mirror of
https://github.com/zebrajr/pytorch.git
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008ba18c5b
* Also pass torch includes to nvcc build * Export ATen/cuda headers with install * Refactor flags common to C++ and CUDA * Improve tests for C++/CUDA extensions * Export .cuh files under THC * Refactor and clean cpp_extension.py slightly * Include ATen in cuda extension test * Clarifying comment in cuda_extension.cu * Replace cuda_extension.cu with cuda_extension_kernel.cu in setup.py * Copy compile args in C++ extension and add second kernel * Conditionally add -std=c++11 to cuda_flags * Also export cuDNN headers * Add comment about deepcopy
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
import unittest
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import torch
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import torch.utils.cpp_extension
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import torch_test_cpp_extension as cpp_extension
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import common
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TEST_CUDA = torch.cuda.is_available()
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class TestCppExtension(common.TestCase):
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def test_extension_function(self):
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x = torch.randn(4, 4)
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y = torch.randn(4, 4)
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z = cpp_extension.sigmoid_add(x, y)
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self.assertEqual(z, x.sigmoid() + y.sigmoid())
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def test_extension_module(self):
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mm = cpp_extension.MatrixMultiplier(4, 8)
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weights = torch.rand(8, 4)
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expected = mm.get().mm(weights)
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result = mm.forward(weights)
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self.assertEqual(expected, result)
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def test_jit_compile_extension(self):
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module = torch.utils.cpp_extension.load(
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name='jit_extension',
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sources=[
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'cpp_extensions/jit_extension.cpp',
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'cpp_extensions/jit_extension2.cpp'
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],
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extra_include_paths=['cpp_extensions'],
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extra_cflags=['-g'],
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verbose=True)
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x = torch.randn(4, 4)
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y = torch.randn(4, 4)
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z = module.tanh_add(x, y)
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self.assertEqual(z, x.tanh() + y.tanh())
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# Checking we can call a method defined not in the main C++ file.
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z = module.exp_add(x, y)
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self.assertEqual(z, x.exp() + y.exp())
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# Checking we can use this JIT-compiled class.
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doubler = module.Doubler(2, 2)
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self.assertEqual(doubler.get().sum(), 4)
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self.assertEqual(doubler.forward().sum(), 8)
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@unittest.skipIf(not TEST_CUDA, "CUDA not found")
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def test_cuda_extension(self):
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import torch_test_cuda_extension as cuda_extension
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x = torch.FloatTensor(100).zero_().cuda()
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y = torch.FloatTensor(100).zero_().cuda()
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z = cuda_extension.sigmoid_add(x, y).cpu()
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# 2 * sigmoid(0) = 2 * 0.5 = 1
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self.assertEqual(z, torch.ones_like(z))
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@unittest.skipIf(not TEST_CUDA, "CUDA not found")
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def test_jit_cuda_extension(self):
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# NOTE: The name of the extension must equal the name of the module.
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module = torch.utils.cpp_extension.load(
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name='torch_test_cuda_extension',
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sources=[
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'cpp_extensions/cuda_extension.cpp',
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'cpp_extensions/cuda_extension.cu'
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],
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extra_cuda_cflags=['-O2'],
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verbose=True)
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x = torch.FloatTensor(100).zero_().cuda()
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y = torch.FloatTensor(100).zero_().cuda()
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z = module.sigmoid_add(x, y).cpu()
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# 2 * sigmoid(0) = 2 * 0.5 = 1
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self.assertEqual(z, torch.ones_like(z))
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if __name__ == '__main__':
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common.run_tests()
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