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use torch.testing.assertclose instead to get more details about error in cis (#35659)

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* use torch.testing.assertclose instead to get more details about error in cis

* fix

* style

* test_all

* revert for I bert

* fixes and updates

* more image processing fixes

* more image processors

* fix mamba and co

* style

* less strick

* ok I won't be strict

* skip and be done

* up
This commit is contained in:
Arthur
2025-01-24 16:55:28 +01:00
committed by GitHub
parent 72d1a4cd53
commit b912f5ee43
255 changed files with 1048 additions and 969 deletions
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24
tests/models/tapas/test_modeling_tapas.py
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@@ -605,12 +605,12 @@ class TapasModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :3, :3], expected_slice, atol=0.0005))
torch.testing.assert_close(outputs.last_hidden_state[:, :3, :3], expected_slice, rtol=0.0005, atol=0.0005)
# test the pooled output
expected_slice = torch.tensor([[0.987518311, -0.970520139, -0.994303405]], device=torch_device)
self.assertTrue(torch.allclose(outputs.pooler_output[:, :3], expected_slice, atol=0.0005))
torch.testing.assert_close(outputs.pooler_output[:, :3], expected_slice, rtol=0.0005, atol=0.0005)
@unittest.skip(reason="Model not available yet")
def test_inference_masked_lm(self):
@@ -666,7 +666,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(logits, expected_tensor, atol=0.015))
torch.testing.assert_close(logits, expected_tensor, rtol=0.015, atol=0.015)
@slow
def test_inference_question_answering_head_conversational_absolute_embeddings(self):
@@ -716,7 +716,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(logits, expected_tensor, atol=0.01))
torch.testing.assert_close(logits, expected_tensor, rtol=0.01, atol=0.01)
@slow
def test_inference_question_answering_head_weak_supervision(self):
@@ -744,7 +744,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(logits[:, -6:], expected_slice, atol=0.4))
torch.testing.assert_close(logits[:, -6:], expected_slice, rtol=0.4, atol=0.4)
# test the aggregation logits
logits_aggregation = outputs.logits_aggregation
@@ -755,7 +755,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(logits_aggregation, expected_tensor, atol=0.001))
torch.testing.assert_close(logits_aggregation, expected_tensor, rtol=0.001, atol=0.001)
# test the predicted answer coordinates and aggregation indices
EXPECTED_PREDICTED_ANSWER_COORDINATES = [[(0, 0)], [(1, 2)]]
@@ -813,7 +813,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
# test the loss
loss = outputs.loss
expected_loss = torch.tensor(3.3527612686157227e-08, device=torch_device)
self.assertTrue(torch.allclose(loss, expected_loss, atol=1e-6))
torch.testing.assert_close(loss, expected_loss, rtol=1e-6, atol=1e-6)
# test the logits on the first example
logits = outputs.logits
@@ -834,7 +834,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(logits[0, -9:], expected_slice, atol=1e-6))
torch.testing.assert_close(logits[0, -9:], expected_slice, rtol=1e-6, atol=1e-6)
# test the aggregation logits on the second example
logits_aggregation = outputs.logits_aggregation
@@ -842,7 +842,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
self.assertEqual(logits_aggregation.shape, expected_shape)
expected_slice = torch.tensor([-4.0538, 40.0304, -5.3554, 23.3965], device=torch_device)
self.assertTrue(torch.allclose(logits_aggregation[1, -4:], expected_slice, atol=1e-4))
torch.testing.assert_close(logits_aggregation[1, -4:], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_question_answering_head_strong_supervision(self):
@@ -890,7 +890,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(logits, expected_tensor, atol=0.02))
torch.testing.assert_close(logits, expected_tensor, rtol=0.02, atol=0.02)
# test the aggregation logits
logits_aggregation = outputs.logits_aggregation
@@ -900,7 +900,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
[[16.5659733, -3.06624889, -2.34152961, -0.970244825]], device=torch_device
) # PyTorch model outputs [[16.5679, -3.0668, -2.3442, -0.9674]]
self.assertTrue(torch.allclose(logits_aggregation, expected_tensor, atol=0.003))
torch.testing.assert_close(logits_aggregation, expected_tensor, rtol=0.003, atol=0.003)
@slow
def test_inference_classification_head(self):
@@ -922,7 +922,7 @@ class TapasModelIntegrationTest(unittest.TestCase):
[[0.795137286, 9.5572]], device=torch_device
) # Note that the PyTorch model outputs [[0.8057, 9.5281]]
self.assertTrue(torch.allclose(outputs.logits, expected_tensor, atol=0.05))
torch.testing.assert_close(outputs.logits, expected_tensor, rtol=0.05, atol=0.05)
@require_torch