SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

wrap forward passes with torch.no_grad() (#19412)

Browse Source
This commit is contained in:
Partho
2022-10-11 00:34:10 +05:30
committed by GitHub
parent 5f5e264a12
commit df2f28120d
1 changed files with 2 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
tests/models/flaubert/test_modeling_flaubert.py
View File

@@ -437,7 +437,8 @@ class FlaubertModelIntegrationTest(unittest.TestCase):
def test_inference_no_head_absolute_embedding(self): def test_inference_no_head_absolute_embedding(self):
model = FlaubertModel.from_pretrained("flaubert/flaubert_base_cased") model = FlaubertModel.from_pretrained("flaubert/flaubert_base_cased")
input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
output = model(input_ids)[0] with torch.no_grad():
output = model(input_ids)[0]
expected_shape = torch.Size((1, 11, 768)) expected_shape = torch.Size((1, 11, 768))
self.assertEqual(output.shape, expected_shape) self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor( expected_slice = torch.tensor(