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a303fd2ea6
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/39087 Differential Revision: D22169697 Pulled By: anjali411 fbshipit-source-id: 4866b7be6742508cc40540ed1ac811f005531d8b
28 lines
1.1 KiB
Python
28 lines
1.1 KiB
Python
import torch
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from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes
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from torch.testing._internal.common_utils import TestCase, run_tests
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devices = (torch.device('cpu'), torch.device('cuda:0'))
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class TestComplexTensor(TestCase):
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@dtypes(*torch.testing.get_all_complex_dtypes())
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def test_to_list(self, device, dtype):
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# test that the complex float tensor has expected values and
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# there's no garbage value in the resultant list
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self.assertEqual(torch.zeros((2, 2), device=device, dtype=dtype).tolist(), [[0j, 0j], [0j, 0j]])
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@dtypes(torch.float32, torch.float64)
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def test_dtype_inference(self, device, dtype):
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# issue: https://github.com/pytorch/pytorch/issues/36834
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default_dtype = torch.get_default_dtype()
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torch.set_default_dtype(dtype)
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x = torch.tensor([3., 3. + 5.j], device=device)
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torch.set_default_dtype(default_dtype)
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self.assertEqual(x.dtype, torch.cdouble if dtype == torch.float64 else torch.cfloat)
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instantiate_device_type_tests(TestComplexTensor, globals())
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if __name__ == '__main__':
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run_tests()
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