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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/63554 Following https://github.com/pytorch/pytorch/pull/61840#issuecomment-884087809, this deprecates all the dtype getters publicly exposed in the `torch.testing` namespace. The reason for this twofold: 1. If someone is not familiar with the C++ dispatch macros PyTorch uses, the names are misleading. For example `torch.testing.floating_types()` will only give you `float32` and `float64` skipping `float16` and `bfloat16`. 2. The dtype getters provide very minimal functionality that can be easily emulated by downstream libraries. We thought about [providing an replacement](https://gist.github.com/pmeier/3dfd2e105842ad0de4505068a1a0270a), but ultimately decided against it. The major problem is BC: by keeping it, either the namespace is getting messy again after a new dtype is added or we need to somehow version the return values of the getters. Test Plan: Imported from OSS Reviewed By: H-Huang Differential Revision: D30662206 Pulled By: mruberry fbshipit-source-id: a2bdb10ab02ae665df1b5b76e8afa9af043bbf56
28 lines
1.2 KiB
Python
28 lines
1.2 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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from torch.testing._internal.common_dtype import get_all_complex_dtypes
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devices = (torch.device('cpu'), torch.device('cuda:0'))
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class TestComplexTensor(TestCase):
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@dtypes(*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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