Fix embeddings resizing in TF models (#8657)

* Resize the biases in same time than the embeddings

* Trigger CI

* Biases are not reset anymore

* Remove get_output_embeddings + better LM model detection in generation utils

* Apply style

* First test on BERT

* Update docstring + new name

* Apply the new resizing logic to all the models

* fix tests

* Apply style

* Update the template

* Fix naming

* Fix naming

* Apply style

* Apply style

* Remove unused import

* Revert get_output_embeddings

* Trigger CI

* Update num parameters

* Restore get_output_embeddings in TFPretrainedModel and add comments

* Style

* Add decoder resizing

* Style

* Fix tests

* Separate bias and decoder resize

* Fix tests

* Fix tests

* Apply style

* Add bias resizing in MPNet

* Trigger CI

* Apply style

src/transformers/modeling_tf_utils.py

@@ -614,13 +614,32 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         """
-        Returns the model's output embeddings.
+        Returns the model's output embeddings
 
         Returns:
             :obj:`tf.keras.layers.Layer`: A torch module mapping hidden states to vocabulary.
         """
         return None  # Overwrite for models with output embeddings
 
+    def get_output_layer_with_bias(self) -> Union[None, tf.keras.layers.Layer]:
+        """
+        Get the layer that handles a bias attribute in case the model has an LM head with weights tied to the
+        embeddings.
+
+        Return:
+            :obj:`tf.keras.layers.Layer`: The layer that handles the bias, None if not an LM model.
+        """
+        return None
+
+    def get_prefix_bias_name(self) -> Union[None, str]:
+        """
+        Get the concatenated prefix name of the bias from the model name to the parent layer.
+
+        Return:
+            :obj:`str`: The prefix name of the bias.
+        """
+        return None
+
     def resize_token_embeddings(self, new_num_tokens=None) -> tf.Variable:
         """
         Resizes input token embeddings matrix of the model if :obj:`new_num_tokens != config.vocab_size`.
@@ -662,7 +681,17 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
             # TFSharedEmbeddings
             return embeddings.weight
         else:
-            raise ValueError("word embedding is not defined.")
+            # Here we build the word embeddings weights if not exists.
+            # And then we retry to get the attribute once built.
+            embeddings.build([])
+            if hasattr(embeddings, "word_embeddings"):
+                # TFBertEmbeddings, TFAlbertEmbeddings, TFElectraEmbeddings
+                return embeddings.word_embeddings
+            elif hasattr(embeddings, "weight"):
+                # TFSharedEmbeddings
+                return embeddings.weight
+            else:
+                raise ValueError("word embedding is not defined.")
 
     def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None) -> tf.Variable:
         """
@@ -684,28 +713,87 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
             :obj:`new_num_tokens` is :obj:`None`
         """
         word_embeddings = self._get_word_embeddings(old_embeddings)
+        bias_layer = self.get_output_layer_with_bias()
+
         if new_num_tokens is None:
             return word_embeddings
+
         old_num_tokens, old_embedding_dim = word_embeddings.shape
+
         if old_num_tokens == new_num_tokens:
             return word_embeddings
 
         # initialize new embeddings
         # todo: initializer range is not always passed in config.
         init_range = getattr(self.config, "initializer_range", 0.02)
+        name = (
+            self.name
+            + "/"
+            + self.base_model_prefix
+            + "/"
+            + old_embeddings.name
+            + "/"
+            + word_embeddings.name.split(":")[0]
+        )
         new_embeddings = self.add_weight(
-            "weight",
+            name=name,
             shape=[new_num_tokens, old_embedding_dim],
             initializer=get_initializer(init_range),
             dtype=tf.float32,
         )
-        init_weights = new_embeddings.numpy()
+        init_weights = tf.make_ndarray(tf.make_tensor_proto(new_embeddings.value()))
 
         # Copy token embeddings from the previous weights
         num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
-        init_weights[:num_tokens_to_copy] = word_embeddings[:num_tokens_to_copy, :]
+        init_weights[:num_tokens_to_copy] = word_embeddings.value()[:num_tokens_to_copy, :]
         new_embeddings.assign(init_weights)
 
+        if bias_layer is not None:
+            if not hasattr(bias_layer, "bias"):
+                bias_layer.build([])
+
+            # Second check in order to be sure the attribute has been properly created
+            if not hasattr(bias_layer, "bias"):
+                raise ValueError("bias is not defined.")
+
+            # initialize bias
+            init_bias = np.zeros((new_num_tokens,))
+            init_bias[:num_tokens_to_copy] = bias_layer.bias.value()[
+                :num_tokens_to_copy
+            ]  # tf.make_ndarray(tf.make_tensor_proto(bias_layer.bias.value()))[:num_tokens_to_copy]
+
+            bias_layer.bias = self.add_weight(
+                shape=(new_num_tokens,),
+                initializer="zeros",
+                trainable=True,
+                name=self.get_prefix_bias_name() + "/bias",
+            )
+
+            bias_layer.bias.assign(init_bias)
+
+        output_embeddings = self.get_output_embeddings()
+
+        if output_embeddings is not None:
+            if self.get_input_embeddings() != output_embeddings:
+                if not hasattr(output_embeddings, "decoder"):
+                    output_embeddings.build([])
+
+                # Second check in order to be sure the attribute has been properly created
+                if not hasattr(output_embeddings, "decoder"):
+                    raise ValueError("decoder is not defined.")
+
+                # initialize decoder
+                init_weights = np.zeros((new_num_tokens, old_embedding_dim))
+                init_weights[:num_tokens_to_copy] = output_embeddings.decoder.value()[:num_tokens_to_copy, :]
+
+                output_embeddings.decoder = self.add_weight(
+                    shape=(new_num_tokens, old_embedding_dim),
+                    initializer="zeros",
+                    trainable=True,
+                    name=self.get_prefix_bias_name() + "/decoder/weight",
+                )
+                output_embeddings.decoder.assign(init_weights)
+
         return new_embeddings
 
     def prune_heads(self, heads_to_prune):

src/transformers/models/albert/modeling_tf_albert.py

@@ -467,6 +467,7 @@ class TFAlbertMLMHead(tf.keras.layers.Layer):
         self.decoder_bias = self.add_weight(
             shape=(self.vocab_size,), initializer="zeros", trainable=True, name="decoder/bias"
         )
+
         super().build(input_shape)
 
     def call(self, hidden_states):
@@ -825,6 +826,32 @@ class TFAlbertForPreTraining(TFAlbertPreTrainedModel):
     def get_output_embeddings(self):
         return self.albert.embeddings
 
+    def resize_token_embeddings(self, new_num_tokens):
+        super().resize_token_embeddings(new_num_tokens=new_num_tokens)
+
+        # ALBERT is a special case where there are two bias to update
+        # even though self.bias is not used anywhere and is here
+        # just to make the loading weights from a PT model happy
+        if new_num_tokens is not None:
+            num_tokens_to_copy = min(self.predictions.bias.shape[0], new_num_tokens)
+            self.predictions.vocab_size = num_tokens_to_copy
+            init_bias = tf.zeros((new_num_tokens,))
+            init_bias[:num_tokens_to_copy] = self.predictions.bias.value()[:num_tokens_to_copy]
+            name = self.name + "/" + self.predictions.name + "/bias"
+            self.predictions.bias = self.add_weight(
+                shape=(new_num_tokens,), initializer="zeros", trainable=True, name=name
+            )
+            self.predictions.bias.assign(init_bias)
+
+            init_decoder_bias = tf.zeros((new_num_tokens,))
+            init_decoder_bias[:num_tokens_to_copy] = self.predictions.decoder_bias.value()[:num_tokens_to_copy]
+            name = self.name + "/" + self.predictions.name + "/decoder_bias"
+            self.predictions.decoder_bias = self.add_weight(
+                shape=(new_num_tokens,), initializer="zeros", trainable=True, name=name
+            )
+
+            self.predictions.decoder_bias.assign(init_decoder_bias)
+
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=TFAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def call(
@@ -933,6 +960,32 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
     def get_output_embeddings(self):
         return self.albert.embeddings
 
+    def resize_token_embeddings(self, new_num_tokens):
+        super().resize_token_embeddings(new_num_tokens=new_num_tokens)
+
+        # ALBERT is a special case where there are two bias to update
+        # even though self.bias is not used anywhere and is here
+        # just to make the loading weights from a PT model happy
+        if new_num_tokens is not None:
+            num_tokens_to_copy = min(self.predictions.bias.shape[0], new_num_tokens)
+            self.predictions.vocab_size = num_tokens_to_copy
+            init_bias = tf.zeros((new_num_tokens,))
+            init_bias[:num_tokens_to_copy] = self.predictions.bias.value()[:num_tokens_to_copy]
+            name = self.name + "/" + self.predictions.name + "/bias"
+            self.predictions.bias = self.add_weight(
+                shape=(new_num_tokens,), initializer="zeros", trainable=True, name=name
+            )
+            self.predictions.bias.assign(init_bias)
+
+            init_decoder_bias = tf.zeros((new_num_tokens,))
+            init_decoder_bias[:num_tokens_to_copy] = self.predictions.decoder_bias.value()[:num_tokens_to_copy]
+            name = self.name + "/" + self.predictions.name + "/decoder_bias"
+            self.predictions.decoder_bias = self.add_weight(
+                shape=(new_num_tokens,), initializer="zeros", trainable=True, name=name
+            )
+
+            self.predictions.decoder_bias.assign(init_decoder_bias)
+
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,

src/transformers/models/bart/modeling_tf_bart.py

@@ -1049,6 +1049,24 @@ class TFBartForConditionalGeneration(TFPretrainedBartModel):
             name="/final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False
         )
 
+    def resize_token_embeddings(self, new_num_tokens):
+        super().resize_token_embeddings(new_num_tokens=new_num_tokens)
+
+        # BART is a special case where the bias has two dimensions
+        # and not named just `bias`
+        if new_num_tokens is not None:
+            num_tokens_to_copy = min(self.final_logits_bias.shape[0], new_num_tokens)
+            init_bias = tf.zeros((new_num_tokens,))
+            init_bias[:num_tokens_to_copy] = self.final_logits_bias.value()[:num_tokens_to_copy]
+            name = self.name + "/final_logits_bias"
+            self.final_logits_bias = self.add_weight(
+                shape=(1, new_num_tokens),
+                initializer="zeros",
+                trainable=False,
+                name=name,
+            )
+            self.final_logits_bias.assign(init_bias)
+
     @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
     def call(

src/transformers/models/bert/modeling_tf_bert.py

@@ -893,6 +893,12 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
     def get_output_embeddings(self):
         return self.bert.embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.mlm.predictions
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
+
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=TFBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def call(
@@ -1002,6 +1008,12 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
     def get_output_embeddings(self):
         return self.bert.embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.mlm.predictions
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
+
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
@@ -1095,6 +1107,12 @@ class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
     def get_output_embeddings(self):
         return self.bert.embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.mlm.predictions
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
+
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,
         checkpoint="bert-base-cased",

src/transformers/models/ctrl/modeling_tf_ctrl.py

@@ -629,6 +629,12 @@ class TFCTRLLMHeadModel(TFCTRLPreTrainedModel, TFCausalLanguageModelingLoss):
     def get_output_embeddings(self):
         return self.lm_head.input_embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.lm_head
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.lm_head.name
+
     def prepare_inputs_for_generation(self, inputs, past, **kwargs):
         # only last token for inputs_ids if past is defined in kwargs
         if past:

src/transformers/models/distilbert/modeling_tf_distilbert.py

@@ -655,6 +655,12 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel, TFMaskedLanguageModel
     def get_output_embeddings(self):
         return self.vocab_projector.input_embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.vocab_projector
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.vocab_projector.name
+
     @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,

src/transformers/models/electra/modeling_tf_electra.py

@@ -882,7 +882,7 @@ class TFElectraMaskedLMHead(tf.keras.layers.Layer):
 
         super().build(input_shape)
 
-    def call(self, hidden_states):
+    def call(self, hidden_states, training=False):
         hidden_states = self.input_embeddings(hidden_states, mode="linear")
         hidden_states = hidden_states + self.bias
 
@@ -914,8 +914,14 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
         self.generator_lm_head = TFElectraMaskedLMHead(config, self.electra.embeddings, name="generator_lm_head")
 
     def get_output_embeddings(self):
+        return self.electra.embeddings
+
+    def get_output_layer_with_bias(self):
         return self.generator_lm_head
 
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.generator_lm_head.name
+
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,

src/transformers/models/flaubert/modeling_tf_flaubert.py

@@ -766,6 +766,12 @@ class TFFlaubertWithLMHeadModel(TFFlaubertPreTrainedModel):
     def get_output_embeddings(self):
         return self.pred_layer.input_embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.pred_layer
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.pred_layer.name
+
     def prepare_inputs_for_generation(self, inputs, **kwargs):
         mask_token_id = self.config.mask_token_id
         lang_id = self.config.lang_id

src/transformers/models/funnel/modeling_tf_funnel.py

@@ -1320,6 +1320,15 @@ class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss)
         self.funnel = TFFunnelMainLayer(config, name="funnel")
         self.lm_head = TFFunnelMaskedLMHead(config, self.funnel.embeddings, name="lm_head")
 
+    def get_output_embeddings(self):
+        return self.funnel.embeddings
+
+    def get_output_layer_with_bias(self):
+        return self.lm_head
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.lm_head.name
+
     @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,

src/transformers/models/longformer/modeling_tf_longformer.py

@@ -2009,6 +2009,12 @@ class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModel
     def get_output_embeddings(self):
         return self.lm_head.decoder
 
+    def get_output_layer_with_bias(self):
+        return self.lm_head
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.lm_head.name
+
     @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,

src/transformers/models/lxmert/modeling_tf_lxmert.py

@@ -1257,6 +1257,15 @@ class TFLxmertForPreTraining(TFLxmertPreTrainedModel):
             **({"obj_labels": obj_labels} if self.config.task_obj_predict else {}),
         }
 
+    def get_output_embeddings(self):
+        return self.lxmert.embeddings
+
+    def get_output_layer_with_bias(self):
+        return self.cls.predictions
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.cls.name + "/" + self.cls.predictions.name
+
     @add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=TFLxmertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def call(

src/transformers/models/mobilebert/modeling_tf_mobilebert.py

@@ -702,6 +702,10 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
     def get_input_embeddings(self):
         return self.embeddings
 
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+        self.embeddings.vocab_size = value.shape[0]
+
     def _resize_token_embeddings(self, new_num_tokens):
         raise NotImplementedError
 
@@ -1024,7 +1028,13 @@ class TFMobileBertForPreTraining(TFMobileBertPreTrainedModel):
         self.seq_relationship = TFMobileBertOnlyNSPHead(2, name="seq_relationship___cls")
 
     def get_output_embeddings(self):
-        return self.mobilebert.embeddings
+        return self.predictions.predictions
+
+    def get_output_layer_with_bias(self):
+        return self.predictions.predictions
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.predictions.name + "/" + self.predictions.predictions.name
 
     @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @replace_return_docstrings(output_type=TFMobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
@@ -1117,7 +1127,13 @@ class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModel
         self.mlm = TFMobileBertMLMHead(config, name="mlm___cls")
 
     def get_output_embeddings(self):
-        return self.mobilebert.embeddings
+        return self.mlm.predictions
+
+    def get_output_layer_with_bias(self):
+        return self.mlm.predictions
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
 
     @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(

src/transformers/models/mpnet/modeling_tf_mpnet.py

@@ -830,6 +830,12 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
     def get_output_embeddings(self):
         return self.mpnet.embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.lm_head
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.lm_head.name
+
     @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,

src/transformers/models/roberta/modeling_tf_roberta.py

@@ -810,6 +810,12 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLos
     def get_output_embeddings(self):
         return self.lm_head.decoder
 
+    def get_output_layer_with_bias(self):
+        return self.lm_head
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.lm_head.name
+
     @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
     @add_code_sample_docstrings(
         tokenizer_class=_TOKENIZER_FOR_DOC,

src/transformers/models/xlm/modeling_tf_xlm.py

@@ -803,6 +803,12 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
     def get_output_embeddings(self):
         return self.pred_layer.input_embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.pred_layer
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.pred_layer.name
+
     def prepare_inputs_for_generation(self, inputs, **kwargs):
         mask_token_id = self.config.mask_token_id
         lang_id = self.config.lang_id

src/transformers/models/xlnet/modeling_tf_xlnet.py

@@ -1221,6 +1221,12 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel, TFCausalLanguageModelingLoss):
     def get_output_embeddings(self):
         return self.lm_loss.input_embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.lm_loss
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.lm_loss.name
+
     def prepare_inputs_for_generation(self, inputs, past, use_mems=None, **kwargs):
         # Add dummy token at the end (no attention on this one)
 

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

@@ -772,10 +772,16 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
 
         self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
         self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
-
+    
     def get_output_embeddings(self):
         return self.{{cookiecutter.lowercase_modelname}}.embeddings
 
+    def get_output_layer_with_bias(self):
+        return self.mlm.predictions
+
+    def get_prefix_bias_name(self):
+        return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
+
     @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,

tests/test_modeling_tf_albert.py

@@ -272,6 +272,17 @@ class TFAlbertModelTest(TFModelTesterMixin, unittest.TestCase):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_question_answering(*config_and_inputs)
 
+    def test_model_common_attributes(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        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()
+            assert x is None
+            name = model.get_prefix_bias_name()
+            assert name is None
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:

tests/test_modeling_tf_bart.py

@@ -126,6 +126,17 @@ class TFBartModelTest(TFModelTesterMixin, unittest.TestCase):
         # Should be uncommented during patrick TF refactor
         pass
 
+    def test_model_common_attributes(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        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()
+            assert x is None
+            name = model.get_prefix_bias_name()
+            assert name is None
+
 
 @require_tf
 class TFBartHeadTests(unittest.TestCase):

tests/test_modeling_tf_bert.py

@@ -331,6 +331,25 @@ class TFBertModelTest(TFModelTesterMixin, unittest.TestCase):
         model = TFBertModel.from_pretrained("jplu/tiny-tf-bert-random")
         self.assertIsNotNone(model)
 
+    def test_model_common_attributes(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        list_lm_models = [TFBertForMaskedLM, TFBertForPreTraining, TFBertLMHeadModel]
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
+
+            if model_class in list_lm_models:
+                x = model.get_output_layer_with_bias()
+                assert isinstance(x, tf.keras.layers.Layer)
+                name = model.get_prefix_bias_name()
+                assert isinstance(name, str)
+            else:
+                x = model.get_output_layer_with_bias()
+                assert x is None
+                name = model.get_prefix_bias_name()
+                assert x is None
+
     def test_custom_load_tf_weights(self):
         model, output_loading_info = TFBertForTokenClassification.from_pretrained(
             "jplu/tiny-tf-bert-random", output_loading_info=True