fix xlm torchscript

pytorch_pretrained_bert/modeling_xlm.py

@@ -536,7 +536,7 @@ class XLMModel(XLMPreTrainedModel):
 
         # positions
         if positions is None:
-            positions = input_ids.new(slen).long()
+            positions = input_ids.new((slen,)).long()
             positions = torch.arange(slen, out=positions).unsqueeze(0)
         else:
             assert positions.size() == (bs, slen)  # (slen, bs)
@@ -585,17 +585,17 @@ class XLMModel(XLMPreTrainedModel):
         tensor *= mask.unsqueeze(-1).to(tensor.dtype)
 
         # transformer layers
-        hidden_states = []
-        attentions = []
+        hidden_states = ()
+        attentions = ()
         for i in range(self.n_layers):
             if self.output_hidden_states:
-                hidden_states.append(tensor)
+                hidden_states = hidden_states + (tensor,)
 
             # self attention
             attn_outputs = self.attentions[i](tensor, attn_mask, cache=cache, head_mask=head_mask[i])
             attn = attn_outputs[0]
             if self.output_attentions:
-                attentions.append(attn_outputs[1])
+                attentions = attentions + (attn_outputs[1],)
             attn = F.dropout(attn, p=self.dropout, training=self.training)
             tensor = tensor + attn
             tensor = self.layer_norm1[i](tensor)
@@ -614,7 +614,7 @@ class XLMModel(XLMPreTrainedModel):
 
         # Add last hidden state
         if self.output_hidden_states:
-            hidden_states.append(tensor)
+            hidden_states = hidden_states + (tensor,)
 
         # update cache length
         if cache is not None:
@@ -623,11 +623,11 @@ class XLMModel(XLMPreTrainedModel):
         # move back sequence length to dimension 0
         # tensor = tensor.transpose(0, 1)
 
-        outputs = [tensor]
+        outputs = (tensor,)
         if self.output_hidden_states:
-            outputs.append(hidden_states)
+            outputs = outputs + (hidden_states,)
         if self.output_attentions:
-            outputs.append(attentions)
+            outputs = outputs + (attentions,)
         return outputs  # outputs, (hidden_states), (attentions)