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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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12
tests/models/perceiver/test_modeling_perceiver.py
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@@ -683,7 +683,7 @@ class PerceiverModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
torch.allclose(hidden_states_no_chunk[modality], hidden_states_with_chunk[modality], atol=1e-3)
)
else:
self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
torch.testing.assert_close(hidden_states_no_chunk, hidden_states_with_chunk, rtol=1e-3, atol=1e-3)
def test_save_load(self):
for model_class in self.all_model_classes:
@@ -909,7 +909,7 @@ class PerceiverModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(logits[0, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
expected_greedy_predictions = [38, 115, 111, 121, 121, 111, 116, 109, 52]
masked_tokens_predictions = logits[0, 52:61].argmax(dim=-1).tolist()
@@ -938,7 +938,7 @@ class PerceiverModelIntegrationTest(unittest.TestCase):
expected_slice = torch.tensor([-1.1652, -0.1992, -0.7520], device=torch_device)
atol = 1e-3 if IS_ROCM_SYSTEM else 1e-4
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=atol))
torch.testing.assert_close(logits[0, :3], expected_slice, rtol=atol, atol=atol)
@slow
def test_inference_image_classification_fourier(self):
@@ -962,7 +962,7 @@ class PerceiverModelIntegrationTest(unittest.TestCase):
expected_slice = torch.tensor([-1.1295, -0.2832, 0.3226], device=torch_device)
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_image_classification_conv(self):
@@ -986,7 +986,7 @@ class PerceiverModelIntegrationTest(unittest.TestCase):
expected_slice = torch.tensor([-1.1186, 0.0554, 0.0897], device=torch_device)
self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_optical_flow(self):
@@ -1030,7 +1030,7 @@ class PerceiverModelIntegrationTest(unittest.TestCase):
device=torch_device,
)
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4))
torch.testing.assert_close(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
@slow
def test_inference_interpolate_pos_encoding(self):