update GPT2 loss computation for more flexbility

pytorch_pretrained_bert/modeling_gpt2.py

@@ -336,6 +336,7 @@ class GPT2MultipleChoiceHead(nn.Module):
         # (bsz, num_choices, 1, hidden_size)
         multiple_choice_h = hidden_states.gather(2, mc_token_ids).squeeze(2)
         # (bsz, num_choices, hidden_size)
+        multiple_choice_h = self.dropout(multiple_choice_h.transpose(1, 2)).transpose(1, 2)
         multiple_choice_logits = self.linear(multiple_choice_h).squeeze(-1)
         # (bsz, num_choices)
         return multiple_choice_logits
@@ -665,9 +666,8 @@ 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].contiguous()
-            shift_labels = lm_labels[:, 1:].contiguous()
-
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = lm_labels[..., 1:].contiguous()
             # Flatten the tokens
             loss_fct = CrossEntropyLoss(ignore_index=-1)
             loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
@@ -746,11 +746,10 @@ 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].contiguous()
-            shift_labels = lm_labels[:, 1:].contiguous()
+            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)))
+            losses.append(loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)))
         if mc_labels is not None:
             loss_fct = CrossEntropyLoss()
             losses.append(loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)))