Fix TF template (#9234)

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

@@ -310,18 +310,22 @@ class TF{{cookiecutter.camelcase_modelname}}Intermediate(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
 
-        self.dense = tf.keras.layers.Dense(
-            config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+        self.dense = tf.keras.layers.experimental.EinsumDense(
+            equation="abc,cd->abd",
+            output_shape=(None, config.intermediate_size),
+            bias_axes="d",
+            kernel_initializer=get_initializer(initializer_range=config.initializer_range),
+            name="dense",
         )
 
         if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = get_tf_activation(config.hidden_act)
+            self.intermediate_act_fn = get_tf_activation(activation_string=config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
 
     def call(self, hidden_states):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
+        hidden_states = self.dense(inputs=hidden_states)
+        hidden_states = self.intermediate_act_fn(inputs=hidden_states)
 
         return hidden_states
 
@@ -331,16 +335,20 @@ class TF{{cookiecutter.camelcase_modelname}}Output(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
 
-        self.dense = tf.keras.layers.Dense(
-            config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
+        self.dense = tf.keras.layers.experimental.EinsumDense(
+            equation="abc,cd->abd",
+            bias_axes="d",
+            output_shape=(None, config.hidden_size),
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="dense",
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
-        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
     def call(self, hidden_states, input_tensor, training=False):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states, training=training)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        hidden_states = self.dense(inputs=hidden_states)
+        hidden_states = self.dropout(inputs=hidden_states, training=training)
+        hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor)
 
         return hidden_states