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wrapped forward passes in torch.no_grad() (#15037)

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Matt Churgin
2022-01-06 08:48:49 -05:00
committed by GitHub
parent 5a06118b39
commit 5ab87cd4da
1 changed files with 6 additions and 3 deletions
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9
tests/test_modeling_roberta.py
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@@ -485,7 +485,8 @@ class RobertaModelIntegrationTest(TestCasePlus):
model = RobertaForMaskedLM.from_pretrained("roberta-base") model = RobertaForMaskedLM.from_pretrained("roberta-base")
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]])
output = model(input_ids)[0] with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 11, 50265)) expected_shape = torch.Size((1, 11, 50265))
self.assertEqual(output.shape, expected_shape) self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice. # compare the actual values for a slice.
@@ -504,7 +505,8 @@ class RobertaModelIntegrationTest(TestCasePlus):
model = RobertaModel.from_pretrained("roberta-base") model = RobertaModel.from_pretrained("roberta-base")
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]])
output = model(input_ids)[0] with torch.no_grad():
output = model(input_ids)[0]
# compare the actual values for a slice. # compare the actual values for a slice.
expected_slice = torch.tensor( expected_slice = torch.tensor(
[[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]] [[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]]
@@ -521,7 +523,8 @@ class RobertaModelIntegrationTest(TestCasePlus):
model = RobertaForSequenceClassification.from_pretrained("roberta-large-mnli") model = RobertaForSequenceClassification.from_pretrained("roberta-large-mnli")
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]])
output = model(input_ids)[0] with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 3)) expected_shape = torch.Size((1, 3))
self.assertEqual(output.shape, expected_shape) self.assertEqual(output.shape, expected_shape)
expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]]) expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]])