mirror of
https://github.com/zebrajr/pytorch.git
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825181ea9d
Summary: This PR is a large codemod to rewrite all C++ API tests with GoogleTest (gtest) instead of Catch. You can largely trust me to have correctly code-modded the tests, so it's not required to review every of the 2000+ changed lines. However, additional things I changed were: 1. Moved the cmake parts for these tests into their own `CMakeLists.txt` under `test/cpp/api` and calling `add_subdirectory` from `torch/CMakeLists.txt` 2. Fixing DataParallel tests which weren't being compiled because `USE_CUDA` wasn't correctly being set at all. 3. Updated README ezyang ebetica Pull Request resolved: https://github.com/pytorch/pytorch/pull/11953 Differential Revision: D9998883 Pulled By: goldsborough fbshipit-source-id: affe3f320b0ca63e7e0019926a59076bb943db80
139 lines
4.2 KiB
C++
139 lines
4.2 KiB
C++
#include <gtest/gtest.h>
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#include <torch/tensor.h>
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#include <ATen/Context.h>
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#include <ATen/Functions.h>
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#include <ATen/OptionsGuard.h>
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#include <ATen/core/TensorOptions.h>
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#include <string>
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#include <vector>
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using namespace at;
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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(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.type().scalarType(), (type_)); \
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ASSERT_TRUE(tensor.type().layout() == (layout_))
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TEST(TensorOptionsTest, DefaultsToTheRightValues) {
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TensorOptions options;
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
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}
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TEST(TensorOptionsTest, ReturnsTheCorrectType) {
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auto options = TensorOptions().device(kCPU).dtype(kInt).layout(kSparse);
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ASSERT_TRUE(
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at::getType(options) == getNonVariableType(Backend::SparseCPU, kInt));
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}
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TEST(TensorOptionsTest, UtilityFunctionsReturnTheRightTensorOptions) {
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auto options = dtype(kInt);
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REQUIRE_OPTIONS(kCPU, -1, kInt, kStrided);
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options = layout(kSparse);
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kSparse);
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options = device({kCUDA, 1});
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REQUIRE_OPTIONS(kCUDA, 1, kFloat, kStrided);
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options = device_index(1);
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REQUIRE_OPTIONS(kCUDA, 1, kFloat, kStrided);
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options = dtype(kByte).layout(kSparse).device({kCUDA, 2}).device_index(3);
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REQUIRE_OPTIONS(kCUDA, 3, kByte, kSparse);
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}
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TEST(TensorOptionsTest, ConstructsWellFromCPUTypes) {
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TensorOptions options;
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
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options = TensorOptions({kCPU, 0});
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REQUIRE_OPTIONS(kCPU, 0, kFloat, kStrided);
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options = TensorOptions(kInt);
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REQUIRE_OPTIONS(kCPU, -1, kInt, kStrided);
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options = TensorOptions(getNonVariableType(Backend::SparseCPU, kFloat));
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kSparse);
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options = TensorOptions(getNonVariableType(Backend::SparseCPU, kByte));
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REQUIRE_OPTIONS(kCPU, -1, kByte, kSparse);
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}
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TEST(TensorOptionsTest, ConstructsWellFromCPUTensors) {
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auto options = empty(5, kDouble).options();
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REQUIRE_OPTIONS(kCPU, -1, kDouble, kStrided);
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options = empty(5, getNonVariableType(Backend::SparseCPU, kByte)).options();
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REQUIRE_OPTIONS(kCPU, -1, kByte, kSparse);
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}
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TEST(TensorOptionsTest, ConstructsWellFromVariables) {
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auto options = torch::empty(5).options();
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
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ASSERT_FALSE(options.requires_grad());
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options = torch::empty(5, at::requires_grad()).options();
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REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
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ASSERT_FALSE(options.requires_grad());
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}
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TEST(TensorOptionsTest, OptionsGuard) {
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Tensor tensor;
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{
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OptionsGuard guard(TensorOptions{});
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tensor = at::empty({10});
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}
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REQUIRE_TENSOR_OPTIONS(kCPU, -1, kFloat, kStrided);
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{
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OptionsGuard guard(TensorOptions().dtype(kInt));
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tensor = at::empty({10});
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}
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REQUIRE_TENSOR_OPTIONS(kCPU, -1, kInt, kStrided);
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{
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OptionsGuard guard(TensorOptions().dtype(kInt).layout(kSparse));
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tensor = at::empty({10});
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}
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REQUIRE_TENSOR_OPTIONS(kCPU, -1, kInt, kSparse);
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{
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OptionsGuard guard(requires_grad(true));
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tensor = torch::empty({10});
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}
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REQUIRE_TENSOR_OPTIONS(kCPU, -1, kFloat, kStrided);
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ASSERT_TRUE(tensor.requires_grad());
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}
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TEST(DeviceTest, ParsesCorrectlyFromString) {
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Device device("cpu:0");
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ASSERT_EQ(device, Device(kCPU, 0));
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device = Device("cpu");
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ASSERT_EQ(device, Device(kCPU));
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device = Device("cuda:123");
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ASSERT_EQ(device, Device(kCUDA, 123));
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device = Device("cuda");
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ASSERT_EQ(device, Device(kCUDA));
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std::vector<std::string> badnesses = {
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"", "cud:1", "cuda:", "cpu::1", ":1", "3", "tpu:4", "??"};
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for (const auto& badness : badnesses) {
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ASSERT_ANY_THROW({ Device d(badness); });
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}
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}
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