BartForSequenceClassification: fix num_labels, add test (#3110)

src/transformers/modeling_bart.py

@@ -1324,7 +1324,7 @@ class BartForSequenceClassification(PretrainedBartModel):
         # Prepend logits
         outputs = (logits,) + outputs[1:]  # Add hidden states and attention if they are here
         if labels is not None:  # prepend loss to output,
-            loss = F.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1))
+            loss = F.cross_entropy(logits.view(-1, self.config.num_labels), labels.view(-1))
             outputs = (loss,) + outputs
 
         return outputs

tests/test_modeling_bart.py

@@ -171,7 +171,7 @@ class BartHeadTests(unittest.TestCase):
 
     vocab_size = 99
 
-    def test_lm_forward(self):
+    def _get_config_and_data(self, output_past=False):
         input_ids = torch.tensor(
             [
                 [71, 82, 18, 33, 46, 91, 2],
@@ -191,9 +191,8 @@ class BartHeadTests(unittest.TestCase):
             dtype=torch.long,
             device=torch_device,
         )
-        batch_size = input_ids.shape[0]
-        decoder_lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size)
 
+        batch_size = input_ids.shape[0]
         config = BartConfig(
             vocab_size=self.vocab_size,
             d_model=24,
@@ -204,14 +203,25 @@ class BartHeadTests(unittest.TestCase):
             encoder_ffn_dim=32,
             decoder_ffn_dim=32,
             max_position_embeddings=48,
+            output_past=output_past,
         )
+        return config, input_ids, batch_size
+
+    def test_sequence_classification_forward(self):
+        config, input_ids, batch_size = self._get_config_and_data()
+        labels = _long_tensor([2] * batch_size).to(torch_device)
         model = BartForSequenceClassification(config)
         model.to(torch_device)
-        outputs = model.forward(input_ids=input_ids, decoder_input_ids=input_ids)
-        logits = outputs[0]
+        outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, labels=labels)
+        logits = outputs[1]
         expected_shape = torch.Size((batch_size, config.num_labels))
         self.assertEqual(logits.shape, expected_shape)
+        loss = outputs[0]
+        self.assertIsInstance(loss.item(), float)
 
+    def test_lm_forward(self):
+        config, input_ids, batch_size = self._get_config_and_data(output_past=False)
+        decoder_lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size)
         lm_model = BartForMaskedLM(config)
         lm_model.to(torch_device)
         loss, logits, enc_features = lm_model.forward(