformers/configuration_encoder_decoder.py

@@ -0,0 +1,84 @@
+# 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