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#include <gtest/gtest.h>
#include <ATen/ATen.h>
#include <ATen/Context.h>
#include <torch/torch.h>
#include <cmath>
#define REQUIRE_TENSOR_OPTIONS(device_, index_, type_, layout_) \
ASSERT_TRUE( \
tensor.device().type() == at::Device((device_), (index_)).type()); \
ASSERT_TRUE( \
tensor.device().index() == at::Device((device_), (index_)).index()); \
ASSERT_EQ(tensor.dtype(), (type_)); \
ASSERT_TRUE(tensor.layout() == (layout_))
TEST(TensorTest, AllocatesTensorOnTheCorrectDevice_MultiCUDA) {
auto tensor = at::tensor({1, 2, 3}, at::device({at::kCUDA, 1}));
ASSERT_EQ(tensor.device().type(), at::Device::Type::CUDA);
ASSERT_EQ(tensor.device().index(), 1);
}
TEST(TensorTest, ToDevice_MultiCUDA) {
auto tensor = at::empty({3, 4});
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
tensor = tensor.to({at::kCUDA, 1});
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
tensor = tensor.to({at::kCUDA, 0});
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kFloat, at::kStrided);
tensor = tensor.to({at::kCUDA, 1});
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
tensor = tensor.to(at::Device(at::kCPU));
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
tensor = tensor.to(at::kCUDA);
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kFloat, at::kStrided);
tensor = tensor.to(at::TensorOptions({at::kCUDA, 1}));
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kFloat, at::kStrided);
tensor = tensor.to(at::TensorOptions({at::kCUDA, 0}));
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kFloat, at::kStrided);
tensor = tensor.to(at::TensorOptions(at::kDouble));
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kDouble, at::kStrided);
tensor = tensor.to(at::TensorOptions({at::kCUDA, 1}));
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kDouble, at::kStrided);
tensor = tensor.to(at::TensorOptions(at::kInt));
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kInt, at::kStrided);
tensor = tensor.to(at::TensorOptions(at::Device(at::kCPU)));
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
tensor = tensor.to(at::TensorOptions(at::kCUDA));
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kInt, at::kStrided);
}
TEST(TensorTest, ToTensorAndTensorAttributes_MultiCUDA) {
auto tensor = at::empty({3, 4});
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
auto other = at::empty({3, 4}, at::kFloat);
tensor = tensor.to(other);
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
other = at::empty({3, 4}, at::TensorOptions(at::kCUDA).dtype(at::kDouble));
tensor = tensor.to(other.dtype());
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kDouble, at::kStrided);
tensor = tensor.to(other.device());
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kDouble, at::kStrided);
other = at::empty({3, 4}, at::TensorOptions({at::kCUDA, 1}).dtype(at::kLong));
tensor = tensor.to(other.device(), other.dtype());
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kLong, at::kStrided);
other = at::empty({3, 4}, at::kFloat);
tensor = tensor.to(other.options());
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
}
TEST(TensorTest, ToDoesNotCopyWhenOptionsAreAllTheSame_CUDA) {
auto tensor = at::empty(
{3, 4}, at::TensorOptions(at::kFloat).device(at::Device("cuda")));
auto hopefully_not_copy = tensor.to(tensor.options());
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
hopefully_not_copy = tensor.to(at::kFloat);
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
hopefully_not_copy = tensor.to("cuda");
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
hopefully_not_copy = tensor.to(at::TensorOptions("cuda"));
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
hopefully_not_copy = tensor.to(at::TensorOptions(at::kFloat));
ASSERT_EQ(hopefully_not_copy.data_ptr<float>(), tensor.data_ptr<float>());
}
TEST(TensorTest, ToDeviceAndDtype_MultiCUDA) {
auto tensor = at::empty({3, 4});
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kFloat, at::kStrided);
tensor = tensor.to({at::kCUDA, 1}, at::kInt);
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kInt, at::kStrided);
tensor = tensor.to(at::TensorOptions({at::kCUDA, 0}).dtype(at::kLong));
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 0, at::kLong, at::kStrided);
tensor = tensor.to(at::TensorOptions({at::kCUDA, 1}).dtype(at::kDouble));
REQUIRE_TENSOR_OPTIONS(at::kCUDA, 1, at::kDouble, at::kStrided);
tensor = tensor.to(at::kCPU, at::kInt);
REQUIRE_TENSOR_OPTIONS(at::kCPU, -1, at::kInt, at::kStrided);
}
TEST(TensorTest, MagmaInitializesCorrectly_CUDA) {
// Any tensor will work here as long as it's invertible
// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
float data[] = {1, 1, 1, 0, 0, 3, 1, 2, 2, 3, 1, 0, 1, 0, 2, 1};
auto tensor =
at::from_blob(data, {4, 4}, at::TensorOptions(at::kFloat)).cuda();
if (at::hasMAGMA()) {
at::inverse(tensor);
}
}
#ifdef USE_CUDA
#include <ATen/cuda/CUDAConfig.h>
#if AT_CUDNN_ENABLED()
TEST(CuDNNBatchNormTest, OutVariantMatchesFunctional) {
if (!torch::cuda::is_available()) {
GTEST_SKIP() << "CUDA is not available";
}
if (!at::Context::hasCuDNN()) {
GTEST_SKIP() << "cuDNN is not available";
}
auto device = torch::device(torch::kCUDA);
auto input = torch::rand({2, 3, 4, 4}, device);
auto weight = torch::randn({3}, device);
auto bias = torch::randn({3}, device);
auto running_mean = torch::zeros({3}, device);
auto running_var = torch::ones({3}, device);
bool training = true;
double exponential_average_factor = 0.1;
double epsilon = 1e-5;
auto output = torch::empty_like(input);
auto save_mean = torch::empty({3}, device);
auto save_var = torch::empty({3}, device);
auto reserve = torch::empty({0}, device.dtype(torch::kByte));
at::native::cudnn_batch_norm_out(
input,
weight,
bias,
running_mean,
running_var,
training,
exponential_average_factor,
epsilon,
output,
save_mean,
save_var,
reserve);
auto ref_outputs = at::native::cudnn_batch_norm(
input,
weight,
bias,
running_mean,
running_var,
training,
exponential_average_factor,
epsilon);
ASSERT_TRUE(torch::allclose(output, std::get<0>(ref_outputs)));
ASSERT_TRUE(torch::allclose(save_mean, std::get<1>(ref_outputs)));
ASSERT_TRUE(torch::allclose(save_var, std::get<2>(ref_outputs)));
ASSERT_TRUE(torch::equal(reserve, std::get<3>(ref_outputs)));
}
#endif // AT_CUDNN_ENABLED()
#endif // USE_CUDA
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