rename the attributes in the Bert Layer

Since the preloading of weights relies on the name of the class's
attributes changing the namespace breaks loading pretrained weights on
Bert and all related models. I reverted `self_attention` to `attention`
and us `crossattention` for the decoder instead.

transformers/modeling_bert.py

@@ -321,25 +321,24 @@ class BertOutput(nn.Module):
 class BertLayer(nn.Module):
     def __init__(self, config):
         super(BertLayer, self).__init__()
-        self.self_attention = BertAttention(config)
-        if getattr(config, "is_decoder", False):
         self.attention = BertAttention(config)
+        if getattr(config, "is_decoder", False):
+            self.crossattention = BertAttention(config)
         self.intermediate = BertIntermediate(config)
         self.output = BertOutput(config)
 
     def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_state=None):
-        self_attention_outputs = self.self_attention(hidden_states, attention_mask, head_mask)
-        self_attention_output = self_attention_outputs[0]
+        attention_outputs = self.attention(hidden_states, attention_mask, head_mask)
+        attention_output = attention_outputs[0]
 
-        attention_outputs = self_attention_outputs
         if encoder_hidden_state:
             try:
-                attention_outputs = self.attention(self_attention_output, attention_mask, head_mask, encoder_hidden_state)
+                crossattention_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")
 
-        attention_output = attention_outputs[0]
-        intermediate_output = self.intermediate(attention_output)
+        crossattention_output = crossattention_outputs[0]
+        intermediate_output = self.intermediate(crossattention_output)
         layer_output = self.output(intermediate_output, attention_output)
         outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
         return outputs
@@ -633,7 +632,7 @@ class BertModel(BertPreTrainedModel):
             See base class PreTrainedModel
         """
         for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].self_attention.prune_heads(heads)
+            self.encoder.layer[layer].attention.prune_heads(heads)
 
     def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None):
         if attention_mask is None:
@@ -737,7 +736,7 @@ class BertDecoderModel(BertPreTrainedModel):
         """
         for layer, heads in heads_to_prune.items():
             self.decoder.layer[layer].attention.prune_heads(heads)
-            self.decoder.layer[layer].self_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):
         if attention_mask is None: