pytorch/test/cpp/api/misc.cpp
Nikita Shulga a9b0a921d5 Disable avoid-non-const-global-variables lint check (#62008)
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`

All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`;  do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008

Reviewed By: driazati, r-barnes

Differential Revision: D29838584

Pulled By: malfet

fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
2021-07-22 18:04:40 -07:00

93 lines
2.0 KiB
C++

#include <gtest/gtest.h>
#include <torch/torch.h>
#include <test/cpp/api/support.h>
#include <functional>
using namespace torch::test;
void torch_warn_once_A() {
TORCH_WARN_ONCE("warn once");
}
void torch_warn_once_B() {
TORCH_WARN_ONCE("warn something else once");
}
void torch_warn() {
TORCH_WARN("warn multiple times");
}
TEST(UtilsTest, WarnOnce) {
{
WarningCapture warnings;
torch_warn_once_A();
torch_warn_once_A();
torch_warn_once_B();
torch_warn_once_B();
ASSERT_EQ(count_substr_occurrences(warnings.str(), "warn once"), 1);
ASSERT_EQ(
count_substr_occurrences(warnings.str(), "warn something else once"),
1);
}
{
WarningCapture warnings;
torch_warn();
torch_warn();
torch_warn();
ASSERT_EQ(
count_substr_occurrences(warnings.str(), "warn multiple times"), 3);
}
}
TEST(NoGradTest, SetsGradModeCorrectly) {
torch::manual_seed(0);
torch::NoGradGuard guard;
torch::nn::Linear model(5, 2);
auto x = torch::randn({10, 5}, torch::requires_grad());
auto y = model->forward(x);
torch::Tensor s = y.sum();
// Mimicking python API behavior:
ASSERT_THROWS_WITH(s.backward(),
"element 0 of tensors does not require grad and does not have a grad_fn")
}
struct AutogradTest : torch::test::SeedingFixture {
AutogradTest() {
x = torch::randn({3, 3}, torch::requires_grad());
y = torch::randn({3, 3});
z = x * y;
}
torch::Tensor x, y, z;
};
TEST_F(AutogradTest, CanTakeDerivatives) {
z.backward(torch::ones_like(z));
ASSERT_TRUE(x.grad().allclose(y));
}
TEST_F(AutogradTest, CanTakeDerivativesOfZeroDimTensors) {
z.sum().backward();
ASSERT_TRUE(x.grad().allclose(y));
}
TEST_F(AutogradTest, CanPassCustomGradientInputs) {
z.sum().backward(torch::ones({}) * 2);
ASSERT_TRUE(x.grad().allclose(y * 2));
}
TEST(UtilsTest, AmbiguousOperatorDefaults) {
auto tmp = at::empty({}, at::kCPU);
at::_test_ambiguous_defaults(tmp);
at::_test_ambiguous_defaults(tmp, 1);
at::_test_ambiguous_defaults(tmp, 1, 1);
at::_test_ambiguous_defaults(tmp, 2, "2");
}