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Add missing atol to torch.testing.assert_close where rtol is specified (#36234)

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ivarflakstad
2025-02-17 14:57:50 +01:00
committed by GitHub
parent dad513e0c2
commit 7ec35bc3bd
4 changed files with 4 additions and 4 deletions
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2
tests/models/informer/test_modeling_informer.py
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@@ -546,4 +546,4 @@ class InformerModelIntegrationTests(unittest.TestCase):
expected_slice = torch.tensor([3400.8005, 4289.2637, 7101.9209], device=torch_device) expected_slice = torch.tensor([3400.8005, 4289.2637, 7101.9209], device=torch_device)
mean_prediction = outputs.sequences.mean(dim=1) mean_prediction = outputs.sequences.mean(dim=1)
torch.testing.assert_close(mean_prediction[0, -3:], expected_slice, rtol=1e-1) torch.testing.assert_close(mean_prediction[0, -3:], expected_slice, rtol=1e-1, atol=1e-1)

2
tests/models/patchtst/test_modeling_patchtst.py
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@@ -385,4 +385,4 @@ class PatchTSTModelIntegrationTests(unittest.TestCase):
device=torch_device, device=torch_device,
) )
mean_prediction = outputs.sequences.mean(dim=1) mean_prediction = outputs.sequences.mean(dim=1)
torch.testing.assert_close(mean_prediction[-5:], expected_slice, rtol=TOLERANCE) torch.testing.assert_close(mean_prediction[-5:], expected_slice, rtol=TOLERANCE, atol=TOLERANCE)

2
tests/models/time_series_transformer/test_modeling_time_series_transformer.py
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@@ -554,4 +554,4 @@ class TimeSeriesTransformerModelIntegrationTests(unittest.TestCase):
expected_slice = torch.tensor([2825.2749, 3584.9207, 6763.9951], device=torch_device) expected_slice = torch.tensor([2825.2749, 3584.9207, 6763.9951], device=torch_device)
mean_prediction = outputs.sequences.mean(dim=1) mean_prediction = outputs.sequences.mean(dim=1)
torch.testing.assert_close(mean_prediction[0, -3:], expected_slice, rtol=1e-1) torch.testing.assert_close(mean_prediction[0, -3:], expected_slice, rtol=1e-1, atol=1e-1)

2
tests/models/wavlm/test_modeling_wavlm.py
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@@ -549,7 +549,7 @@ class WavLMModelIntegrationTest(unittest.TestCase):
[[[0.2122, 0.0500], [0.2118, 0.0563]], [[0.1353, 0.1818], [0.2453, 0.0595]]] [[[0.2122, 0.0500], [0.2118, 0.0563]], [[0.1353, 0.1818], [0.2453, 0.0595]]]
) )
torch.testing.assert_close(hidden_states_slice, EXPECTED_HIDDEN_STATES_SLICE, rtol=5e-2) torch.testing.assert_close(hidden_states_slice, EXPECTED_HIDDEN_STATES_SLICE, rtol=5e-2, atol=5e-2)
def test_inference_diarization(self): def test_inference_diarization(self):
model = WavLMForAudioFrameClassification.from_pretrained("microsoft/wavlm-base-plus-sd").to(torch_device) model = WavLMForAudioFrameClassification.from_pretrained("microsoft/wavlm-base-plus-sd").to(torch_device)