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

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

Browse Source 
* 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
Download Patch File Download Diff File Expand all files Collapse all files

10
tests/models/stablelm/test_modeling_stablelm.py
View File

@@ -395,7 +395,7 @@ class StableLmModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
# 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))
@@ -465,11 +465,11 @@ class StableLmModelIntegrationTest(unittest.TestCase):
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor([[2.7146, 2.4245, 1.5616, 1.4424, 2.6790]]).to(torch_device)
self.assertTrue(torch.allclose(output.mean(dim=-1), EXPECTED_MEAN, atol=1e-4, rtol=1e-4))
torch.testing.assert_close(output.mean(dim=-1), EXPECTED_MEAN, rtol=1e-4, atol=1e-4)
# Expected logits sliced from [0, 0, 0:30]
EXPECTED_SLICE = torch.tensor([7.1030, -1.4195, 9.9206, 7.7008, 4.9891, 4.2169, 5.5426, 3.7878, 6.7593, 5.7360, 8.4691, 5.5448, 5.0544, 10.4129, 8.5573, 13.0405, 7.3265, 3.5868, 6.1106, 5.9406, 5.6376, 5.7490, 5.4850, 4.8124, 5.1991, 4.6419, 4.5719, 9.9588, 6.7222, 4.5070]).to(torch_device) # fmt: skip
self.assertTrue(torch.allclose(output[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4))
torch.testing.assert_close(output[0, 0, :30], EXPECTED_SLICE, rtol=1e-4, atol=1e-4)
@slow
def test_model_stablelm_3b_4e1t_generation(self):
@@ -498,11 +498,11 @@ class StableLmModelIntegrationTest(unittest.TestCase):
# Expected mean on dim = -1
EXPECTED_MEAN = torch.tensor([[-2.7196, -3.6099, -2.6877, -3.1973, -3.9344]]).to(torch_device)
self.assertTrue(torch.allclose(output.mean(dim=-1), EXPECTED_MEAN, atol=1e-4, rtol=1e-4))
torch.testing.assert_close(output.mean(dim=-1), EXPECTED_MEAN, rtol=1e-4, atol=1e-4)
# Expected logits sliced from [0, 0, 0:30]
EXPECTED_SLICE = torch.tensor([2.8364, 5.3811, 5.1659, 7.5485, 4.3219, 6.3315, 1.3967, 6.9147, 3.9679, 6.4786, 5.9176, 3.3067, 5.2917, 0.1485, 3.9630, 7.9947,10.6727, 9.6757, 8.8772, 8.3527, 7.8445, 6.6025, 5.5786, 7.0985,6.1369, 3.4259, 1.9397, 4.6157, 4.8105, 3.1768]).to(torch_device) # fmt: skip
self.assertTrue(torch.allclose(output[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4))
torch.testing.assert_close(output[0, 0, :30], EXPECTED_SLICE, rtol=1e-4, atol=1e-4)
@slow
def test_model_tiny_random_stablelm_2_generation(self):