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3a66a1cb99
Summary: Add cppcoreguidelines-avoid-magic-numbers exclusion to clang-tidy Remove existing nolint warnings using following script: ``` for file in `git ls-files | grep -v \.py`; do gsed '/^ *\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)/d' -i $file; done ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/57841 Reviewed By: samestep Differential Revision: D28295045 Pulled By: malfet fbshipit-source-id: 7c6e8d1213c9593f169ed3df6a916498f1a97163
83 lines
3.0 KiB
C++
83 lines
3.0 KiB
C++
#include <gtest/gtest.h>
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#include <torch/torch.h>
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#include <torch/cuda.h>
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// NB: This file is compiled even in CPU build (for some reason), so
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// make sure you don't include any CUDA only headers.
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using namespace at;
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// TODO: This might be generally helpful aliases elsewhere.
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at::Device CPUDevice() {
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return at::Device(at::kCPU);
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}
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at::Device CUDADevice(DeviceIndex index) {
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return at::Device(at::kCUDA, index);
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}
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// A macro so we don't lose location information when an assertion fails.
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#define REQUIRE_OPTIONS(device_, index_, type_, layout_) \
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ASSERT_EQ(options.device().type(), Device((device_), (index_)).type()); \
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ASSERT_TRUE( \
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options.device().index() == Device((device_), (index_)).index()); \
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ASSERT_EQ(typeMetaToScalarType(options.dtype()), (type_)); \
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ASSERT_TRUE(options.layout() == (layout_))
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#define REQUIRE_TENSOR_OPTIONS(device_, index_, type_, layout_) \
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ASSERT_EQ(tensor.device().type(), Device((device_), (index_)).type()); \
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ASSERT_EQ(tensor.device().index(), Device((device_), (index_)).index()); \
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ASSERT_EQ(tensor.scalar_type(), (type_)); \
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ASSERT_TRUE(tensor.options().layout() == (layout_))
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorOptionsTest, ConstructsWellFromCUDATypes_CUDA) {
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auto options = CUDA(kFloat).options();
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REQUIRE_OPTIONS(kCUDA, -1, kFloat, kStrided);
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options = CUDA(kInt).options();
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REQUIRE_OPTIONS(kCUDA, -1, kInt, kStrided);
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options = getDeprecatedTypeProperties(Backend::SparseCUDA, kFloat).options();
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REQUIRE_OPTIONS(kCUDA, -1, kFloat, kSparse);
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options = getDeprecatedTypeProperties(Backend::SparseCUDA, kByte).options();
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REQUIRE_OPTIONS(kCUDA, -1, kByte, kSparse);
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// NOLINTNEXTLINE(bugprone-argument-comment,cppcoreguidelines-avoid-magic-numbers)
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options = CUDA(kFloat).options(/*device=*/5);
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REQUIRE_OPTIONS(kCUDA, 5, kFloat, kStrided);
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options =
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// NOLINTNEXTLINE(bugprone-argument-comment,cppcoreguidelines-avoid-magic-numbers)
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getDeprecatedTypeProperties(Backend::SparseCUDA, kFloat).options(/*device=*/5);
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REQUIRE_OPTIONS(kCUDA, 5, kFloat, kSparse);
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}
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// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
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TEST(TensorOptionsTest, ConstructsWellFromCUDATensors_MultiCUDA) {
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auto options = empty(5, device(kCUDA).dtype(kDouble)).options();
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REQUIRE_OPTIONS(kCUDA, 0, kDouble, kStrided);
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options = empty(5, getDeprecatedTypeProperties(Backend::SparseCUDA, kByte)).options();
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REQUIRE_OPTIONS(kCUDA, 0, kByte, kSparse);
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if (torch::cuda::device_count() > 1) {
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Tensor tensor;
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{
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DeviceGuard guard(CUDADevice(1));
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tensor = empty(5, device(kCUDA));
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}
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options = tensor.options();
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REQUIRE_OPTIONS(kCUDA, 1, kFloat, kStrided);
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{
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DeviceGuard guard(CUDADevice(1));
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tensor = empty(5, device(kCUDA).layout(kSparse));
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}
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options = tensor.options();
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REQUIRE_OPTIONS(kCUDA, 1, kFloat, kSparse);
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}
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}
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