Fix loss computation for indexes bigger than max_seq_length.

modeling_pytorch.py

@@ -485,9 +485,22 @@ class BertForQuestionAnswering(nn.Module):
         start_logits, end_logits = logits.split(1, dim=-1)
 
         if start_positions is not None and end_positions is not None:
-            loss_fct = CrossEntropyLoss()
-            start_loss = loss_fct(start_logits, start_positions)
-            end_loss = loss_fct(end_logits, end_positions)
+            #loss_fct = CrossEntropyLoss()
+            #start_loss = loss_fct(start_logits, start_positions)
+            #end_loss = loss_fct(end_logits, end_positions)
+            batch_size, seq_length = input_ids.size()
+            
+            def compute_loss(logits, positions):
+                max_position = positions.max().item()
+                one_hot = torch.FloatTensor(batch_size, max(max_position, seq_length) +1).zero_()
+                one_hot = one_hot.scatter(1, positions, 1)
+                one_hot = one_hot[:, :seq_length]
+                log_probs = nn.functional.log_softmax(logits, dim = -1).view(batch_size, seq_length)
+                loss = -torch.mean(torch.sum(one_hot*log_probs), dim = -1)
+                return loss
+            
+            start_loss = compute_loss(start_logits, start_positions)
+            end_loss = compute_loss(end_logits, end_positions)
             total_loss = (start_loss + end_loss) / 2
             return total_loss, (start_logits, end_logits)
         else: