fix a bug in early exit of bert

fastNLP/modules/encoder/bert.py

@@ -374,20 +374,18 @@ class BertEncoder(nn.Module):
         self.num_output_layer = max(min(num_output_layer, len(self.layer)), 0)
         if self.num_output_layer + 1 < len(self.layer):
             logger.info(f'The transformer encoder will early exit after layer-{self.num_output_layer} '
-                        f'(start from 0)!')
+                        f'(layer 0 means embedding layer)!')
 
     def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
         all_encoder_layers = []
         for idx, layer_module in enumerate(self.layer):
-            if idx > self.num_output_layer:
+            if idx >= self.num_output_layer:
                 break
             hidden_states = layer_module(hidden_states, attention_mask)
             if output_all_encoded_layers:
                 all_encoder_layers.append(hidden_states)
         if not output_all_encoded_layers:
             all_encoder_layers.append(hidden_states)
-        if len(all_encoder_layers) == 0:
-            all_encoder_layers.append(hidden_states)
         return all_encoder_layers
 
 
@@ -445,8 +443,8 @@ class BertModel(nn.Module):
         self.hidden_size = self.config.hidden_size
         self.model_type = 'bert'
         neg_num_output_layer = kwargs.get('neg_num_output_layer', -1)
-        pos_num_output_layer = kwargs.get('pos_num_output_layer', self.config.num_hidden_layers - 1)
-        self.num_output_layer = max(neg_num_output_layer + self.config.num_hidden_layers, pos_num_output_layer)
+        pos_num_output_layer = kwargs.get('pos_num_output_layer', self.config.num_hidden_layers)
+        self.num_output_layer = max(neg_num_output_layer + 1 + self.config.num_hidden_layers, pos_num_output_layer)
         if hasattr(config, 'sinusoidal_pos_embds'):
             self.model_type = 'distilbert'
         elif 'model_type' in kwargs:
@@ -535,6 +533,7 @@ class BertModel(nn.Module):
         encoded_layers = self.encoder(embedding_output,
                                       extended_attention_mask,
                                       output_all_encoded_layers=output_all_encoded_layers)
+        encoded_layers.insert(0, embedding_output)
         sequence_output = encoded_layers[-1]
         if self.model_type != 'distilbert':
             pooled_output = self.pooler(sequence_output)
@@ -542,8 +541,6 @@ class BertModel(nn.Module):
             pooled_output = sequence_output[:, 0]
         if not output_all_encoded_layers:
             encoded_layers = encoded_layers[-1]
-        else:
-            encoded_layers.insert(0, embedding_output)
         return encoded_layers, pooled_output
 
     @classmethod