fix #1223

pytorch_transformers/modeling_xlnet.py

@@ -743,8 +743,9 @@ class XLNetModel(XLNetPreTrainedModel):
 
         if data_mask is not None:
             # all mems can be attended to
-            mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)
-            data_mask = torch.cat([mems_mask, data_mask], dim=1)
+            if mlen > 0:
+                mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)
+                data_mask = torch.cat([mems_mask, data_mask], dim=1)
             if attn_mask is None:
                 attn_mask = data_mask[:, :, :, None]
             else:
@@ -755,7 +756,8 @@ class XLNetModel(XLNetPreTrainedModel):
 
         if attn_mask is not None:
             non_tgt_mask = -torch.eye(qlen).to(attn_mask)
-            non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)
+            if mlen > 0:
+                non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)
             non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask)
         else:
             non_tgt_mask = None
@@ -775,8 +777,11 @@ class XLNetModel(XLNetPreTrainedModel):
         ##### Segment embedding
         if token_type_ids is not None:
             # Convert `token_type_ids` to one-hot `seg_mat`
-            mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
-            cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)
+            if mlen > 0:
+                mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
+                cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)
+            else:
+                cat_ids = token_type_ids
 
             # `1` indicates not in the same segment [qlen x klen x bsz]
             seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long()