fix consistency CrossEntropyLoss in modeling_bart (#6265)

src/transformers/modeling_bart.py

@@ -1040,7 +1040,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
 
         masked_lm_loss = None
         if labels is not None:
-            loss_fct = nn.CrossEntropyLoss()
+            loss_fct = CrossEntropyLoss()
             # TODO(SS): do we need to ignore pad tokens in labels?
             masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
 
@@ -1179,7 +1179,8 @@ class BartForSequenceClassification(PretrainedBartModel):
 
         loss = None
         if labels is not None:
-            loss = F.cross_entropy(logits.view(-1, self.config.num_labels), labels.view(-1))
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
 
         if not return_dict:
             output = (logits,) + outputs[1:]