Fix template (#9840)

src/transformers/models/bert/modeling_tf_bert.py

@@ -777,7 +777,9 @@ class TFBertMainLayer(tf.keras.layers.Layer):
         # Since we are adding it to the raw scores before the softmax, this is
         # effectively the same as removing these entirely.
         extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
-        extended_attention_mask = tf.multiply(tf.subtract(1.0, extended_attention_mask), -10000.0)
+        one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
+        ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
+        extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
 
         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head

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

@@ -695,7 +695,9 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
         # Since we are adding it to the raw scores before the softmax, this is
         # effectively the same as removing these entirely.
         extended_attention_mask = tf.cast(extended_attention_mask, dtype=embedding_output.dtype)
-        extended_attention_mask = tf.multiply(tf.subtract(1.0, extended_attention_mask), -10000.0)
+        one_cst = tf.constant(1.0, dtype=embedding_output.dtype)
+        ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype)
+        extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst)
 
         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head
@@ -917,7 +919,7 @@ 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, inputs_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings.word_embeddings, name="mlm___cls")
+        self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, input_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings.word_embeddings, name="mlm___cls")
     
     def get_lm_head(self) -> tf.keras.layers.Layer:
         return self.mlm.predictions
@@ -1014,7 +1016,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelca
             logger.warning("If you want to use `TF{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
 
         self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
-        self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, inputs_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings.word_embeddings, name="mlm___cls")
+        self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, input_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings.word_embeddings, name="mlm___cls")
 
     def get_lm_head(self) -> tf.keras.layers.Layer:
         return self.mlm.predictions