fix syntax errors

transformers/modeling_bert.py

@@ -201,7 +201,7 @@ class BertSelfAttention(nn.Module):
     def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None):
         mixed_key_layer = self.key(hidden_states)
         mixed_value_layer = self.value(hidden_states)
-        if encoder_hidden_states:  # if encoder-decoder attention
+        if encoder_hidden_states is not None:  # if encoder-decoder attention
             mixed_query_layer = self.query(encoder_hidden_states)
         else:
             mixed_query_layer = self.query(hidden_states)
@@ -331,11 +331,12 @@ class BertLayer(nn.Module):
         attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
         attention_output = attention_outputs[0]
 
-        if encoder_hidden_state:
+        if encoder_hidden_state is not None:
             try:
                 attention_outputs = self.crossattention(attention_output, attention_mask, head_mask, encoder_hidden_state)
             except AttributeError as ae:
-                raise ae("you need to set `is_encoder` to True in the configuration to instantiate an encoder layer")
+                print("You need to set `is_encoder` to True in the configuration to instantiate an encoder layer:", ae)
+                raise
 
         attention_output = attention_outputs[0]
         intermediate_output = self.intermediate(attention_output)
@@ -382,7 +383,7 @@ class BertDecoder(nn.Module):
         config.is_decoder = True
         self.output_attentions = config.output_attentions
         self.output_hidden_states = config.output_hidden_states
-        self.layers = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
+        self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
 
     def forward(self, hidden_states, encoder_outputs, attention_mask=None, head_mask=None):
         all_hidden_states = ()
@@ -738,7 +739,7 @@ class BertDecoderModel(BertPreTrainedModel):
             self.decoder.layer[layer].attention.prune_heads(heads)
             self.decoder.layer[layer].crossattention.prune_heads(heads)
 
-    def forward(self, input_ids, encoder_outputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
+    def forward(self, input_ids, encoder_outputs, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, training=False):
         if attention_mask is None:
             attention_mask = torch.ones_like(input_ids)
         if token_type_ids is None:
@@ -782,7 +783,7 @@ class BertDecoderModel(BertPreTrainedModel):
         sequence_output = decoder_outputs[0]
         pooled_output = self.pooler(sequence_output)
 
-        outputs = (sequence_output, pooled_output,) + encoder_outputs[1:]  # add hidden_states and attentions if they are here
+        outputs = (sequence_output, pooled_output,) + decoder_outputs[1:]  # add hidden_states and attentions if they are here
         return outputs  # sequence_output, pooled_output, (hidden_states), (attentions)
 
 
@@ -1387,8 +1388,7 @@ class Bert2Rnd(BertPreTrainedModel):
                                        head_mask=head_mask)
         encoder_output = encoder_outputs[0]
 
-        decoder_input = torch.empty_like(input_ids).normal_(mean=0.0, std=self.config.initializer_range)
-        decoder_outputs = self.decoder(decoder_input,
+        decoder_outputs = self.decoder(input_ids,
                                        encoder_output,
                                        token_type_ids=token_type_ids,
                                        position_ids=position_ids,