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

[Tests, GPU, SLOW] fix a bunch of GPU hardcoded tests in Pytorch (#4468)

Browse Source 
* fix gpu slow tests in pytorch

* change model to device syntax
This commit is contained in:
Patrick von Platen
2020-05-19 21:35:04 +02:00
committed by GitHub
parent 5856999a9f
commit aa925a52fa
11 changed files with 28 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
tests/test_modeling_longformer.py
View File

@@ -214,32 +214,39 @@ class LongformerModelIntegrationTest(unittest.TestCase):
@slow
def test_inference_no_head(self):
model = LongformerModel.from_pretrained("longformer-base-4096")
model.to(torch_device)
# 'Hello world! ' repeated 1000 times
input_ids = torch.tensor([[0] + [20920, 232, 328, 1437] * 1000 + [2]]) # long input
input_ids = torch.tensor(
[[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device
) # long input
attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device)
attention_mask[:, [1, 4, 21]] = 2 # Set global attention on a few random positions
output = model(input_ids, attention_mask=attention_mask)[0]
expected_output_sum = torch.tensor(74585.8594)
expected_output_mean = torch.tensor(0.0243)
expected_output_sum = torch.tensor(74585.8594, device=torch_device)
expected_output_mean = torch.tensor(0.0243, device=torch_device)
self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4))
self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4))
@slow
def test_inference_masked_lm(self):
model = LongformerForMaskedLM.from_pretrained("longformer-base-4096")
model.to(torch_device)
# 'Hello world! ' repeated 1000 times
input_ids = torch.tensor([[0] + [20920, 232, 328, 1437] * 1000 + [2]]) # long input
input_ids = torch.tensor(
[[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device
) # long input
loss, prediction_scores = model(input_ids, masked_lm_labels=input_ids)
expected_loss = torch.tensor(0.0620)
expected_prediction_scores_sum = torch.tensor(-6.1599e08)
expected_prediction_scores_mean = torch.tensor(-3.0622)
expected_loss = torch.tensor(0.0620, device=torch_device)
expected_prediction_scores_sum = torch.tensor(-6.1599e08, device=torch_device)
expected_prediction_scores_mean = torch.tensor(-3.0622, device=torch_device)
input_ids = input_ids.to(torch_device)
self.assertTrue(torch.allclose(loss, expected_loss, atol=1e-4))
self.assertTrue(torch.allclose(prediction_scores.sum(), expected_prediction_scores_sum, atol=1e-4))