fixing SequenceSummary head in TF 2.0

transformers/modeling_tf_utils.py

@@ -394,8 +394,8 @@ class TFSequenceSummary(tf.keras.layers.Layer):
             # We can probably just use the multi-head attention module of PyTorch >=1.1.0
             raise NotImplementedError
 
-        self.summary = None
-        if hasattr(config, 'summary_use_proj') and config.summary_use_proj:
+        self.has_summary = hasattr(config, 'summary_use_proj') and config.summary_use_proj
+        if self.has_summary:
             if hasattr(config, 'summary_proj_to_labels') and config.summary_proj_to_labels and config.num_labels > 0:
                 num_classes = config.num_labels
             else:
@@ -404,16 +404,16 @@ class TFSequenceSummary(tf.keras.layers.Layer):
                                                     kernel_initializer=get_initializer(initializer_range),
                                                     name='summary')
 
-        self.activation = None
-        if hasattr(config, 'summary_activation') and config.summary_activation == 'tanh':
+        self.has_activation = hasattr(config, 'summary_activation') and config.summary_activation == 'tanh'
+        if self.has_activation:
             self.activation = tf.keras.activations.tanh
 
-        self.first_dropout = None
-        if hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0:
+        self.has_first_dropout = hasattr(config, 'summary_first_dropout') and config.summary_first_dropout > 0
+        if self.has_first_dropout:
             self.first_dropout = tf.keras.layers.Dropout(config.summary_first_dropout)
 
-        self.last_dropout = None
-        if hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0:
+        self.has_last_dropout = hasattr(config, 'summary_last_dropout') and config.summary_last_dropout > 0
+        if self.has_last_dropout:
             self.last_dropout = tf.keras.layers.Dropout(config.summary_last_dropout)
 
     def call(self, inputs, training=False):
@@ -456,17 +456,17 @@ class TFSequenceSummary(tf.keras.layers.Layer):
         elif self.summary_type == 'attn':
             raise NotImplementedError
 
-        if training and self.first_dropout is not None:
-            output = self.first_dropout(output)
+        if self.has_first_dropout:
+            output = self.first_dropout(output, training=training)
 
-        if self.summary is not None:
+        if self.has_summary:
             output = self.summary(output)
 
-        if self.activation is not None:
+        if self.has_activation:
             output = self.activation(output)
 
-        if training and self.last_dropout is not None:
-            output = self.last_dropout(output)
+        if self.has_last_dropout:
+            output = self.last_dropout(output, training=training)
 
         return output