create LxmertModelIntegrationTest Pytorch (#9989)

* create LxmertModelIntegrationTest

* implementation using numpy seeding to fix inputs params.

* fix code quality

* isort check

tests/test_modeling_lxmert.py

@@ -17,6 +17,8 @@
 import copy
 import unittest
 
+import numpy as np
+
 from transformers import is_torch_available
 from transformers.models.auto import get_values
 from transformers.testing_utils import require_torch, slow, torch_device
@@ -727,3 +729,24 @@ class LxmertModelTest(ModelTesterMixin, unittest.TestCase):
         self.assertIsNotNone(attentions_vision.grad)
         self.assertIsNotNone(hidden_states_vision.grad)
         self.assertIsNotNone(attentions_vision.grad)
+
+
+@require_torch
+class LxmertModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_no_head_absolute_embedding(self):
+        model = LxmertModel.from_pretrained(LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST[0])
+        input_ids = torch.tensor([[101, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 102]])
+        num_visual_features = 10
+        _, visual_feats = np.random.seed(0), np.random.rand(1, num_visual_features, LxmertModel.config.visual_feat_dim)
+        _, visual_pos = np.random.seed(0), np.random.rand(1, num_visual_features, 4)
+        visual_feats = torch.as_tensor(visual_feats, dtype=torch.float32)
+        visual_pos = torch.as_tensor(visual_pos, dtype=torch.float32)
+        output = model(input_ids, visual_feats=visual_feats, visual_pos=visual_pos)[0]
+        expected_shape = torch.Size([1, 11, 768])
+        self.assertEqual(expected_shape, output.shape)
+        expected_slice = torch.tensor(
+            [[[0.2417, -0.9807, 0.1480], [1.2541, -0.8320, 0.5112], [1.4070, -1.1052, 0.6990]]]
+        )
+
+        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))