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wrap forward passes with torch.no_grad() (#19413)

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Partho
2022-10-11 00:33:46 +05:30
committed by GitHub
parent c6a928cadb
commit 5f5e264a12
1 changed files with 6 additions and 3 deletions
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9
tests/models/fnet/test_modeling_fnet.py
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@@ -493,7 +493,8 @@ class FNetModelIntegrationTest(unittest.TestCase):
model.to(torch_device)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
output = model(input_ids)[0]
with torch.no_grad():
output = model(input_ids)[0]
vocab_size = 32000
@@ -536,7 +537,8 @@ class FNetModelIntegrationTest(unittest.TestCase):
model.to(torch_device)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
output = model(input_ids)[0]
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 2))
self.assertEqual(output.shape, expected_shape)
@@ -551,7 +553,8 @@ class FNetModelIntegrationTest(unittest.TestCase):
model.to(torch_device)
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]], device=torch_device)
output = model(input_ids)[0]
with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 6, model.config.hidden_size))
self.assertEqual(output.shape, expected_shape)