fix hidden-state extraction

pytorch_pretrained_bert/modeling_xlnet.py

@@ -855,19 +855,22 @@ class XLNetModel(XLNetPreTrainedModel):
         for i, layer_module in enumerate(self.layer):
             # cache new mems
             new_mems.append(self.cache_mem(output_h, mems[i]))
-            hidden_states.append((output_h, output_g))
+            hidden_states.append((output_h, output_g) if output_g is not None else output_h)
 
             output_h, output_g = layer_module(output_h, output_g,
                                               attn_mask_h=non_tgt_mask, attn_mask_g=attn_mask,
                                               r=pos_emb, seg_mat=seg_mat,
                                               mems=mems[i], target_mapping=target_mapping,
                                               head_mask=head_mask)
-        hidden_states.append((output_h, output_g))
+        hidden_states.append((output_h, output_g) if output_g is not None else output_h)
         output = self.dropout(output_g if output_g is not None else output_h)
 
         # We transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method)
         output = output.permute(1, 0, 2).contiguous()
-        hidden_states = [hs.permute(1, 0, 2).contiguous() for hs in hidden_states]
+        if output_g is not None:
+            hidden_states = [h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs]
+        else:
+            hidden_states = [hs.permute(1, 0, 2).contiguous() for hs in hidden_states]
 
         return output, hidden_states, new_mems