GPT can be exported to TorchScript

pytorch_pretrained_bert/modeling_openai.py

@@ -355,7 +355,7 @@ class OpenAIGPTLMHead(nn.Module):
     def set_embeddings_weights(self, model_embeddings_weights, predict_special_tokens=True):
         self.predict_special_tokens = predict_special_tokens
         embed_shape = model_embeddings_weights.shape
-        self.decoder.weight = model_embeddings_weights  # Tied weights
+        self.decoder.weight = nn.Parameter(model_embeddings_weights.clone())  # Tied weights
 
     def forward(self, hidden_state):
         lm_logits = self.decoder(hidden_state)
@@ -579,26 +579,26 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
 
         output_shape = input_shape + (hidden_states.size(-1),)
 
-        all_attentions = []
-        all_hidden_states = []
+        all_attentions = ()
+        all_hidden_states = ()
         for i, block in enumerate(self.h):
             if self.output_hidden_states:
-                all_hidden_states.append(hidden_states.view(*output_shape))
+                all_hidden_states += (hidden_states.view(*output_shape),)
 
             outputs = block(hidden_states, head_mask[i])
             hidden_states = outputs[0]
             if self.output_attentions:
-                all_attentions.append(outputs[1])
+                all_attentions += (outputs[1],)
 
         # Add last layer
         if self.output_hidden_states:
-            all_hidden_states.append(hidden_states.view(*output_shape))
+            all_hidden_states += (hidden_states.view(*output_shape),)
 
-        outputs = [hidden_states.view(*output_shape)]
+        outputs = (hidden_states.view(*output_shape),)
         if self.output_hidden_states:
-            outputs.append(all_hidden_states)
+            outputs += (all_hidden_states,)
         if self.output_attentions:
-            outputs.append(all_attentions)
+            outputs += (all_attentions,)
         return outputs  # last hidden state, (all hidden states), (all attentions)
 
 
@@ -682,7 +682,7 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
         hidden_states = transformer_outputs[0]
         lm_logits = self.lm_head(hidden_states)
 
-        outputs = [lm_logits] + transformer_outputs[1:]
+        outputs = (lm_logits,) + transformer_outputs[1:]
         if lm_labels is not None:
             # Shift so that tokens < n predict n
             shift_logits = lm_logits[..., :-1, :].contiguous()
@@ -691,7 +691,7 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
             loss_fct = CrossEntropyLoss(ignore_index=-1)
             loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
                             shift_labels.view(-1))
-            outputs = [loss] + outputs
+            outputs = (loss,) + outputs
 
         return outputs  # (loss), lm_logits, (all hidden states), (all attentions)
 
@@ -785,18 +785,18 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
         lm_logits = self.lm_head(hidden_states)
         mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids)
 
-        outputs = [lm_logits, mc_logits] + transformer_outputs[1:]
+        outputs = (lm_logits, mc_logits) + transformer_outputs[1:]
         if mc_labels is not None:
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)),
                             mc_labels.view(-1))
-            outputs = [loss] + outputs
+            outputs = (loss,) + outputs
         if lm_labels is not None:
             shift_logits = lm_logits[..., :-1, :].contiguous()
             shift_labels = lm_labels[..., 1:].contiguous()
             loss_fct = CrossEntropyLoss(ignore_index=-1)
             loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
                             shift_labels.view(-1))
-            outputs = [loss] + outputs
+            outputs = (loss,) + outputs
 
         return outputs  # (lm loss), (mc loss), lm logits, mc logits, (all hidden_states), (attentions)