Add 'with torch.no_grad()' to BertGeneration integration test forward passes (#14963)

tests/test_modeling_bert_generation.py

@@ -307,7 +307,8 @@ class BertGenerationEncoderIntegrationTest(unittest.TestCase):
     def test_inference_no_head_absolute_embedding(self):
         model = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
         input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
-        output = model(input_ids)[0]
+        with torch.no_grad():
+            output = model(input_ids)[0]
         expected_shape = torch.Size([1, 8, 1024])
         self.assertEqual(output.shape, expected_shape)
         expected_slice = torch.tensor(
@@ -322,7 +323,8 @@ class BertGenerationDecoderIntegrationTest(unittest.TestCase):
     def test_inference_no_head_absolute_embedding(self):
         model = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
         input_ids = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
-        output = model(input_ids)[0]
+        with torch.no_grad():
+            output = model(input_ids)[0]
         expected_shape = torch.Size([1, 8, 50358])
         self.assertEqual(output.shape, expected_shape)
         expected_slice = torch.tensor(