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Clang-format tests, examples, libraries, benchmarks, etc.

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This commit is contained in:
Antonio Sánchez
2023-12-05 21:22:55 +00:00
committed by Rasmus Munk Larsen
parent 3252ecc7a4
commit 46e9cdb7fe
876 changed files with 33453 additions and 37795 deletions
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31
unsupported/test/cxx11_tensor_random_sycl.cpp
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@@ -20,9 +20,8 @@
#include <unsupported/Eigen/CXX11/Tensor>
template <typename DataType, int DataLayout, typename IndexType>
static void test_sycl_random_uniform(const Eigen::SyclDevice& sycl_device)
{
Tensor<DataType, 2,DataLayout, IndexType> out(72,97);
static void test_sycl_random_uniform(const Eigen::SyclDevice& sycl_device) {
Tensor<DataType, 2, DataLayout, IndexType> out(72, 97);
out.setZero();
std::size_t out_bytes = out.size() * sizeof(DataType);
@@ -32,11 +31,11 @@ static void test_sycl_random_uniform(const Eigen::SyclDevice& sycl_device)
array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}};
DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);
gpu_out.device(sycl_device)=gpu_out.random();
sycl_device.memcpyDeviceToHost(out.data(), d_out,out_bytes);
gpu_out.device(sycl_device) = gpu_out.random();
sycl_device.memcpyDeviceToHost(out.data(), d_out, out_bytes);
// For now we just check the code doesn't crash.
// TODO: come up with a valid test of randomness
@@ -44,9 +43,8 @@ static void test_sycl_random_uniform(const Eigen::SyclDevice& sycl_device)
}
template <typename DataType, int DataLayout, typename IndexType>
void test_sycl_random_normal(const Eigen::SyclDevice& sycl_device)
{
Tensor<DataType, 2,DataLayout,IndexType> out(72,97);
void test_sycl_random_normal(const Eigen::SyclDevice& sycl_device) {
Tensor<DataType, 2, DataLayout, IndexType> out(72, 97);
out.setZero();
std::size_t out_bytes = out.size() * sizeof(DataType);
@@ -55,29 +53,28 @@ void test_sycl_random_normal(const Eigen::SyclDevice& sycl_device)
array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}};
DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);
Eigen::internal::NormalRandomGenerator<DataType> gen(true);
gpu_out.device(sycl_device)=gpu_out.random(gen);
sycl_device.memcpyDeviceToHost(out.data(), d_out,out_bytes);
gpu_out.device(sycl_device) = gpu_out.random(gen);
sycl_device.memcpyDeviceToHost(out.data(), d_out, out_bytes);
// For now we just check the code doesn't crash.
// TODO: come up with a valid test of randomness
sycl_device.deallocate(d_out);
}
template<typename DataType, typename dev_Selector> void sycl_random_test_per_device(dev_Selector s){
template <typename DataType, typename dev_Selector>
void sycl_random_test_per_device(dev_Selector s) {
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_sycl_random_uniform<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_random_uniform<DataType, ColMajor, int64_t>(sycl_device);
test_sycl_random_normal<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_random_normal<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_random_sycl)
{
for (const auto& device :Eigen::get_sycl_supported_devices()) {
EIGEN_DECLARE_TEST(cxx11_tensor_random_sycl) {
for (const auto& device : Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_random_test_per_device<half>(device));
CALL_SUBTEST(sycl_random_test_per_device<float>(device));
#ifdef EIGEN_SYCL_DOUBLE_SUPPORT