Added Integration testing for DistilBert model from issue #9948' (#9995)

tests/test_modeling_distilbert.py

@@ -24,6 +24,8 @@ from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention
 
 
 if is_torch_available():
+    import torch
+
     from transformers import (
         DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
         DistilBertConfig,
@@ -246,3 +248,19 @@ class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
         for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             model = DistilBertModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
+
+
+@require_torch
+class DistilBertModelIntergrationTest(unittest.TestCase):
+    @slow
+    def test_inference_no_head_absolute_embedding(self):
+        model = DistilBertModel.from_pretrained("distilbert-base-uncased")
+        input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
+        output = model(input_ids)[0]
+        expected_shape = torch.Size((1, 11, 768))
+        self.assertEqual(output.shape, expected_shape)
+        expected_slice = torch.tensor(
+            [[[0.4026, -0.2919, 0.3902], [0.3828, -0.2129, 0.3563], [0.3919, -0.2287, 0.3438]]]
+        )
+
+        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))