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/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",