Fix TF s2s models (#9478)

* Fix Seq2Seq models for serving

* Apply style

* Fix lonfgormer

* Fix mBart/Pegasus/Blenderbot

* Apply style

* Add a main intermediate layer

* Apply style

* Remove import

* Apply tf.function to Longformer

* Fix utils check_copy

* Update S2S template

* Fix BART + Blenderbot

* Fix BlenderbotSmall

* Fix BlenderbotSmall

* Fix BlenderbotSmall

* Fix MBart

* Fix Marian

* Fix Pegasus + template

* Apply style

* Fix common attributes test

* Forgot to fix the LED test

* Apply Patrick's comment on LED Decoder

src/transformers/modeling_tf_utils.py

@@ -322,6 +322,7 @@ def input_processing(func, config, input_ids, **kwargs):
     """
     signature = dict(inspect.signature(func).parameters)
     signature.pop("kwargs", None)
+    signature.pop("self", None)
     parameter_names = list(signature.keys())
     output = {}
     allowed_types = (tf.Tensor, bool, int, ModelOutput, tuple, list, dict, np.ndarray)
@@ -346,6 +347,8 @@ def input_processing(func, config, input_ids, **kwargs):
             f"The following keyword arguments are not supported by this model: {list(kwargs['kwargs_call'].keys())}."
         )
 
+    kwargs.pop("kwargs_call")
+
     for k, v in kwargs.items():
         if isinstance(v, allowed_types) or v is None:
             output[k] = v
@@ -356,8 +359,8 @@ def input_processing(func, config, input_ids, **kwargs):
         for i, input in enumerate(input_ids):
             # EagerTensors don't allow to use the .name property so we check for a real Tensor
             if type(input) == tf.Tensor:
-                # Tensor names have always the pattern name:device_id then we check only the
-                # name and not the device id
+                # Tensor names have always the pattern `name:id` then we check only the
+                # `name` part
                 tensor_name = input.name.split(":")[0]
 
                 if tensor_name in parameter_names:

src/transformers/models/bart/modeling_tf_bart.py

@@ -411,29 +411,6 @@ class TFBartPretrainedModel(TFPreTrainedModel):
         }
         return dummy_inputs
 
-    def get_input_embeddings(self):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        return base_model.shared
-
-    def set_input_embeddings(self, value):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        try:
-            base_model.shared.weight = value
-        except AttributeError:
-            self(self.dummy_inputs)
-            base_model.shared.weight = value
-
-        base_model.shared.vocab_size = shape_list(base_model.shared.weight)[0]
-
-        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
-            pass
-
-        embed_tokens = TFWrappedEmbeddings(base_model.shared, abs_scope_name=shared_abs_scope_name)
-        base_model.encoder.set_embed_tokens(embed_tokens)
-        base_model.decoder.set_embed_tokens(embed_tokens)
-
     @tf.function(
         input_signature=[
             {
@@ -605,6 +582,9 @@ class TFBartEncoder(tf.keras.layers.Layer):
         self.layers = [TFBartEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
         self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -744,6 +724,9 @@ class TFBartDecoder(tf.keras.layers.Layer):
 
         self.dropout = tf.keras.layers.Dropout(config.dropout)
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -871,13 +854,15 @@ class TFBartDecoder(tf.keras.layers.Layer):
         hidden_states = self.dropout(hidden_states, training=inputs["training"])
 
         # decoder layers
-        all_hidden_states = ()
-        all_self_attns = ()
-        present_key_values = ()
+        all_hidden_states = () if inputs["output_hidden_states"] else None
+        all_self_attns = () if inputs["output_attentions"] else None
+        present_key_values = () if inputs["use_cache"] else None
+
         for idx, decoder_layer in enumerate(self.layers):
             # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
             if inputs["output_hidden_states"]:
                 all_hidden_states += (hidden_states,)
+
             dropout_probability = random.uniform(0, 1)
 
             if inputs["training"] and (dropout_probability < self.layerdrop):
@@ -901,12 +886,12 @@ class TFBartDecoder(tf.keras.layers.Layer):
 
         if inputs["output_hidden_states"]:
             all_hidden_states += (hidden_states,)
-        else:
-            all_hidden_states = None
 
-        all_self_attns = list(all_self_attns) if inputs["output_attentions"] else None
+        if inputs["output_attentions"]:
+            all_self_attns = list(all_self_attns)
 
-        present_key_values = (encoder_hidden_states, present_key_values) if inputs["use_cache"] else None
+        if inputs["use_cache"]:
+            present_key_values = (inputs["encoder_hidden_states"], present_key_values)
 
         if not inputs["return_dict"]:
             return hidden_states, present_key_values, all_hidden_states, all_self_attns
@@ -919,16 +904,14 @@ class TFBartDecoder(tf.keras.layers.Layer):
             )
 
 
-@add_start_docstrings(
-    "The bare BART Model outputting raw hidden-states without any specific head on top.",
-    BART_START_DOCSTRING,
-)
 @keras_serializable
-class TFBartModel(TFBartPretrainedModel):
-    base_model_prefix = "model"
+class TFBartMainLayer(tf.keras.layers.Layer):
+    config_class = BartConfig
 
-    def __init__(self, config: BartConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
+    def __init__(self, config: BartConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
         self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
 
         with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
@@ -942,19 +925,20 @@ class TFBartModel(TFBartPretrainedModel):
         self.encoder = TFBartEncoder(config, embed_tokens, name="encoder")
         self.decoder = TFBartDecoder(config, embed_tokens, name="decoder")
 
-    def get_encoder(self):
-        return self.encoder
+    def get_input_embeddings(self):
+        return self.shared
 
-    def get_decoder(self):
-        return self.decoder
+    def set_input_embeddings(self, new_embeddings):
+        self.shared.weight = new_embeddings
+        self.shared.vocab_size = self.shared.weight.shape[0]
+        # retrieve correct absolute scope for embed token wrapper
+        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
+            pass
+        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
+        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
+        self.encoder.set_embed_tokens(embed_tokens)
+        self.decoder.set_embed_tokens(embed_tokens)
 
-    @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="facebook/bart-large",
-        output_type=TFSeq2SeqModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def call(
         self,
         input_ids=None,
@@ -1053,8 +1037,86 @@ class TFBartModel(TFBartPretrainedModel):
             encoder_attentions=inputs["encoder_outputs"].attentions,
         )
 
+
+@add_start_docstrings(
+    "The bare BART Model outputting raw hidden-states without any specific head on top.",
+    BART_START_DOCSTRING,
+)
+class TFBartModel(TFBartPretrainedModel):
+    def __init__(self, config: BartConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.model = TFBartMainLayer(config, name="model")
+
+    def get_encoder(self):
+        return self.model.encoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
+    @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="facebook/bart-large",
+        output_type=TFSeq2SeqModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
+        past_key_values=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        training=False,
+        **kwargs
+    ):
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_outputs,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        outputs = self.model(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            decoder_input_ids=inputs["decoder_input_ids"],
+            decoder_attention_mask=inputs["decoder_attention_mask"],
+            encoder_outputs=inputs["encoder_outputs"],
+            past_key_values=inputs["past_key_values"],
+            inputs_embeds=inputs["inputs_embeds"],
+            decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
+            use_cache=inputs["use_cache"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -1083,7 +1145,7 @@ class TFBartForConditionalGeneration(TFBartPretrainedModel):
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
-        self.model = TFBartModel(config, name="model")
+        self.model = TFBartMainLayer(config, name="model")
         self.use_cache = config.use_cache
         # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
         self.final_logits_bias = self.add_weight(
@@ -1199,7 +1261,7 @@ class TFBartForConditionalGeneration(TFBartPretrainedModel):
         )
 
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None

src/transformers/models/blenderbot/modeling_tf_blenderbot.py

@@ -24,6 +24,7 @@ import tensorflow as tf
 
 from ...activations_tf import get_tf_activation
 from ...file_utils import (
+    add_code_sample_docstrings,
     add_end_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -47,7 +48,6 @@ from ...modeling_tf_utils import (
     shape_list,
 )
 from ...utils import logging
-from ..blenderbot_small import TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
 from .configuration_blenderbot import BlenderbotConfig
 
 
@@ -416,31 +416,6 @@ class TFBlenderbotPreTrainedModel(TFPreTrainedModel):
         }
         return dummy_inputs
 
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.get_input_embeddings
-    def get_input_embeddings(self):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        return base_model.shared
-
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.set_input_embeddings
-    def set_input_embeddings(self, value):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        try:
-            base_model.shared.weight = value
-        except AttributeError:
-            self(self.dummy_inputs)
-            base_model.shared.weight = value
-
-        base_model.shared.vocab_size = shape_list(base_model.shared.weight)[0]
-
-        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
-            pass
-
-        embed_tokens = TFWrappedEmbeddings(base_model.shared, abs_scope_name=shared_abs_scope_name)
-        base_model.encoder.set_embed_tokens(embed_tokens)
-        base_model.decoder.set_embed_tokens(embed_tokens)
-
     @tf.function(
         input_signature=[
             {
@@ -604,6 +579,9 @@ class TFBlenderbotEncoder(tf.keras.layers.Layer):
         self.layers = [TFBlenderbotEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
         self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -744,6 +722,9 @@ class TFBlenderbotDecoder(tf.keras.layers.Layer):
 
         self.dropout = tf.keras.layers.Dropout(config.dropout)
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -921,16 +902,14 @@ class TFBlenderbotDecoder(tf.keras.layers.Layer):
             )
 
 
-@add_start_docstrings(
-    "The bare BLENDERBOT Model outputting raw hidden-states without any specific head on top.",
-    BLENDERBOT_START_DOCSTRING,
-)
 @keras_serializable
-class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
-    base_model_prefix = "model"
+class TFBlenderbotMainLayer(tf.keras.layers.Layer):
+    config_class = BlenderbotConfig
 
-    def __init__(self, config: BlenderbotConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
+    def __init__(self, config: BlenderbotConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
         self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
 
         with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
@@ -944,22 +923,20 @@ class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
         self.encoder = TFBlenderbotEncoder(config, embed_tokens, name="encoder")
         self.decoder = TFBlenderbotDecoder(config, embed_tokens, name="decoder")
 
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
-        if pretrained_model_name_or_path == "facebook/blenderbot-90M":
-            warnings.warn(
-                "The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical checkpoint `facebook/small_blenderbot-90M` with `TFBlenderbotSmallModel.from_pretrained('facebook/small_blenderbot-90M')` instead.",
-                FutureWarning,
-            )
-            return TFBlenderbotSmallModel.from_pretrained(pretrained_model_name_or_path)
-
-        return super(TFBlenderbotModel, cls).from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+    def get_input_embeddings(self):
+        return self.shared
 
-    def get_decoder(self):
-        return self.decoder
+    def set_input_embeddings(self, new_embeddings):
+        self.shared.weight = new_embeddings
+        self.shared.vocab_size = self.shared.weight.shape[0]
+        # retrieve correct absolute scope for embed token wrapper
+        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
+            pass
+        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
+        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
+        self.encoder.set_embed_tokens(embed_tokens)
+        self.decoder.set_embed_tokens(embed_tokens)
 
-    @add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
         input_ids=None,
@@ -977,22 +954,6 @@ class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
         training=False,
         **kwargs
     ):
-        r"""
-        Returns:
-
-        Example::
-
-            >>> from transformers import BlenderbotTokenizer, TFBlenderbotModel
-
-            >>> model = TFBlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill")
-            >>> tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
-
-            >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="tf").input_ids  # Batch size 1
-            >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids  # Batch size 1
-            >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
-
-            >>> last_hidden_states = outputs.last_hidden_state
-        """
         inputs = input_processing(
             func=self.call,
             config=self.config,
@@ -1066,9 +1027,100 @@ class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
             encoder_attentions=inputs["encoder_outputs"].attentions,
         )
 
+
+@add_start_docstrings(
+    "The bare BLENDERBOT Model outputting raw hidden-states without any specific head on top.",
+    BLENDERBOT_START_DOCSTRING,
+)
+class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
+    def __init__(self, config: BlenderbotConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.model = TFBlenderbotMainLayer(config, name="model")
+
+    def get_encoder(self):
+        return self.model.encoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
+        if pretrained_model_name_or_path == "facebook/blenderbot-90M":
+            from ..blenderbot_small import TFBlenderbotSmallModel
+
+            warnings.warn(
+                "The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical checkpoint `facebook/small_blenderbot-90M` with `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')` instead.",
+                FutureWarning,
+            )
+            return TFBlenderbotSmallModel.from_pretrained(pretrained_model_name_or_path)
+
+        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+
+    @add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="facebook/blenderbot-400M-distill",
+        output_type=TFSeq2SeqModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
+        past_key_values=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        training=False,
+        **kwargs
+    ):
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_outputs,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        outputs = self.model(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            decoder_input_ids=inputs["decoder_input_ids"],
+            decoder_attention_mask=inputs["decoder_attention_mask"],
+            encoder_outputs=inputs["encoder_outputs"],
+            past_key_values=inputs["past_key_values"],
+            inputs_embeds=inputs["inputs_embeds"],
+            decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
+            use_cache=inputs["use_cache"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -1097,25 +1149,43 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel):
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
-        self.model = TFBlenderbotModel(config, name="model")
+        self.model = TFBlenderbotMainLayer(config, name="model")
         self.use_cache = config.use_cache
         # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
         self.final_logits_bias = self.add_weight(
             name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
         )
 
+    def get_decoder(self):
+        return self.model.decoder
+
+    def get_encoder(self):
+        return self.model.encoder
+
+    def get_output_embeddings(self):
+        return self.get_input_embeddings()
+
+    def set_output_embeddings(self, value):
+        self.set_input_embeddings(value)
+
+    def get_bias(self):
+        return {"final_logits_bias": self.final_logits_bias}
+
+    def set_bias(self, value):
+        self.final_logits_bias = value["final_logits_bias"]
+
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
         if pretrained_model_name_or_path == "facebook/blenderbot-90M":
+            from ..blenderbot_small import TFBlenderbotSmallForConditionalGeneration
+
             warnings.warn(
                 "The checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical checkpoint `facebook/small_blenderbot-90M` with `TFBlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')` instead.",
                 FutureWarning,
             )
             return TFBlenderbotSmallForConditionalGeneration.from_pretrained(pretrained_model_name_or_path)
 
-        return super(TFBlenderbotForConditionalGeneration, cls).from_pretrained(
-            pretrained_model_name_or_path, *model_args, **kwargs
-        )
+        return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
 
     @add_start_docstrings_to_model_forward(BLENDERBOT_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
@@ -1208,7 +1278,7 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel):
 
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -1283,21 +1353,6 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel):
         else:
             return logits
 
-    def get_encoder(self):
-        return self.model.encoder
-
-    def get_output_embeddings(self):
-        return self.get_input_embeddings()
-
-    def set_output_embeddings(self, value):
-        self.set_input_embeddings(value)
-
-    def get_bias(self):
-        return {"final_logits_bias": self.final_logits_bias}
-
-    def set_bias(self, value):
-        self.final_logits_bias = value["final_logits_bias"]
-
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.compute_loss
     def compute_loss(self, labels, logits):
         """CrossEntropyLoss that ignores pad tokens"""

src/transformers/models/blenderbot_small/__init__.py

@@ -52,7 +52,7 @@ if TYPE_CHECKING:
         )
 
     if is_tf_available():
-        from .modeling_tf_blenderbot import TFBlenderbotForConditionalGeneration, TFBlenderbotModel
+        from .modeling_tf_blenderbot_small import TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
 
 else:
     import importlib

src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py

@@ -22,6 +22,7 @@ import tensorflow as tf
 
 from ...activations_tf import get_tf_activation
 from ...file_utils import (
+    add_code_sample_docstrings,
     add_end_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -414,31 +415,6 @@ class TFBlenderbotSmallPreTrainedModel(TFPreTrainedModel):
         }
         return dummy_inputs
 
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.get_input_embeddings
-    def get_input_embeddings(self):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        return base_model.shared
-
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.set_input_embeddings
-    def set_input_embeddings(self, value):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        try:
-            base_model.shared.weight = value
-        except AttributeError:
-            self(self.dummy_inputs)
-            base_model.shared.weight = value
-
-        base_model.shared.vocab_size = shape_list(base_model.shared.weight)[0]
-
-        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
-            pass
-
-        embed_tokens = TFWrappedEmbeddings(base_model.shared, abs_scope_name=shared_abs_scope_name)
-        base_model.encoder.set_embed_tokens(embed_tokens)
-        base_model.decoder.set_embed_tokens(embed_tokens)
-
     @tf.function(
         input_signature=[
             {
@@ -608,6 +584,9 @@ class TFBlenderbotSmallEncoder(tf.keras.layers.Layer):
         self.layers = [TFBlenderbotSmallEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
         self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -748,6 +727,9 @@ class TFBlenderbotSmallDecoder(tf.keras.layers.Layer):
 
         self.dropout = tf.keras.layers.Dropout(config.dropout)
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -922,16 +904,14 @@ class TFBlenderbotSmallDecoder(tf.keras.layers.Layer):
             )
 
 
-@add_start_docstrings(
-    "The bare BLENDERBOT_SMALL Model outputting raw hidden-states without any specific head on top.",
-    BLENDERBOT_SMALL_START_DOCSTRING,
-)
 @keras_serializable
-class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
-    base_model_prefix = "model"
+class TFBlenderbotSmallMainLayer(tf.keras.layers.Layer):
+    config_class = BlenderbotSmallConfig
 
-    def __init__(self, config: BlenderbotSmallConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
+    def __init__(self, config: BlenderbotSmallConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
         self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
 
         with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
@@ -945,14 +925,20 @@ class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
         self.encoder = TFBlenderbotSmallEncoder(config, embed_tokens, name="encoder")
         self.decoder = TFBlenderbotSmallDecoder(config, embed_tokens, name="decoder")
 
-    def get_encoder(self):
-        return self.encoder
+    def get_input_embeddings(self):
+        return self.shared
 
-    def get_decoder(self):
-        return self.decoder
+    def set_input_embeddings(self, new_embeddings):
+        self.shared.weight = new_embeddings
+        self.shared.vocab_size = self.shared.weight.shape[0]
+        # retrieve correct absolute scope for embed token wrapper
+        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
+            pass
+        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
+        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
+        self.encoder.set_embed_tokens(embed_tokens)
+        self.decoder.set_embed_tokens(embed_tokens)
 
-    @add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
         input_ids=None,
@@ -970,22 +956,6 @@ class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
         training=False,
         **kwargs
     ):
-        r"""
-        Returns:
-
-        Example::
-
-            >>> from transformers import BlenderbotSmallTokenizer, TFBlenderbotSmallModel
-
-            >>> model = TFBlenderbotSmallModel.from_pretrained("facebook/blenderbot_small-90M")
-            >>> tokenizer = BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot_small-90M")
-
-            >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="tf").input_ids  # Batch size 1
-            >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids  # Batch size 1
-            >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
-
-            >>> last_hidden_states = outputs.last_hidden_state
-        """
         inputs = input_processing(
             func=self.call,
             config=self.config,
@@ -1059,9 +1029,87 @@ class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
             encoder_attentions=inputs["encoder_outputs"].attentions,
         )
 
+
+@add_start_docstrings(
+    "The bare BLENDERBOT_SMALL Model outputting raw hidden-states without any specific head on top.",
+    BLENDERBOT_SMALL_START_DOCSTRING,
+)
+class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
+    def __init__(self, config: BlenderbotSmallConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.model = TFBlenderbotSmallMainLayer(config, name="model")
+
+    def get_encoder(self):
+        return self.model.encoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
+    @add_start_docstrings_to_model_forward(BLENDERBOT_SMALL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="facebook/blenderbot_small-90M",
+        output_type=TFSeq2SeqModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
+        past_key_values=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        training=False,
+        **kwargs
+    ):
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_outputs,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        outputs = self.model(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            decoder_input_ids=inputs["decoder_input_ids"],
+            decoder_attention_mask=inputs["decoder_attention_mask"],
+            encoder_outputs=inputs["encoder_outputs"],
+            past_key_values=inputs["past_key_values"],
+            inputs_embeds=inputs["inputs_embeds"],
+            decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
+            use_cache=inputs["use_cache"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -1090,7 +1138,7 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
-        self.model = TFBlenderbotSmallModel(config, name="model")
+        self.model = TFBlenderbotSmallMainLayer(config, name="model")
         self.use_cache = config.use_cache
         # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
         self.final_logits_bias = self.add_weight(
@@ -1206,7 +1254,7 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel
 
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None

src/transformers/models/led/modeling_tf_led.py

@@ -320,6 +320,8 @@ class TFLEDEncoderSelfAttention(tf.keras.layers.Layer):
         )
 
         # make sure that local attention probabilities are set to 0 for indices of global attn
+        # When is_global_attn is True, the last dimension is always self.one_sided_attn_window_size * 2 + 1 + 1
+        # because of the concat Line 713.
         attn_probs = tf.where(
             tf.broadcast_to(is_index_global_attn[:, :, None, None], shape_list(attn_probs)),
             tf.zeros(shape_list(attn_probs), dtype=tf.dtypes.float32),
@@ -882,6 +884,7 @@ class TFLEDEncoderAttention(tf.keras.layers.Layer):
             [hidden_states, attention_mask, is_index_masked, is_index_global_attn, is_global_attn],
             training=training,
         )
+
         attention_output = self.output_dense(self_outputs[0], training=training)
         outputs = (attention_output,) + self_outputs[1:]
 
@@ -1046,15 +1049,16 @@ class TFLEDEncoderLayer(tf.keras.layers.Layer):
         )
 
         hidden_states = layer_outputs[0]
+
         tf.debugging.assert_equal(
             shape_list(hidden_states),
             shape_list(residual),
             message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}",
         )
+
         hidden_states = self.dropout(hidden_states, training=training)
         hidden_states = residual + hidden_states
         hidden_states = self.self_attn_layer_norm(hidden_states)
-
         residual = hidden_states
         hidden_states = self.activation_fn(self.fc1(hidden_states))
         hidden_states = self.activation_dropout(hidden_states, training=training)
@@ -1182,29 +1186,6 @@ class TFLEDPreTrainedModel(TFPreTrainedModel):
         }
         return dummy_inputs
 
-    def get_input_embeddings(self):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        return base_model.shared
-
-    def set_input_embeddings(self, value):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        try:
-            base_model.shared.weight = value
-        except AttributeError:
-            self(self.dummy_inputs)
-            base_model.shared.weight = value
-
-        base_model.shared.vocab_size = shape_list(base_model.shared.weight)[0]
-
-        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
-            pass
-
-        embed_tokens = TFWrappedEmbeddings(base_model.shared, abs_scope_name=shared_abs_scope_name)
-        base_model.encoder.set_embed_tokens(embed_tokens)
-        base_model.decoder.set_embed_tokens(embed_tokens)
-
     @tf.function(
         input_signature=[
             {
@@ -1521,6 +1502,9 @@ class TFLEDEncoder(tf.keras.layers.Layer):
         self.layers = [TFLEDEncoderLayer(config, i, name=f"layers.{i}") for i in range(config.encoder_layers)]
         self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -1624,20 +1608,17 @@ class TFLEDEncoder(tf.keras.layers.Layer):
         # check attention mask and invert
         if inputs["attention_mask"] is not None:
             # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            attention_mask = _expand_mask(inputs["attention_mask"])[:, 0, 0, :]
-            attention_mask = attention_mask[:, :, None, None]
-        else:
-            attention_mask = None
+            inputs["attention_mask"] = _expand_mask(inputs["attention_mask"])[:, 0, 0, :]
+            inputs["attention_mask"] = inputs["attention_mask"][:, :, None, None]
 
         encoder_states = () if inputs["output_hidden_states"] else None
-        all_attentions = () if inputs["output_attentions"] else None
-        all_global_attentions = () if inputs["output_attentions"] and is_global_attn else None
+        all_attentions = all_global_attentions = () if inputs["output_attentions"] else None
 
         # encoder layers
         for encoder_layer in self.layers:
 
             if inputs["output_hidden_states"]:
-                hidden_states_to_add = hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
+                hidden_states_to_add = self.compute_hidden_states(hidden_states, padding_len)
                 encoder_states = encoder_states + (hidden_states_to_add,)
             # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
             dropout_probability = random.uniform(0, 1)
@@ -1646,7 +1627,7 @@ class TFLEDEncoder(tf.keras.layers.Layer):
 
             layer_outputs = encoder_layer(
                 hidden_states=hidden_states,
-                attention_mask=attention_mask,
+                attention_mask=inputs["attention_mask"],
                 is_index_masked=is_index_masked,
                 is_index_global_attn=is_index_global_attn,
                 is_global_attn=is_global_attn,
@@ -1658,14 +1639,12 @@ class TFLEDEncoder(tf.keras.layers.Layer):
                 # bzs x seq_len x num_attn_heads x (num_global_attn + attention_window_len + 1) => bzs x num_attn_heads x seq_len x (num_global_attn + attention_window_len + 1)
                 all_attentions = all_attentions + (tf.transpose(layer_outputs[1], (0, 2, 1, 3)),)
 
-                if is_global_attn:
                 # bzs x num_attn_heads x num_global_attn x seq_len => bzs x num_attn_heads x seq_len x num_global_attn
                 all_global_attentions = all_global_attentions + (tf.transpose(layer_outputs[2], (0, 1, 3, 2)),)
 
         # undo padding
-        if padding_len > 0:
         # unpad `hidden_states` because the calling function is expecting a length == input_ids.size(1)
-            hidden_states = hidden_states[:, :-padding_len]
+        hidden_states = self.compute_hidden_states(hidden_states, padding_len)
 
         if inputs["output_hidden_states"]:
             encoder_states = encoder_states + (hidden_states,)
@@ -1679,6 +1658,11 @@ class TFLEDEncoder(tf.keras.layers.Layer):
             global_attentions=all_global_attentions,
         )
 
+    @tf.function
+    def compute_hidden_states(self, hidden_states, padding_len):
+        return hidden_states[:, :-padding_len] if padding_len > 0 else hidden_states
+
+    @tf.function
     def _pad_to_window_size(
         self,
         input_ids,
@@ -1777,19 +1761,14 @@ class TFLEDDecoder(tf.keras.layers.Layer):
         Args:
             input_ids (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`):
                 Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
-                provide it.
-
-                Indices can be obtained using :class:`~transformers.LEDTokenizer`. See
+                provide it. Indices can be obtained using :class:`~transformers.LEDTokenizer`. See
                 :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__`
-                for details.
-
-                `What are input IDs? <../glossary.html#input-ids>`__
+                for details. `What are input IDs? <../glossary.html#input-ids>`__
             attention_mask (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
                 Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
 
                 - 1 for tokens that are **not masked**,
                 - 0 for tokens that are **masked**.
-
                 `What are attention masks? <../glossary.html#attention-mask>`__
             encoder_hidden_states (:obj:`tf.Tensor` of shape :obj:`(batch_size, encoder_sequence_length, hidden_size)`, `optional`):
                 Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
@@ -1800,13 +1779,10 @@ class TFLEDDecoder(tf.keras.layers.Layer):
 
                 - 1 for tokens that are **not masked**,
                 - 0 for tokens that are **masked**.
-
                 `What are attention masks? <../glossary.html#attention-mask>`__
             past_key_values (:obj:`Tuple[Tuple[tf.Tensor]]` of length :obj:`config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
                 Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up
-                decoding.
-
-                If :obj:`past_key_values` are used, the user can optionally input only the last
+                decoding. If :obj:`past_key_values` are used, the user can optionally input only the last
                 :obj:`decoder_input_ids` (those that don't have their past key value states given to this model) of
                 shape :obj:`(batch_size, 1)` instead of all :obj:`decoder_input_ids`` of shape :obj:`(batch_size,
                 sequence_length)`.
@@ -1930,16 +1906,13 @@ class TFLEDDecoder(tf.keras.layers.Layer):
             )
 
 
-@add_start_docstrings(
-    "The bare LED Model outputting raw hidden-states without any specific head on top.",
-    LED_START_DOCSTRING,
-)
 @keras_serializable
-class TFLEDModel(TFLEDPreTrainedModel):
-    base_model_prefix = "led"
+class TFLEDMainLayer(tf.keras.layers.Layer):
+    config_class = LEDConfig
 
-    def __init__(self, config: LEDConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
+    def __init__(self, config: LEDConfig, **kwargs):
+        super().__init__(**kwargs)
+        self.config = config
         self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="led.shared")
 
         with tf.compat.v1.variable_scope("led.shared") as shared_abs_scope_name:
@@ -1953,19 +1926,20 @@ class TFLEDModel(TFLEDPreTrainedModel):
         self.encoder = TFLEDEncoder(config, embed_tokens, name="encoder")
         self.decoder = TFLEDDecoder(config, embed_tokens, name="decoder")
 
-    def get_encoder(self):
-        return self.encoder
+    def get_input_embeddings(self):
+        return self.shared
 
-    def get_decoder(self):
-        return self.decoder
+    def set_input_embeddings(self, new_embeddings):
+        self.shared.weight = new_embeddings
+        self.shared.vocab_size = self.shared.weight.shape[0]
+        # retrieve correct absolute scope for embed token wrapper
+        with tf.compat.v1.variable_scope("led.shared") as shared_abs_scope_name:
+            pass
+        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
+        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
+        self.encoder.set_embed_tokens(embed_tokens)
+        self.decoder.set_embed_tokens(embed_tokens)
 
-    @add_start_docstrings_to_model_forward(LED_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="allenai/led-base-16384",
-        output_type=TFLEDSeq2SeqModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def call(
         self,
         input_ids=None,
@@ -2007,12 +1981,6 @@ class TFLEDModel(TFLEDPreTrainedModel):
         if inputs["decoder_input_ids"] is None and inputs["decoder_inputs_embeds"] is None:
             inputs["use_cache"] = False
 
-        inputs["output_hidden_states"] = (
-            inputs["output_hidden_states"]
-            if inputs["output_hidden_states"] is not None
-            else self.config.output_hidden_states
-        )
-
         if inputs["encoder_outputs"] is None:
             inputs["encoder_outputs"] = self.encoder(
                 input_ids=inputs["input_ids"],
@@ -2063,8 +2031,88 @@ class TFLEDModel(TFLEDPreTrainedModel):
             encoder_global_attentions=inputs["encoder_outputs"].global_attentions,
         )
 
+
+@add_start_docstrings(
+    "The bare LED Model outputting raw hidden-states without any specific head on top.",
+    LED_START_DOCSTRING,
+)
+class TFLEDModel(TFLEDPreTrainedModel):
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.led = TFLEDMainLayer(config, name="led")
+
+    def get_encoder(self):
+        return self.led.encoder
+
+    def get_decoder(self):
+        return self.led.decoder
+
+    @add_start_docstrings_to_model_forward(LED_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="allenai/led-base-16384",
+        output_type=TFLEDSeq2SeqModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        encoder_outputs: Optional[Union[Tuple, TFLEDEncoderBaseModelOutput]] = None,
+        global_attention_mask=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        training=False,
+        **kwargs
+    ):
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_outputs,
+            global_attention_mask=global_attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+        outputs = self.led(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            decoder_input_ids=inputs["decoder_input_ids"],
+            decoder_attention_mask=inputs["decoder_attention_mask"],
+            encoder_outputs=inputs["encoder_outputs"],
+            global_attention_mask=inputs["global_attention_mask"],
+            past_key_values=inputs["past_key_values"],
+            inputs_embeds=inputs["inputs_embeds"],
+            decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
+            use_cache=inputs["use_cache"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -2095,7 +2143,7 @@ class TFLEDForConditionalGeneration(TFLEDPreTrainedModel):
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
-        self.led = TFLEDModel(config, name="led")
+        self.led = TFLEDMainLayer(config, name="led")
         self.use_cache = config.use_cache
         # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
         self.final_logits_bias = self.add_weight(
@@ -2157,6 +2205,7 @@ class TFLEDForConditionalGeneration(TFLEDPreTrainedModel):
             >>> probs = tf.nn.softmax(logits[0])
             >>> # probs[5] is associated with the mask token
         """
+
         inputs = input_processing(
             func=self.call,
             config=self.config,
@@ -2221,7 +2270,7 @@ class TFLEDForConditionalGeneration(TFLEDPreTrainedModel):
         )
 
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None

src/transformers/models/longformer/modeling_tf_longformer.py

@@ -974,6 +974,8 @@ class TFLongformerSelfAttention(tf.keras.layers.Layer):
         )
 
         # make sure that local attention probabilities are set to 0 for indices of global attn
+        # When is_global_attn is True, the last dimension is always self.one_sided_attn_window_size * 2 + 1 + 1
+        # because of the concat Line 713.
         attn_probs = tf.where(
             tf.broadcast_to(is_index_global_attn[:, :, None, None], shape_list(attn_probs)),
             tf.zeros(shape_list(attn_probs), dtype=tf.dtypes.float32),

src/transformers/models/marian/modeling_tf_marian.py

@@ -23,6 +23,7 @@ import tensorflow as tf
 
 from ...activations_tf import get_tf_activation
 from ...file_utils import (
+    add_code_sample_docstrings,
     add_end_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -444,31 +445,6 @@ class TFMarianPreTrainedModel(TFPreTrainedModel):
         }
         return dummy_inputs
 
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.get_input_embeddings
-    def get_input_embeddings(self):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        return base_model.shared
-
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.set_input_embeddings
-    def set_input_embeddings(self, value):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        try:
-            base_model.shared.weight = value
-        except AttributeError:
-            self(self.dummy_inputs)
-            base_model.shared.weight = value
-
-        base_model.shared.vocab_size = shape_list(base_model.shared.weight)[0]
-
-        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
-            pass
-
-        embed_tokens = TFWrappedEmbeddings(base_model.shared, abs_scope_name=shared_abs_scope_name)
-        base_model.encoder.set_embed_tokens(embed_tokens)
-        base_model.decoder.set_embed_tokens(embed_tokens)
-
     @tf.function(
         input_signature=[
             {
@@ -625,6 +601,9 @@ class TFMarianEncoder(tf.keras.layers.Layer):
         )
         self.layers = [TFMarianEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -761,6 +740,9 @@ class TFMarianDecoder(tf.keras.layers.Layer):
 
         self.dropout = tf.keras.layers.Dropout(config.dropout)
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -935,16 +917,14 @@ class TFMarianDecoder(tf.keras.layers.Layer):
             )
 
 
-@add_start_docstrings(
-    "The bare MARIAN Model outputting raw hidden-states without any specific head on top.",
-    MARIAN_START_DOCSTRING,
-)
 @keras_serializable
-class TFMarianModel(TFMarianPreTrainedModel):
-    base_model_prefix = "model"
+class TFMarianMainLayer(tf.keras.layers.Layer):
+    config_class = MarianConfig
 
-    def __init__(self, config: MarianConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
+    def __init__(self, config: MarianConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
         self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
 
         with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
@@ -958,14 +938,20 @@ class TFMarianModel(TFMarianPreTrainedModel):
         self.encoder = TFMarianEncoder(config, embed_tokens, name="encoder")
         self.decoder = TFMarianDecoder(config, embed_tokens, name="decoder")
 
-    def get_encoder(self):
-        return self.encoder
+    def get_input_embeddings(self):
+        return self.shared
 
-    def get_decoder(self):
-        return self.decoder
+    def set_input_embeddings(self, new_embeddings):
+        self.shared.weight = new_embeddings
+        self.shared.vocab_size = self.shared.weight.shape[0]
+        # retrieve correct absolute scope for embed token wrapper
+        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
+            pass
+        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
+        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
+        self.encoder.set_embed_tokens(embed_tokens)
+        self.decoder.set_embed_tokens(embed_tokens)
 
-    @add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
         input_ids=None,
@@ -983,24 +969,6 @@ class TFMarianModel(TFMarianPreTrainedModel):
         training=False,
         **kwargs
     ):
-        r"""
-        Returns:
-
-        Example::
-
-            >>> from transformers import MarianTokenizer, TFMarianModel
-
-            >>> tokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-de')
-            >>> model = TFMarianModel.from_pretrained('Helsinki-NLP/opus-mt-en-de')
-
-            >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="tf").input_ids  # Batch size 1
-            >>> decoder_input_ids = tokenizer("<pad> Studien haben gezeigt dass es hilfreich ist einen Hund zu besitzen",
-            ... return_tensors="tf", add_special_tokens=False).input_ids  # Batch size 1
-            >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
-
-            >>> last_hidden_states = outputs.last_hidden_state
-        """
-
         inputs = input_processing(
             func=self.call,
             config=self.config,
@@ -1077,9 +1045,87 @@ class TFMarianModel(TFMarianPreTrainedModel):
             encoder_attentions=inputs["encoder_outputs"].attentions,
         )
 
+
+@add_start_docstrings(
+    "The bare MARIAN Model outputting raw hidden-states without any specific head on top.",
+    MARIAN_START_DOCSTRING,
+)
+class TFMarianModel(TFMarianPreTrainedModel):
+    def __init__(self, config: MarianConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.model = TFMarianMainLayer(config, name="model")
+
+    def get_encoder(self):
+        return self.model.encoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
+    @add_start_docstrings_to_model_forward(MARIAN_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="Helsinki-NLP/opus-mt-en-de",
+        output_type=TFSeq2SeqModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
+        past_key_values=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        training=False,
+        **kwargs
+    ):
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_outputs,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        outputs = self.model(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            decoder_input_ids=inputs["decoder_input_ids"],
+            decoder_attention_mask=inputs["decoder_attention_mask"],
+            encoder_outputs=inputs["encoder_outputs"],
+            past_key_values=inputs["past_key_values"],
+            inputs_embeds=inputs["inputs_embeds"],
+            decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
+            use_cache=inputs["use_cache"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -1108,7 +1154,7 @@ class TFMarianMTModel(TFMarianPreTrainedModel):
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
-        self.model = TFMarianModel(config, name="model")
+        self.model = TFMarianMainLayer(config, name="model")
         self.use_cache = config.use_cache
         # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
         self.final_logits_bias = self.add_weight(
@@ -1225,7 +1271,7 @@ class TFMarianMTModel(TFMarianPreTrainedModel):
 
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None

src/transformers/models/mbart/modeling_tf_mbart.py

@@ -417,31 +417,6 @@ class TFMBartPreTrainedModel(TFPreTrainedModel):
         }
         return dummy_inputs
 
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.get_input_embeddings
-    def get_input_embeddings(self):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        return base_model.shared
-
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.set_input_embeddings
-    def set_input_embeddings(self, value):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        try:
-            base_model.shared.weight = value
-        except AttributeError:
-            self(self.dummy_inputs)
-            base_model.shared.weight = value
-
-        base_model.shared.vocab_size = shape_list(base_model.shared.weight)[0]
-
-        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
-            pass
-
-        embed_tokens = TFWrappedEmbeddings(base_model.shared, abs_scope_name=shared_abs_scope_name)
-        base_model.encoder.set_embed_tokens(embed_tokens)
-        base_model.decoder.set_embed_tokens(embed_tokens)
-
     @tf.function(
         input_signature=[
             {
@@ -615,6 +590,9 @@ class TFMBartEncoder(tf.keras.layers.Layer):
         self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
         self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -757,6 +735,9 @@ class TFMBartDecoder(tf.keras.layers.Layer):
 
         self.dropout = tf.keras.layers.Dropout(config.dropout)
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -934,16 +915,14 @@ class TFMBartDecoder(tf.keras.layers.Layer):
             )
 
 
-@add_start_docstrings(
-    "The bare MBART Model outputting raw hidden-states without any specific head on top.",
-    MBART_START_DOCSTRING,
-)
 @keras_serializable
-class TFMBartModel(TFMBartPreTrainedModel):
-    base_model_prefix = "model"
+class TFMBartMainLayer(tf.keras.layers.Layer):
+    config_class = MBartConfig
 
-    def __init__(self, config: MBartConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
+    def __init__(self, config: MBartConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
         self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
 
         with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
@@ -957,19 +936,20 @@ class TFMBartModel(TFMBartPreTrainedModel):
         self.encoder = TFMBartEncoder(config, embed_tokens, name="encoder")
         self.decoder = TFMBartDecoder(config, embed_tokens, name="decoder")
 
-    def get_encoder(self):
-        return self.encoder
+    def get_input_embeddings(self):
+        return self.shared
 
-    def get_decoder(self):
-        return self.decoder
+    def set_input_embeddings(self, new_embeddings):
+        self.shared.weight = new_embeddings
+        self.shared.vocab_size = self.shared.weight.shape[0]
+        # retrieve correct absolute scope for embed token wrapper
+        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
+            pass
+        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
+        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
+        self.encoder.set_embed_tokens(embed_tokens)
+        self.decoder.set_embed_tokens(embed_tokens)
 
-    @add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="facebook/mbart-large-cc25",
-        output_type=TFSeq2SeqModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def call(
         self,
         input_ids=None,
@@ -1066,9 +1046,87 @@ class TFMBartModel(TFMBartPreTrainedModel):
             encoder_attentions=inputs["encoder_outputs"].attentions,
         )
 
+
+@add_start_docstrings(
+    "The bare MBART Model outputting raw hidden-states without any specific head on top.",
+    MBART_START_DOCSTRING,
+)
+class TFMBartModel(TFMBartPreTrainedModel):
+    def __init__(self, config: MBartConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.model = TFMBartMainLayer(config, name="model")
+
+    def get_encoder(self):
+        return self.model.encoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
+    @add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="facebook/mbart-large-cc25",
+        output_type=TFSeq2SeqModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
+        past_key_values=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        training=False,
+        **kwargs
+    ):
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_outputs,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        outputs = self.model(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            decoder_input_ids=inputs["decoder_input_ids"],
+            decoder_attention_mask=inputs["decoder_attention_mask"],
+            encoder_outputs=inputs["encoder_outputs"],
+            past_key_values=inputs["past_key_values"],
+            inputs_embeds=inputs["inputs_embeds"],
+            decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
+            use_cache=inputs["use_cache"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -1097,7 +1155,7 @@ class TFMBartForConditionalGeneration(TFMBartPreTrainedModel):
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
-        self.model = TFMBartModel(config, name="model")
+        self.model = TFMBartMainLayer(config, name="model")
         self.use_cache = config.use_cache
         # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
         self.final_logits_bias = self.add_weight(
@@ -1212,7 +1270,7 @@ class TFMBartForConditionalGeneration(TFMBartPreTrainedModel):
 
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None

src/transformers/models/pegasus/modeling_tf_pegasus.py

@@ -23,6 +23,7 @@ import tensorflow as tf
 
 from ...activations_tf import get_tf_activation
 from ...file_utils import (
+    add_code_sample_docstrings,
     add_end_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
@@ -445,31 +446,6 @@ class TFPegasusPreTrainedModel(TFPreTrainedModel):
         }
         return dummy_inputs
 
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.get_input_embeddings
-    def get_input_embeddings(self):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        return base_model.shared
-
-    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.set_input_embeddings
-    def set_input_embeddings(self, value):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        try:
-            base_model.shared.weight = value
-        except AttributeError:
-            self(self.dummy_inputs)
-            base_model.shared.weight = value
-
-        base_model.shared.vocab_size = shape_list(base_model.shared.weight)[0]
-
-        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
-            pass
-
-        embed_tokens = TFWrappedEmbeddings(base_model.shared, abs_scope_name=shared_abs_scope_name)
-        base_model.encoder.set_embed_tokens(embed_tokens)
-        base_model.decoder.set_embed_tokens(embed_tokens)
-
     @tf.function(
         input_signature=[
             {
@@ -631,6 +607,9 @@ class TFPegasusEncoder(tf.keras.layers.Layer):
         self.layers = [TFPegasusEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
         self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -770,6 +749,9 @@ class TFPegasusDecoder(tf.keras.layers.Layer):
 
         self.dropout = tf.keras.layers.Dropout(config.dropout)
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -946,16 +928,14 @@ class TFPegasusDecoder(tf.keras.layers.Layer):
             )
 
 
-@add_start_docstrings(
-    "The bare PEGASUS Model outputting raw hidden-states without any specific head on top.",
-    PEGASUS_START_DOCSTRING,
-)
 @keras_serializable
-class TFPegasusModel(TFPegasusPreTrainedModel):
-    base_model_prefix = "model"
+class TFPegasusMainLayer(tf.keras.layers.Layer):
+    config_class = PegasusConfig
 
-    def __init__(self, config: PegasusConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
+    def __init__(self, config: PegasusConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
         self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
 
         with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
@@ -969,14 +949,20 @@ class TFPegasusModel(TFPegasusPreTrainedModel):
         self.encoder = TFPegasusEncoder(config, embed_tokens, name="encoder")
         self.decoder = TFPegasusDecoder(config, embed_tokens, name="decoder")
 
-    def get_encoder(self):
-        return self.encoder
+    def get_input_embeddings(self):
+        return self.shared
 
-    def get_decoder(self):
-        return self.decoder
+    def set_input_embeddings(self, new_embeddings):
+        self.shared.weight = new_embeddings
+        self.shared.vocab_size = self.shared.weight.shape[0]
+        # retrieve correct absolute scope for embed token wrapper
+        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
+            pass
+        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
+        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
+        self.encoder.set_embed_tokens(embed_tokens)
+        self.decoder.set_embed_tokens(embed_tokens)
 
-    @add_start_docstrings_to_model_forward(PEGASUS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
     def call(
         self,
         input_ids=None,
@@ -994,22 +980,6 @@ class TFPegasusModel(TFPegasusPreTrainedModel):
         training=False,
         **kwargs
     ):
-        r"""
-        Returns:
-
-        Example::
-
-            >>> from transformers import PegasusTokenizer, TFPegasusModel
-
-            >>> tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-large")
-            >>> model = TFPegasusModel.from_pretrained("google/pegasus-large")
-
-            >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="tf").input_ids  # Batch size 1
-            >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="tf").input_ids  # Batch size 1
-            >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
-
-            >>> last_hidden_states = outputs.last_hidden_state
-        """
         inputs = input_processing(
             func=self.call,
             config=self.config,
@@ -1086,9 +1056,87 @@ class TFPegasusModel(TFPegasusPreTrainedModel):
             encoder_attentions=inputs["encoder_outputs"].attentions,
         )
 
+
+@add_start_docstrings(
+    "The bare PEGASUS Model outputting raw hidden-states without any specific head on top.",
+    PEGASUS_START_DOCSTRING,
+)
+class TFPegasusModel(TFPegasusPreTrainedModel):
+    def __init__(self, config: PegasusConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.model = TFPegasusMainLayer(config, name="model")
+
+    def get_encoder(self):
+        return self.model.encoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
+    @add_start_docstrings_to_model_forward(PEGASUS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="google/pegasus-large",
+        output_type=TFSeq2SeqModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
+        past_key_values=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        training=False,
+        **kwargs
+    ):
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_outputs,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        outputs = self.model(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            decoder_input_ids=inputs["decoder_input_ids"],
+            decoder_attention_mask=inputs["decoder_attention_mask"],
+            encoder_outputs=inputs["encoder_outputs"],
+            past_key_values=inputs["past_key_values"],
+            inputs_embeds=inputs["inputs_embeds"],
+            decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
+            use_cache=inputs["use_cache"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -1117,7 +1165,7 @@ class TFPegasusForConditionalGeneration(TFPegasusPreTrainedModel):
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
-        self.model = TFPegasusModel(config, name="model")
+        self.model = TFPegasusMainLayer(config, name="model")
         self.use_cache = config.use_cache
         # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
         self.final_logits_bias = self.add_weight(
@@ -1234,7 +1282,7 @@ class TFPegasusForConditionalGeneration(TFPegasusPreTrainedModel):
 
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
     def serving_output(self, output):
-        pkv = (tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,)
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None

src/transformers/models/t5/modeling_tf_t5.py

@@ -1207,7 +1207,7 @@ class TFT5Model(TFT5PreTrainedModel):
         )
 
     def serving_output(self, output):
-        pkv = (tf.convert_to_tensor(output.past_key_values[1:]) if self.config.use_cache else None,)
+        pkv = tf.convert_to_tensor(output.past_key_values[1:]) if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -1437,7 +1437,7 @@ class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModeling
         )
 
     def serving_output(self, output):
-        pkv = (tf.convert_to_tensor(output.past_key_values[1:]) if self.config.use_cache else None,)
+        pkv = tf.convert_to_tensor(output.past_key_values[1:]) if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -18,6 +18,7 @@
 
 
 import tensorflow as tf
+from tensorflow.keras import layers
 
 from transformers.modeling_tf_outputs import TFCausalLMOutput
 
@@ -1915,29 +1916,6 @@ class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
         }
         return dummy_inputs
     
-    def get_input_embeddings(self):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        return base_model.shared
-
-    def set_input_embeddings(self, value):
-        base_model = getattr(self, self.base_model_prefix, self)
-
-        try:
-            base_model.shared.weight = value
-        except AttributeError:
-            self(self.dummy_inputs)
-            base_model.shared.weight = value
-
-        base_model.shared.vocab_size = shape_list(base_model.shared.weight)[0]
-
-        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
-            pass
-
-        embed_tokens = TFWrappedEmbeddings(base_model.shared, abs_scope_name=shared_abs_scope_name)
-        base_model.encoder.set_embed_tokens(embed_tokens)
-        base_model.decoder.set_embed_tokens(embed_tokens)
-    
     @tf.function(
         input_signature=[
             {
@@ -1948,6 +1926,7 @@ class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel):
             }
         ]
     )
+    # Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.serving
     def serving(self, inputs):
         output = self.call(inputs)
 
@@ -2080,6 +2059,9 @@ class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
         self.layers = [TF{{cookiecutter.camelcase_modelname}}EncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
         self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
     
@@ -2148,7 +2130,7 @@ class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
         if inputs["inputs_embeds"] is None:
-            inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"]) * self.embed_scale
+            inputs_embeds = self.embed_tokens(inputs["input_ids"]) * self.embed_scale
 
         embed_pos = self.embed_positions(input_shape)
         hidden_states = inputs["inputs_embeds"] + embed_pos
@@ -2158,9 +2140,7 @@ class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
         # check attention mask and invert
         if inputs["attention_mask"] is not None:
             # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            attention_mask = _expand_mask(inputs["attention_mask"])
-        else:
-            attention_mask = None
+            inputs["attention_mask"] = _expand_mask(inputs["attention_mask"])
 
         encoder_states = () if inputs["output_hidden_states"] else None
         all_attentions = () if inputs["output_attentions"] else None
@@ -2175,7 +2155,7 @@ class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
             if inputs["training"] and (dropout_probability < self.layerdrop):  # skip the layer
                 continue
 
-            hidden_states, attn = encoder_layer(hidden_states, attention_mask)
+            hidden_states, attn = encoder_layer(hidden_states, inputs["attention_mask"])
 
             if inputs["output_attentions"]:
                 all_attentions += (attn,)
@@ -2219,6 +2199,9 @@ class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
 
         self.dropout = tf.keras.layers.Dropout(config.dropout)
 
+    def get_embed_tokens(self):
+        return self.embed_tokens
+
     def set_embed_tokens(self, embed_tokens):
         self.embed_tokens = embed_tokens
 
@@ -2321,21 +2304,14 @@ class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
         positions = self.embed_positions(input_shape, past_key_values_length)
 
         if inputs["inputs_embeds"] is None:
-            inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"]) * self.embed_scale
+            inputs["inputs_embeds"] = self.embed_tokens(inputs["input_ids"])
 
         hidden_states = inputs["inputs_embeds"]
 
-        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-        if input_shape[-1] > 1:
-            combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
-        else:
-            combined_attention_mask = _expand_mask(
-                tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
+        inputs["attention_mask"], combined_attention_mask = self.compute_combined_attns_mask(
+            inputs, input_shape, past_key_values_length
         )
 
-        if inputs["attention_mask"] is not None and input_shape[-1] > 1:
-            combined_attention_mask = combined_attention_mask + _expand_mask(inputs["attention_mask"], tgt_len=input_shape[-1])
-
         if inputs["encoder_hidden_states"] is not None and inputs["encoder_attention_mask"] is not None:
             # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
             inputs["encoder_attention_mask"] = _expand_mask(inputs["encoder_attention_mask"], tgt_len=input_shape[-1])
@@ -2344,13 +2320,15 @@ class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
         hidden_states = self.dropout(hidden_states, training=inputs["training"])
 
         # decoder layers
-        all_hidden_states = ()
-        all_self_attns = ()
-        present_key_values = ()
+        all_hidden_states = () if inputs["output_hidden_states"] else None
+        all_self_attns = () if inputs["output_attentions"] else None
+        present_key_values = () if inputs["use_cache"] else None
+
         for idx, decoder_layer in enumerate(self.layers):
             # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
             if inputs["output_hidden_states"]:
                 all_hidden_states += (hidden_states,)
+
             dropout_probability = random.uniform(0, 1)
 
             if inputs["training"] and (dropout_probability < self.layerdrop):
@@ -2374,12 +2352,12 @@ class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
 
         if inputs["output_hidden_states"]:
             all_hidden_states += (hidden_states,)
-        else:
-            all_hidden_states = None
         
-        all_self_attns = list(all_self_attns) if inputs["output_attentions"] else None
+        if inputs["output_attentions"]:
+            all_self_attns = list(all_self_attns)
 
-        present_key_values = (encoder_hidden_states, present_key_values) if inputs["use_cache"] else None
+        if inputs["use_cache"]:
+            present_key_values = (inputs["encoder_hidden_states"], present_key_values)
 
         if not inputs["return_dict"]:
             return hidden_states, present_key_values, all_hidden_states, all_self_attns
@@ -2391,17 +2369,42 @@ class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer):
                 attentions=all_self_attns,
             )
     
+    @tf.function
+    def compute_combined_attns_mask(self, inputs, input_shape, past_key_values_length):
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
+        else:
+            combined_attention_mask = _expand_mask(
+                tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1]
+            )
+
+        if inputs["attention_mask"] is None and inputs["input_ids"] is not None and input_shape[-1] > 1:
+            attention_mask = tf.cast(
+                tf.math.not_equal(inputs["input_ids"], self.config.pad_token_id), inputs["input_ids"].dtype
+            )
+            attention_mask = tf.concat(
+                [
+                    tf.ones((input_shape[0], past_key_values_length), dtype=attention_mask.dtype),
+                    attention_mask,
+                ],
+                axis=-1,
+            )
+        else:
+            attention_mask = tf.ones((input_shape[0], input_shape[1] + past_key_values_length), dtype=tf.int32)
+
+        return attention_mask, combined_attention_mask
+
 
-@add_start_docstrings(
-    "The bare {{cookiecutter.uppercase_modelname}} Model outputting raw hidden-states without any specific head on top.",
-    {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
-)
 @keras_serializable
-class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
-    base_model_prefix = "model"
+class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
+    config_class = {{cookiecutter.camelcase_modelname}}Config
 
-    def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
+    def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
         self.shared = TFSharedEmbeddings(config.vocab_size, config.d_model, config.pad_token_id, name="model.shared")
 
         with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
@@ -2415,19 +2418,20 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
         self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, embed_tokens, name="encoder")
         self.decoder = TF{{cookiecutter.camelcase_modelname}}Decoder(config, embed_tokens, name="decoder")
     
-    def get_encoder(self):
-        return self.encoder
+    def get_input_embeddings(self):
+        return self.shared
 
-    def get_decoder(self):
-        return self.decoder
+    def set_input_embeddings(self, new_embeddings):
+        self.shared.weight = new_embeddings
+        self.shared.vocab_size = self.shared.weight.shape[0]
+        # retrieve correct absolute scope for embed token wrapper
+        with tf.compat.v1.variable_scope("model.shared") as shared_abs_scope_name:
+            pass
+        # Wraps layer to avoid problems with weight restoring and ensuring we're in the correct TF scope.
+        embed_tokens = TFWrappedEmbeddings(self.shared, abs_scope_name=shared_abs_scope_name)
+        self.encoder.set_embed_tokens(embed_tokens)
+        self.decoder.set_embed_tokens(embed_tokens)
     
-    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="{{cookiecutter.checkpoint_identifier}}",
-        output_type=TFSeq2SeqModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
     def call(
         self,
         input_ids=None,
@@ -2467,12 +2471,6 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
         if inputs["decoder_input_ids"] is None and inputs["decoder_inputs_embeds"] is None:
             inputs["use_cache"] = False
 
-        inputs["output_hidden_states"] = (
-            inputs["output_hidden_states"]
-            if inputs["output_hidden_states"] is not None
-            else self.config.output_hidden_states
-        )
-
         if inputs["encoder_outputs"] is None:
             inputs["encoder_outputs"] = self.encoder(
                 input_ids=inputs["input_ids"],
@@ -2521,9 +2519,87 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
             encoder_attentions=inputs["encoder_outputs"].attentions,
         )
 
+
+@add_start_docstrings(
+    "The bare {{cookiecutter.uppercase_modelname}} Model outputting raw hidden-states without any specific head on top.",
+    {{cookiecutter.uppercase_modelname}}_START_DOCSTRING,
+)
+class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel):
+    def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        
+        self.model = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="model")
+
+    def get_encoder(self):
+        return self.model.encoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
+    @add_start_docstrings_to_model_forward({{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        tokenizer_class=_TOKENIZER_FOR_DOC,
+        checkpoint="{{cookiecutter.checkpoint_identifier}}",
+        output_type=TFSeq2SeqModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
+        past_key_values=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        training=False,
+        **kwargs
+    ):
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=encoder_outputs,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        outputs = self.model(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            decoder_input_ids=inputs["decoder_input_ids"],
+            decoder_attention_mask=inputs["decoder_attention_mask"],
+            encoder_outputs=inputs["encoder_outputs"],
+            past_key_values=inputs["past_key_values"],
+            inputs_embeds=inputs["inputs_embeds"],
+            decoder_inputs_embeds=inputs["decoder_inputs_embeds"],
+            use_cache=inputs["use_cache"],
+            output_attentions=inputs["output_attentions"],
+            output_hidden_states=inputs["output_hidden_states"],
+            return_dict=inputs["return_dict"],
+            training=inputs["training"],
+        )
+
+        return outputs
+    
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
     def serving_output(self, output):
-        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
@@ -2552,7 +2628,8 @@ class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiec
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
-        self.model = TF{{cookiecutter.camelcase_modelname}}Model(config, name="model")
+        self.model = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="model")
+        self.model._set_save_spec(inputs=self.serving.input_signature)
         self.use_cache = config.use_cache
         # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
         self.final_logits_bias = self.add_weight(
@@ -2675,7 +2752,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiec
     
     # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
     def serving_output(self, output):
-        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,
+        pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
         dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
         enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None

tests/test_modeling_tf_bart.py

@@ -264,22 +264,8 @@ class TFBartModelTest(TFModelTesterMixin, unittest.TestCase):
                                 models_equal = False
                     self.assertTrue(models_equal)
 
-    @slow
-    def test_saved_model_with_hidden_states_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    @slow
-    def test_saved_model_with_attentions_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
     def test_saved_model_creation(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    def test_saved_model_creation_extended(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
+        # This test is too long (>30sec) and makes fail the CI
         pass
 
 

tests/test_modeling_tf_blenderbot.py

@@ -200,22 +200,8 @@ class TFBlenderbotModelTest(TFModelTesterMixin, unittest.TestCase):
                 name = model.get_bias()
                 assert name is None
 
-    @slow
-    def test_saved_model_with_hidden_states_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    @slow
-    def test_saved_model_with_attentions_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
     def test_saved_model_creation(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    def test_saved_model_creation_extended(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
+        # This test is too long (>30sec) and makes fail the CI
         pass
 
     def test_resize_token_embeddings(self):

tests/test_modeling_tf_blenderbot_small.py

@@ -188,29 +188,20 @@ class TFBlenderbotSmallModelTest(TFModelTesterMixin, unittest.TestCase):
         for model_class in self.all_model_classes:
             model = model_class(config)
             assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
-            x = model.get_output_layer_with_bias()
+
+            if model_class in self.all_generative_model_classes:
+                x = model.get_output_embeddings()
+                assert isinstance(x, tf.keras.layers.Layer)
+                name = model.get_bias()
+                assert isinstance(name, dict)
+                for k, v in name.items():
+                    assert isinstance(v, tf.Variable)
+            else:
+                x = model.get_output_embeddings()
                 assert x is None
-            name = model.get_prefix_bias_name()
+                name = model.get_bias()
                 assert name is None
 
-    @slow
-    def test_saved_model_with_hidden_states_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    @slow
-    def test_saved_model_with_attentions_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    def test_saved_model_creation(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    def test_saved_model_creation_extended(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
     def test_resize_token_embeddings(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -274,6 +265,10 @@ class TFBlenderbotSmallModelTest(TFModelTesterMixin, unittest.TestCase):
                                 models_equal = False
                     self.assertTrue(models_equal)
 
+    def test_saved_model_creation(self):
+        # This test is too long (>30sec) and makes fail the CI
+        pass
+
 
 def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
     """If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""

tests/test_modeling_tf_common.py

@@ -211,36 +211,35 @@ class TFModelTesterMixin:
     def test_saved_model_with_hidden_states_output(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
         config.output_hidden_states = True
+        config.output_attentions = False
+
+        if hasattr(config, "use_cache"):
+            config.use_cache = False
 
         for model_class in self.all_model_classes:
             class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
-            # A saved model is always executed in graph mode, since we merged the PR #8777
-            # the booleans in graph mode are always the ones in the config, then we update
-            # the use_cache property if it exists in order to have similar booleans with the inputs
-            if "use_cache" in class_inputs_dict:
-                config.use_cache = class_inputs_dict.pop("use_cache")
             model = model_class(config)
             num_out = len(model(class_inputs_dict))
 
             with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-                saved_model_dir = os.path.join(tmpdirname, "saved_model")
-                model = tf.keras.models.load_model(saved_model_dir)
+                model.save_pretrained(tmpdirname, saved_model=True)
+                model = tf.keras.models.load_model(os.path.join(tmpdirname, "saved_model", "1"))
                 outputs = model(class_inputs_dict)
 
                 if self.is_encoder_decoder:
-                    output = outputs["encoder_hidden_states"] if isinstance(outputs, dict) else outputs[-1]
+                    output = outputs["encoder_hidden_states"]
                 else:
-                    output = outputs["hidden_states"] if isinstance(outputs, dict) else outputs[-1]
+                    output = outputs["hidden_states"]
 
-                hidden_states = [t.numpy() for t in output]
                 self.assertEqual(len(outputs), num_out)
+
                 expected_num_layers = getattr(
                     self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
                 )
-                self.assertEqual(len(hidden_states), expected_num_layers)
+
+                self.assertEqual(len(output), expected_num_layers)
                 self.assertListEqual(
-                    list(hidden_states[0].shape[-2:]),
+                    list(output[0].shape[-2:]),
                     [self.model_tester.seq_length, self.model_tester.hidden_size],
                 )
 
@@ -248,36 +247,33 @@ class TFModelTesterMixin:
     def test_saved_model_with_attentions_output(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
         config.output_attentions = True
+        config.output_hidden_states = False
+
+        if hasattr(config, "use_cache"):
+            config.use_cache = False
 
         encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
         encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
 
         for model_class in self.all_model_classes:
             class_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
-            # A saved model is always executed in graph mode, since we merged the PR #8777
-            # the booleans in graph mode are always the ones in the config, then we update
-            # the use_cache property if it exists in order to have similar booleans with the inputs
-            if "use_cache" in class_inputs_dict:
-                config.use_cache = class_inputs_dict.pop("use_cache")
             model = model_class(config)
             num_out = len(model(class_inputs_dict))
 
             with tempfile.TemporaryDirectory() as tmpdirname:
-                saved_model_dir = os.path.join(tmpdirname, "saved_model")
-                model.save_pretrained(saved_model_dir)
-                model = tf.keras.models.load_model(saved_model_dir)
+                model.save_pretrained(tmpdirname, saved_model=True)
+                model = tf.keras.models.load_model(os.path.join(tmpdirname, "saved_model", "1"))
                 outputs = model(class_inputs_dict)
 
                 if self.is_encoder_decoder:
-                    output = outputs["encoder_attentions"] if isinstance(outputs, dict) else outputs[-1]
+                    output = outputs["encoder_attentions"]
                 else:
-                    output = outputs["attentions"] if isinstance(outputs, dict) else outputs[-1]
+                    output = outputs["attentions"]
 
-                attentions = [t.numpy() for t in output]
-                self.assertEqual(len(outputs), num_out)
-                self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+                self.assertEqual(len(output), num_out)
+                self.assertEqual(len(output), self.model_tester.num_hidden_layers)
                 self.assertListEqual(
-                    list(attentions[0].shape[-3:]),
+                    list(output[0].shape[-3:]),
                     [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
                 )
 

tests/test_modeling_tf_led.py

@@ -352,30 +352,20 @@ class TFLEDModelTest(TFModelTesterMixin, unittest.TestCase):
             self.assertEqual(model.config.output_hidden_states, True)
             check_encoder_attentions_output(outputs)
 
-    @slow
-    def test_saved_model_with_attentions_output(self):
-        # longformer has special attentions which are not
-        # compatible in graph mode
-        pass
-
-    @slow
-    def test_saved_model_with_hidden_states_output(self):
-        # TODO(JPLU, PVP) this test should pass!!! PVP:
-        # IMO there is a problem with the signature check.
-        # Test passes for TFLEDModel, but not for TFLEDForConditionalGeneration
-        # IMO the reason is that the tensor variable name cannot be changed
-        # from decoder_input_ids -> input_ids, which poses a BIG restrictions
-        pass
-
-    @slow
-    def test_saved_model_creation_extended(self):
-        # All the tests about building a saved model
-        # fails because the Seq2Seq models uses model in a model
-        # as a layer.
-        # TODO(JPLU) WARNING: NEED TO BE FIXED ASAP
+    def test_saved_model_creation(self):
+        # This test is too long (>30sec) and makes fail the CI
         pass
 
-    def test_saved_model_creation(self):
+    def test_saved_model_with_attentions_output(self):
+        # This test don't pass because of the error:
+        # condition [13,8,4,5], then [13,8,4,5], and else [13,8,4,6] must be broadcastable
+        # This occurs line 323 in modeling_tf_led.py because the condition line 255
+        # returns a tensor of shape
+        # [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + 2]
+        # if is_global_attn is True and a tensor of shape
+        # [batch_size, seq_len, self.num_heads, self.one_sided_attn_window_size * 2 + 1]
+        # This is due to the tf.concat call line 703 that adds one dimension
+        # Need to check with PVP how to properly fix this
         pass
 
 

tests/test_modeling_tf_marian.py

@@ -233,22 +233,8 @@ class TFMarianModelTest(TFModelTesterMixin, unittest.TestCase):
                 name = model.get_bias()
                 assert name is None
 
-    @slow
-    def test_saved_model_with_hidden_states_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    @slow
-    def test_saved_model_with_attentions_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
     def test_saved_model_creation(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    def test_saved_model_creation_extended(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
+        # This test is too long (>30sec) and makes fail the CI
         pass
 
     def test_resize_token_embeddings(self):

tests/test_modeling_tf_mbart.py

@@ -204,22 +204,8 @@ class TFMBartModelTest(TFModelTesterMixin, unittest.TestCase):
                 name = model.get_bias()
                 assert name is None
 
-    @slow
-    def test_saved_model_with_hidden_states_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    @slow
-    def test_saved_model_with_attentions_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
     def test_saved_model_creation(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    def test_saved_model_creation_extended(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
+        # This test is too long (>30sec) and makes fail the CI
         pass
 
     def test_resize_token_embeddings(self):

tests/test_modeling_tf_pegasus.py

@@ -231,22 +231,8 @@ class TFPegasusModelTest(TFModelTesterMixin, unittest.TestCase):
                 name = model.get_bias()
                 assert name is None
 
-    @slow
-    def test_saved_model_with_hidden_states_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    @slow
-    def test_saved_model_with_attentions_output(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
     def test_saved_model_creation(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
-        pass
-
-    def test_saved_model_creation_extended(self):
-        # TODO(JPLU, PVP) - fix this with s2s tf-serving PR
+        # This test is too long (>30sec) and makes fail the CI
         pass
 
     def test_resize_token_embeddings(self):