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Fix vits low-precision dtype (#35418)

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* fix vits dtype

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* add tests

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* use weight dtype

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

---------

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>
This commit is contained in:
jiqing-feng
2025-01-21 00:19:31 +08:00
committed by GitHub
parent 729b569531
commit 641238eb76
2 changed files with 35 additions and 2 deletions
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32
tests/models/vits/test_modeling_vits.py
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@@ -27,6 +27,7 @@ from transformers.testing_utils import (
is_flaky,
is_torch_available,
require_torch,
require_torch_fp16,
require_torch_multi_gpu,
slow,
torch_device,
@@ -434,3 +435,34 @@ class VitsModelIntegrationTests(unittest.TestCase):
)
# fmt: on
self.assertTrue(torch.allclose(outputs.waveform[0, 10000:10030].cpu(), EXPECTED_LOGITS, atol=1e-4))
@require_torch_fp16
def test_forward_fp16(self):
# GPU gives different results than CPU
torch_device = "cpu"
model = VitsModel.from_pretrained("facebook/mms-tts-eng", torch_dtype=torch.float16)
model.to(torch_device)
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng")
set_seed(555) # make deterministic
input_text = "Mister quilter is the apostle of the middle classes and we are glad to welcome his gospel!"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(torch_device)
with torch.no_grad():
outputs = model(input_ids)
self.assertEqual(outputs.waveform.shape, (1, 87040))
# fmt: off
EXPECTED_LOGITS = torch.tensor(
[
0.0101, 0.0318, 0.0489, 0.0627, 0.0728, 0.0865, 0.1053, 0.1279,
0.1514, 0.1703, 0.1827, 0.1829, 0.1694, 0.1509, 0.1332, 0.1188,
0.1066, 0.0978, 0.0936, 0.0867, 0.0724, 0.0493, 0.0197, -0.0141,
-0.0501, -0.0817, -0.1065, -0.1223, -0.1311, -0.1339
]
).to(torch.float16)
# fmt: on
self.assertTrue(torch.allclose(outputs.waveform[0, 10000:10030].cpu(), EXPECTED_LOGITS, atol=1e-4))