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

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Partho
2022-10-04 19:39:23 +05:30
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parent f134d38553
commit 2403dbd607
1 changed files with 2 additions and 1 deletions
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tests/models/deberta_v2/test_modeling_deberta_v2.py
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@@ -299,7 +299,8 @@ class DebertaV2ModelIntegrationTest(unittest.TestCase):
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) 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]]) 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. # compare the actual values for a slice.
expected_slice = torch.tensor( expected_slice = torch.tensor(
[[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]