wrap forward passes with torch.no_grad() (#19278)

tests/models/deberta_v2/test_modeling_deberta_v2.py

@@ -299,7 +299,8 @@ class DebertaV2ModelIntegrationTest(unittest.TestCase):
 
         input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
         attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
-        output = model(input_ids, attention_mask=attention_mask)[0]
+        with torch.no_grad():
+            output = model(input_ids, attention_mask=attention_mask)[0]
         # compare the actual values for a slice.
         expected_slice = torch.tensor(
             [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]