Fix test failures due to old torch issue with non-contiguous view

pytorch_pretrained_bert/modeling_gpt2.py

@@ -618,8 +618,8 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
         lm_logits = self.lm_head(hidden_states)
         if lm_labels is not None:
             # Shift so that tokens < n predict n
-            shift_logits = lm_logits[:, :-1]
-            shift_labels = lm_labels[:, 1:]
+            shift_logits = lm_logits[:, :-1].contiguous()
+            shift_labels = lm_labels[:, 1:].contiguous()
 
             # In tensorflow, it's [batch, d_0, d_1, ..., d_{r-1}, num_classes]
             # in pytorch, it's [batch, num_classes, d_0, d_1, ..., d_{r-1}]
@@ -698,8 +698,8 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
         mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids)
         losses = []
         if lm_labels is not None:
-            shift_logits = lm_logits[:, :-1]
-            shift_labels = lm_labels[:, 1:]
+            shift_logits = lm_logits[:, :-1].contiguous()
+            shift_labels = lm_labels[:, 1:].contiguous()
             loss_fct = CrossEntropyLoss(ignore_index=-1)
             losses.append(loss_fct(shift_logits.view(-1,
                           shift_logits.size(-1)), shift_labels.view(-1)))

pytorch_pretrained_bert/modeling_openai.py

@@ -717,8 +717,8 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
         lm_logits = self.lm_head(hidden_states)
         if lm_labels is not None:
             # Shift so that tokens < n predict n
-            shift_logits = lm_logits[:, :-1]
-            shift_labels = lm_labels[:, 1:]
+            shift_logits = lm_logits[:, :-1].contiguous()
+            shift_labels = lm_labels[:, 1:].contiguous()
 
             # In tensorflow, it's [batch, d_0, d_1, ..., d_{r-1}, num_classes]
             # in pytorch, it's [batch, num_classes, d_0, d_1, ..., d_{r-1}]
@@ -811,8 +811,8 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
         mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids)
         losses = []
         if lm_labels is not None:
-            shift_logits = lm_logits[:, :-1]
-            shift_labels = lm_labels[:, 1:]
+            shift_logits = lm_logits[:, :-1].contiguous()
+            shift_labels = lm_labels[:, 1:].contiguous()
             loss_fct = CrossEntropyLoss(ignore_index=-1)
             losses.append(loss_fct(shift_logits.view(-1,
                           shift_logits.size(-1)), shift_labels.view(-1)))