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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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6
tests/models/persimmon/test_modeling_persimmon.py
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@@ -410,7 +410,7 @@ class PersimmonModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
torch.testing.assert_close(original_short_output, scaled_short_output, rtol=1e-5, atol=1e-5)
else:
self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5))
@@ -483,14 +483,14 @@ class PersimmonIntegrationTest(unittest.TestCase):
[[-11.4726, -11.1495, -11.2694, -11.2223, -10.9452, -11.0663, -11.0031, -11.1028]]
)
# change dtype to `torch.float32` before calling `mean` to avoid `nan` values
torch.testing.assert_close(out.cpu().to(torch.float32).mean(-1), EXPECTED_MEAN, atol=1e-4, rtol=1e-4)
torch.testing.assert_close(out.cpu().to(torch.float32).mean(-1), EXPECTED_MEAN, rtol=1e-4, atol=1e-4)
# fmt: off
EXPECTED_SLICE = torch.tensor(
[-16.9062, -16.9062, -16.9062, -16.9062, -16.8906, -16.9062, -16.9531, -16.9062, -16.9062, -16.9062, -16.9531, -16.9062, -16.9531, -16.9062, -16.9062, -16.9062, -16.9062, -16.9062, -16.9531, -16.9062, -16.9062, -16.9062, -16.9062, -16.9062, -16.9062, -16.9531, -16.9062, -16.9531, -16.9062, -16.9062],
dtype=torch.float16
)
# fmt: on
torch.testing.assert_close(out.cpu()[0, 0, :30], EXPECTED_SLICE, atol=1e-5, rtol=1e-5)
torch.testing.assert_close(out.cpu()[0, 0, :30], EXPECTED_SLICE, rtol=1e-5, atol=1e-5)
backend_empty_cache(torch_device)
del model