Fix the issue of using only inputs_embeds in convbert model (#21398)

* Fix the input embeds issue with tests

* Fix black and isort issue

* Clean up tests

* Add slow tag to the test introduced

* Incorporate PR feedbacks

src/transformers/models/convbert/modeling_convbert.py

@@ -818,12 +818,12 @@ class ConvBertModel(ConvBertPreTrainedModel):
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
             input_shape = input_ids.size()
-            batch_size, seq_length = input_shape
         elif inputs_embeds is not None:
             input_shape = inputs_embeds.size()[:-1]
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
+        batch_size, seq_length = input_shape
         device = input_ids.device if input_ids is not None else inputs_embeds.device
 
         if attention_mask is None:

tests/models/convbert/test_modeling_convbert.py

@@ -437,6 +437,17 @@ class ConvBertModelTest(ModelTesterMixin, unittest.TestCase):
                 loaded = torch.jit.load(os.path.join(tmp, "traced_model.pt"), map_location=torch_device)
                 loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device))
 
+    def test_model_for_input_embeds(self):
+        batch_size = 2
+        seq_length = 10
+        inputs_embeds = torch.rand([batch_size, seq_length, 768])
+        config = self.model_tester.get_config()
+        model = ConvBertModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(inputs_embeds=inputs_embeds)
+        self.assertEqual(result.last_hidden_state.shape, (batch_size, seq_length, config.hidden_size))
+
 
 @require_torch
 class ConvBertModelIntegrationTest(unittest.TestCase):