pytorch/test/cpp/api/tensor_options.cpp
Wanchao Liang 4e1c64caee Add c10::optional to type syntax (#12582)
Summary:
This PR adds optional type to ATen native, autograd, JIT schema and Python Arg parser, closes #9513. It allows us to use optional default values (including None) for function signature and implementations like clamp, etc., and also let us remove the python_default_init hack.

Follow up:

remove python_default_init completely.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12582

Differential Revision: D10417423

Pulled By: wanchaol

fbshipit-source-id: 1c80f0727bb528188b47c595629e2996be269b89
2018-10-25 16:08:29 -07:00

157 lines
4.7 KiB
C++

#include <gtest/gtest.h>
#include <torch/tensor.h>
#include <ATen/Context.h>
#include <ATen/Functions.h>
#include <ATen/OptionsGuard.h>
#include <ATen/core/TensorOptions.h>
#include <string>
#include <vector>
using namespace at;
// A macro so we don't lose location information when an assertion fails.
#define REQUIRE_OPTIONS(device_, index_, type_, layout_) \
ASSERT_EQ(options.device().type(), Device((device_), (index_)).type()); \
ASSERT_TRUE( \
options.device().index() == Device((device_), (index_)).index()); \
ASSERT_EQ(options.dtype(), (type_)); \
ASSERT_TRUE(options.layout() == (layout_))
#define REQUIRE_TENSOR_OPTIONS(device_, index_, type_, layout_) \
ASSERT_EQ(tensor.device().type(), Device((device_), (index_)).type()); \
ASSERT_EQ(tensor.device().index(), Device((device_), (index_)).index()); \
ASSERT_EQ(tensor.type().scalarType(), (type_)); \
ASSERT_TRUE(tensor.type().layout() == (layout_))
TEST(TensorOptionsTest, DefaultsToTheRightValues) {
TensorOptions options;
REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
}
TEST(TensorOptionsTest, ReturnsTheCorrectType) {
auto options = TensorOptions().device(kCPU).dtype(kInt).layout(kSparse);
ASSERT_TRUE(
at::getType(options) == getNonVariableType(Backend::SparseCPU, kInt));
}
TEST(TensorOptionsTest, UtilityFunctionsReturnTheRightTensorOptions) {
auto options = dtype(kInt);
REQUIRE_OPTIONS(kCPU, -1, kInt, kStrided);
options = layout(kSparse);
REQUIRE_OPTIONS(kCPU, -1, kFloat, kSparse);
options = device({kCUDA, 1});
REQUIRE_OPTIONS(kCUDA, 1, kFloat, kStrided);
options = device_index(1);
REQUIRE_OPTIONS(kCUDA, 1, kFloat, kStrided);
options = dtype(kByte).layout(kSparse).device(kCUDA, 2).device_index(3);
REQUIRE_OPTIONS(kCUDA, 3, kByte, kSparse);
}
TEST(TensorOptionsTest, ConstructsWellFromCPUTypes) {
TensorOptions options;
REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
options = TensorOptions({kCPU, 0});
REQUIRE_OPTIONS(kCPU, 0, kFloat, kStrided);
options = TensorOptions(kInt);
REQUIRE_OPTIONS(kCPU, -1, kInt, kStrided);
options = TensorOptions(getNonVariableType(Backend::SparseCPU, kFloat));
REQUIRE_OPTIONS(kCPU, -1, kFloat, kSparse);
options = TensorOptions(getNonVariableType(Backend::SparseCPU, kByte));
REQUIRE_OPTIONS(kCPU, -1, kByte, kSparse);
}
TEST(TensorOptionsTest, ConstructsWellFromCPUTensors) {
auto options = empty(5, kDouble).options();
REQUIRE_OPTIONS(kCPU, -1, kDouble, kStrided);
options = empty(5, getNonVariableType(Backend::SparseCPU, kByte)).options();
REQUIRE_OPTIONS(kCPU, -1, kByte, kSparse);
}
TEST(TensorOptionsTest, ConstructsWellFromVariables) {
auto options = torch::empty(5).options();
REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
ASSERT_FALSE(options.requires_grad());
options = torch::empty(5, at::requires_grad()).options();
REQUIRE_OPTIONS(kCPU, -1, kFloat, kStrided);
ASSERT_FALSE(options.requires_grad());
}
TEST(TensorOptionsTest, OptionsGuard) {
Tensor tensor;
{
OptionsGuard guard(TensorOptions{});
tensor = at::empty({10});
}
REQUIRE_TENSOR_OPTIONS(kCPU, -1, kFloat, kStrided);
{
OptionsGuard guard(TensorOptions().dtype(kInt));
tensor = at::empty({10});
}
REQUIRE_TENSOR_OPTIONS(kCPU, -1, kInt, kStrided);
{
OptionsGuard guard(TensorOptions().dtype(kInt).layout(kSparse));
tensor = at::empty({10});
}
REQUIRE_TENSOR_OPTIONS(kCPU, -1, kInt, kSparse);
{
OptionsGuard guard(requires_grad(true));
tensor = torch::empty({10});
}
REQUIRE_TENSOR_OPTIONS(kCPU, -1, kFloat, kStrided);
ASSERT_TRUE(tensor.requires_grad());
}
TEST(DeviceTest, ParsesCorrectlyFromString) {
Device device("cpu:0");
ASSERT_EQ(device, Device(DeviceType::CPU, 0));
device = Device("cpu");
ASSERT_EQ(device, Device(DeviceType::CPU));
device = Device("cuda:123");
ASSERT_EQ(device, Device(DeviceType::CUDA, 123));
device = Device("cuda");
ASSERT_EQ(device, Device(DeviceType::CUDA));
device = Device("mkldnn");
ASSERT_EQ(device, Device(DeviceType::MKLDNN));
device = Device("opengl");
ASSERT_EQ(device, Device(DeviceType::OPENGL));
device = Device("opencl");
ASSERT_EQ(device, Device(DeviceType::OPENCL));
device = Device("ideep");
ASSERT_EQ(device, Device(DeviceType::IDEEP));
device = Device("hip");
ASSERT_EQ(device, Device(DeviceType::HIP));
device = Device("hip:321");
ASSERT_EQ(device, Device(DeviceType::HIP, 321));
std::vector<std::string> badnesses = {
"", "cud:1", "cuda:", "cpu::1", ":1", "3", "tpu:4", "??"};
for (const auto& badness : badnesses) {
ASSERT_ANY_THROW({ Device d(badness); });
}
}