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[ci] Re-run integration ground truth from fairseq

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Adopted best practice set by @patrickvonplaten of commenting lines run on fairseq, for easy comparison

also see #3020
This commit is contained in:
Julien Chaumond
2020-03-03 15:31:31 -05:00
parent 5b396457e5
commit f631e01d2c
4 changed files with 39 additions and 24 deletions
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31
tests/test_modeling_roberta.py
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@@ -329,10 +329,15 @@ class RobertaModelIntegrationTest(unittest.TestCase):
expected_shape = torch.Size((1, 11, 50265))
self.assertEqual(output.shape, expected_shape)
# compare the actual values for a slice.
expected_slice = torch.Tensor(
[[[33.8843, -4.3107, 22.7779], [4.6533, -2.8099, 13.6252], [1.8222, -3.6898, 8.8600]]]
expected_slice = torch.tensor(
[[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3))
# roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
# roberta.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_no_head(self):
@@ -341,10 +346,15 @@ class RobertaModelIntegrationTest(unittest.TestCase):
input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
# compare the actual values for a slice.
expected_slice = torch.Tensor(
[[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0539, -0.0174], [0.0548, 0.0799, 0.1687]]]
expected_slice = torch.tensor(
[[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3))
# roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
# roberta.eval()
# expected_slice = roberta.extract_features(input_ids)[:, :3, :3].detach()
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
@slow
def test_inference_classification_head(self):
@@ -354,5 +364,10 @@ class RobertaModelIntegrationTest(unittest.TestCase):
output = model(input_ids)[0]
expected_shape = torch.Size((1, 3))
self.assertEqual(output.shape, expected_shape)
expected_tensor = torch.Tensor([[-0.9469, 0.3913, 0.5118]])
self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-3))
expected_tensor = torch.tensor([[-0.9469, 0.3913, 0.5118]])
# roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
# roberta.eval()
# expected_tensor = roberta.predict("mnli", input_ids, return_logits=True).detach()
self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-4))