fix error with torch.no_grad and loss computation

hubconfs/bert_hubconf.py

@@ -238,8 +238,7 @@ def bertForSequenceClassification(*args, **kwargs):
                 seq_classif_logits = model(tokens_tensor, segments_tensors)
         # Or get the sequence classification loss
         >>> labels = torch.tensor([1])
-        >>> with torch.no_grad():
-                seq_classif_loss = model(tokens_tensor, segments_tensors, labels=labels)
+        >>> seq_classif_loss = model(tokens_tensor, segments_tensors, labels=labels)
     """
     model = BertForSequenceClassification.from_pretrained(*args, **kwargs)
     return model
@@ -273,8 +272,7 @@ def bertForMultipleChoice(*args, **kwargs):
                 multiple_choice_logits = model(tokens_tensor, segments_tensors)
         # Or get the multiple choice loss
         >>> labels = torch.tensor([1])
-        >>> with torch.no_grad():
-                multiple_choice_loss = model(tokens_tensor, segments_tensors, labels=labels)
+        >>> multiple_choice_loss = model(tokens_tensor, segments_tensors, labels=labels)
     """
     model = BertForMultipleChoice.from_pretrained(*args, **kwargs)
     return model
@@ -306,8 +304,7 @@ def bertForQuestionAnswering(*args, **kwargs):
                 start_logits, end_logits = model(tokens_tensor, segments_tensors)
         # Or get the total loss which is the sum of the CrossEntropy loss for the start and end token positions
         >>> start_positions, end_positions = torch.tensor([12]), torch.tensor([14])
-        >>> with torch.no_grad():
-                multiple_choice_loss = model(tokens_tensor, segments_tensors, start_positions=start_positions, end_positions=end_positions)
+        >>> multiple_choice_loss = model(tokens_tensor, segments_tensors, start_positions=start_positions, end_positions=end_positions)
     """
     model = BertForQuestionAnswering.from_pretrained(*args, **kwargs)
     return model
@@ -344,8 +341,7 @@ def bertForTokenClassification(*args, **kwargs):
                 classif_logits = model(tokens_tensor, segments_tensors)
         # Or get the token classification loss
         >>> labels = torch.tensor([[0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0]])
-        >>> with torch.no_grad():
-                classif_loss = model(tokens_tensor, segments_tensors, labels=labels)
+        >>> classif_loss = model(tokens_tensor, segments_tensors, labels=labels)
     """
     model = BertForTokenClassification.from_pretrained(*args, **kwargs)
     return model