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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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20
tests/models/hubert/test_modeling_hubert.py
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@@ -812,7 +812,7 @@ class HubertModelIntegrationTest(unittest.TestCase):
expected_logits = torch.tensor([7.6692, 17.7795, 11.1562, 11.8232], dtype=torch.float16, device=torch_device)
self.assertListEqual(predicted_ids.tolist(), expected_labels)
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=3e-2))
torch.testing.assert_close(predicted_logits, expected_logits, rtol=3e-2, atol=3e-2)
def test_inference_intent_classification(self):
model = HubertForSequenceClassification.from_pretrained(
@@ -849,9 +849,9 @@ class HubertModelIntegrationTest(unittest.TestCase):
self.assertListEqual(predicted_ids_location.tolist(), expected_labels_location)
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
self.assertTrue(torch.allclose(predicted_logits_action, expected_logits_action, atol=3e-1))
self.assertTrue(torch.allclose(predicted_logits_object, expected_logits_object, atol=3e-1))
self.assertTrue(torch.allclose(predicted_logits_location, expected_logits_location, atol=3e-1))
torch.testing.assert_close(predicted_logits_action, expected_logits_action, rtol=3e-1, atol=3e-1)
torch.testing.assert_close(predicted_logits_object, expected_logits_object, rtol=3e-1, atol=3e-1)
torch.testing.assert_close(predicted_logits_location, expected_logits_location, rtol=3e-1, atol=3e-1)
def test_inference_speaker_identification(self):
model = HubertForSequenceClassification.from_pretrained(
@@ -877,7 +877,7 @@ class HubertModelIntegrationTest(unittest.TestCase):
self.assertListEqual(predicted_ids.tolist(), expected_labels)
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=10))
torch.testing.assert_close(predicted_logits, expected_logits, rtol=10, atol=10)
def test_inference_emotion_recognition(self):
model = HubertForSequenceClassification.from_pretrained(
@@ -899,7 +899,7 @@ class HubertModelIntegrationTest(unittest.TestCase):
self.assertListEqual(predicted_ids.tolist(), expected_labels)
# TODO: lower the tolerance after merging the padding fix https://github.com/pytorch/fairseq/pull/3572
self.assertTrue(torch.allclose(predicted_logits, expected_logits, atol=1e-1))
torch.testing.assert_close(predicted_logits, expected_logits, rtol=1e-1, atol=1e-1)
def test_inference_distilhubert(self):
model = HubertModel.from_pretrained("ntu-spml/distilhubert").to(torch_device)
@@ -940,8 +940,8 @@ class HubertModelIntegrationTest(unittest.TestCase):
)
expected_output_sum = -3776.0730
self.assertTrue(torch.allclose(outputs[:, :4, :4], expected_outputs_first, atol=5e-3))
self.assertTrue(torch.allclose(outputs[:, -4:, -4:], expected_outputs_last, atol=5e-3))
torch.testing.assert_close(outputs[:, :4, :4], expected_outputs_first, rtol=5e-3, atol=5e-3)
torch.testing.assert_close(outputs[:, -4:, -4:], expected_outputs_last, rtol=5e-3, atol=5e-3)
self.assertTrue(abs(outputs.sum() - expected_output_sum) < 0.1)
def test_inference_hubert_25hz(self):
@@ -977,6 +977,6 @@ class HubertModelIntegrationTest(unittest.TestCase):
)
expected_output_sum = 1681.7603
self.assertTrue(torch.allclose(outputs[:, :4, :4], expected_outputs_first, atol=5e-3))
self.assertTrue(torch.allclose(outputs[:, -4:, -4:], expected_outputs_last, atol=5e-3))
torch.testing.assert_close(outputs[:, :4, :4], expected_outputs_first, rtol=5e-3, atol=5e-3)
torch.testing.assert_close(outputs[:, -4:, -4:], expected_outputs_last, rtol=5e-3, atol=5e-3)
self.assertTrue(abs(outputs.sum() - expected_output_sum) < 0.1)