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

2
tests/models/clvp/test_feature_extraction_clvp.py
View File

@@ -238,4 +238,4 @@ class ClvpFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.Tes
feature_extractor = ClvpFeatureExtractor.from_pretrained("susnato/clvp_dev")
input_features = feature_extractor(input_speech, sampling_rate=sr[0], return_tensors="pt").input_features
self.assertEqual(input_features.shape, (1, 80, 517))
self.assertTrue(torch.allclose(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, atol=1e-4))
torch.testing.assert_close(input_features[0, 0, :30], EXPECTED_INPUT_FEATURES, rtol=1e-4, atol=1e-4)

12
tests/models/clvp/test_modeling_clvp.py
View File

@@ -591,14 +591,14 @@ class ClvpIntegrationTest(unittest.TestCase):
[[-0.8582, 0.5228, 1.9944], [-0.0465, -1.1017, -0.0093], [-0.0466, -0.6030, -0.1280]]
)
self.assertTrue(torch.allclose(conditioning_encoder_outputs[0, :3, :3], EXPECTED_OUTPUTS, atol=1e-4))
torch.testing.assert_close(conditioning_encoder_outputs[0, :3, :3], EXPECTED_OUTPUTS, rtol=1e-4, atol=1e-4)
def test_decoder_model_generate(self):
autoregressive_model_output = self.model.speech_decoder_model.generate(input_ids=self.text_tokens).cpu()
EXPECTED_OUTPUTS = torch.tensor([[147, 2, 54, 2, 43, 2, 169, 122, 29, 64, 2, 136, 37, 33, 9, 8193]])
self.assertTrue(torch.allclose(autoregressive_model_output, EXPECTED_OUTPUTS))
torch.testing.assert_close(autoregressive_model_output, EXPECTED_OUTPUTS)
def test_text_and_speech_encoder_models(self):
# check for text embeds
@@ -608,7 +608,7 @@ class ClvpIntegrationTest(unittest.TestCase):
EXPECTED_TEXT_EMBEDS = torch.tensor([1.4798, -2.0005, 2.3902, -0.5042, 1.6401, -2.4135, -1.4800, 3.0118, -2.4422, 1.3266, 2.2339, 1.4761, -4.8983, -1.3592, 6.0251, 6.7364, 2.2576, 3.7229, -10.0436, 4.6676])
# fmt: on
self.assertTrue(torch.allclose(text_embeds[0, :20], EXPECTED_TEXT_EMBEDS, atol=1e-4))
torch.testing.assert_close(text_embeds[0, :20], EXPECTED_TEXT_EMBEDS, rtol=1e-4, atol=1e-4)
# check for speech embeds
speech_embeds = self.model.speech_encoder_model(input_ids=self.text_tokens, return_dict=True)[0].cpu()
@@ -617,7 +617,7 @@ class ClvpIntegrationTest(unittest.TestCase):
EXPECTED_SPEECH_EMBEDS = torch.tensor([3.1202, -3.1183, -1.4264, -6.1339, 1.8885, -0.1983, 0.9461, -1.7414, 0.3320, -3.8400, -1.5715, 1.5096, -1.7576, 0.2387, 4.9758, 5.8450, -6.2534, 2.8587, -5.5816, 4.7821])
# fmt: on
self.assertTrue(torch.allclose(speech_embeds[0, :20], EXPECTED_SPEECH_EMBEDS, atol=1e-4))
torch.testing.assert_close(speech_embeds[0, :20], EXPECTED_SPEECH_EMBEDS, rtol=1e-4, atol=1e-4)
def test_full_model_integration(self):
full_model_output = self.model.generate(
@@ -632,5 +632,5 @@ class ClvpIntegrationTest(unittest.TestCase):
EXPECTED_SPEECH_IDS = torch.tensor([[1953, 1080, 612], [1953, 612, 493], [1953, 612, 716]])
EXPECTED_SIMILARITY_SCORES = torch.tensor([[14.7660, 14.4569, 13.6472, 13.5683]])
self.assertTrue(torch.allclose(full_model_output.speech_ids.cpu()[-3:, -3:], EXPECTED_SPEECH_IDS))
self.assertTrue(torch.allclose(full_model_output.logits_per_text.cpu(), EXPECTED_SIMILARITY_SCORES))
torch.testing.assert_close(full_model_output.speech_ids.cpu()[-3:, -3:], EXPECTED_SPEECH_IDS)
torch.testing.assert_close(full_model_output.logits_per_text.cpu(), EXPECTED_SIMILARITY_SCORES)