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

CLVP Fixes (#27547)

Browse Source 
* fixes

* more fixes

* style fix

* more fix

* comments
This commit is contained in:
Susnato Dhar
2023-11-28 22:10:01 +05:30
committed by GitHub
parent 30e92ea323
commit dfbd209c25
2 changed files with 106 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
tests/models/clvp/test_modeling_clvp.py
View File

@@ -604,12 +604,7 @@ class ClvpIntegrationTest(unittest.TestCase):
text_embeds = self.model.text_encoder_model(input_ids=self.text_tokens, return_dict=True)[0].cpu()
# fmt: off
EXPECTED_TEXT_EMBEDS = torch.tensor(
[ 1.8060e+00, -2.7928e+00, 3.2021e+00, -1.5673e+00, 2.3284e+00, -3.2065e+00, -1.3368e+00, 2.2322e+00,
-1.7667e+00, 4.1505e-01, 2.4119e+00, -5.8133e-03, -4.6367e+00, 1.6450e-01, 6.7459e+00, 6.6292e+00,
1.1046e+00, 3.6196e+00, -1.0496e+01, 5.4924e+00
]
)
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))
@@ -618,11 +613,7 @@ class ClvpIntegrationTest(unittest.TestCase):
speech_embeds = self.model.speech_encoder_model(input_ids=self.text_tokens, return_dict=True)[0].cpu()
# fmt: off
EXPECTED_SPEECH_EMBEDS = torch.tensor(
[ 4.6143, -5.5784, 0.8983, -3.9665, -0.6714, -1.0665, -1.1277, 1.5619, 2.6322, -7.2008, -2.4932, 0.3265,
-1.4738, 0.1425, 5.0825, 4.1760, -5.4708, 2.1935, -6.0044, 3.9540
]
)
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))
@@ -635,8 +626,10 @@ class ClvpIntegrationTest(unittest.TestCase):
num_beams=4,
num_return_sequences=4,
max_new_tokens=10,
).speech_ids.cpu()
)
EXPECTED_OUTPUTS = torch.tensor([[1953, 1080, 612], [1953, 1953, 612], [1953, 612, 716]])
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[-3:, -3:], EXPECTED_OUTPUTS))
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))