[RoBERTa] RobertaForSequenceClassification + conversion
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@@ -179,5 +179,63 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
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shutil.rmtree(cache_dir)
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self.assertIsNotNone(model)
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class RobertaModelIntegrationTest(unittest.TestCase):
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@pytest.mark.slow
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def test_inference_masked_lm(self):
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model = RobertaForMaskedLM.from_pretrained('roberta-base')
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input_ids = torch.tensor([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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expected_shape = torch.Size((1, 11, 50265))
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self.assertEqual(
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output.shape,
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expected_shape
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)
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# compare the actual values for a slice.
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expected_slice = torch.Tensor(
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[[[33.8843, -4.3107, 22.7779],
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[ 4.6533, -2.8099, 13.6252],
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[ 1.8222, -3.6898, 8.8600]]]
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)
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self.assertTrue(
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torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
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)
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@pytest.mark.slow
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def test_inference_no_head(self):
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model = RobertaModel.from_pretrained('roberta-base')
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input_ids = torch.tensor([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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# compare the actual values for a slice.
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expected_slice = torch.Tensor(
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[[[-0.0231, 0.0782, 0.0074],
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[-0.1854, 0.0539, -0.0174],
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[ 0.0548, 0.0799, 0.1687]]]
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)
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self.assertTrue(
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torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)
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)
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@pytest.mark.slow
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def test_inference_classification_head(self):
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model = RobertaForSequenceClassification.from_pretrained('roberta-large-mnli')
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input_ids = torch.tensor([[ 0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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expected_shape = torch.Size((1, 3))
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self.assertEqual(
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output.shape,
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expected_shape
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)
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expected_tensor = torch.Tensor([[-0.9469, 0.3913, 0.5118]])
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self.assertTrue(
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torch.allclose(output, expected_tensor, atol=1e-3)
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)
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if __name__ == "__main__":
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unittest.main()
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