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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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74
unsupported/test/cxx11_tensor_padding_sycl.cpp
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@@ -12,14 +12,12 @@
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
@@ -28,11 +26,8 @@ using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
template<typename DataType, int DataLayout, typename IndexType>
static void test_simple_padding(const Eigen::SyclDevice& sycl_device)
{
template <typename DataType, int DataLayout, typename IndexType>
static void test_simple_padding(const Eigen::SyclDevice& sycl_device) {
IndexType sizeDim1 = 2;
IndexType sizeDim2 = 3;
IndexType sizeDim3 = 5;
@@ -56,27 +51,26 @@ static void test_simple_padding(const Eigen::SyclDevice& sycl_device)
Tensor<DataType, 4, DataLayout, IndexType> padded(padedtensorRange);
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(padded.size() * sizeof(DataType)));
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu2(gpu_data2, padedtensorRange);
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(padded.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu2(gpu_data2, padedtensorRange);
VERIFY_IS_EQUAL(padded.dimension(0), 2+0);
VERIFY_IS_EQUAL(padded.dimension(1), 3+3);
VERIFY_IS_EQUAL(padded.dimension(2), 5+7);
VERIFY_IS_EQUAL(padded.dimension(3), 7+0);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
gpu2.device(sycl_device)=gpu1.pad(paddings);
sycl_device.memcpyDeviceToHost(padded.data(), gpu_data2,(padded.size())*sizeof(DataType));
VERIFY_IS_EQUAL(padded.dimension(0), 2 + 0);
VERIFY_IS_EQUAL(padded.dimension(1), 3 + 3);
VERIFY_IS_EQUAL(padded.dimension(2), 5 + 7);
VERIFY_IS_EQUAL(padded.dimension(3), 7 + 0);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
gpu2.device(sycl_device) = gpu1.pad(paddings);
sycl_device.memcpyDeviceToHost(padded.data(), gpu_data2, (padded.size()) * sizeof(DataType));
for (IndexType i = 0; i < padedSizeDim1; ++i) {
for (IndexType j = 0; j < padedSizeDim2; ++j) {
for (IndexType k = 0; k < padedSizeDim3; ++k) {
for (IndexType l = 0; l < padedSizeDim4; ++l) {
if (j >= 2 && j < 5 && k >= 3 && k < 8) {
VERIFY_IS_EQUAL(padded(i,j,k,l), tensor(i,j-2,k-3,l));
VERIFY_IS_EQUAL(padded(i, j, k, l), tensor(i, j - 2, k - 3, l));
} else {
VERIFY_IS_EQUAL(padded(i,j,k,l), 0.0f);
VERIFY_IS_EQUAL(padded(i, j, k, l), 0.0f);
}
}
}
@@ -86,9 +80,8 @@ static void test_simple_padding(const Eigen::SyclDevice& sycl_device)
sycl_device.deallocate(gpu_data2);
}
template<typename DataType, int DataLayout, typename IndexType>
static void test_padded_expr(const Eigen::SyclDevice& sycl_device)
{
template <typename DataType, int DataLayout, typename IndexType>
static void test_padded_expr(const Eigen::SyclDevice& sycl_device) {
IndexType sizeDim1 = 2;
IndexType sizeDim2 = 3;
IndexType sizeDim3 = 5;
@@ -108,27 +101,25 @@ static void test_padded_expr(const Eigen::SyclDevice& sycl_device)
reshape_dims[0] = 12;
reshape_dims[1] = 84;
Tensor<DataType, 2, DataLayout, IndexType> result(reshape_dims);
Tensor<DataType, 2, DataLayout, IndexType> result(reshape_dims);
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size() * sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(result.size() * sizeof(DataType)));
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu2(gpu_data2, reshape_dims);
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(result.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 2,DataLayout,IndexType>> gpu2(gpu_data2, reshape_dims);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
gpu2.device(sycl_device)=gpu1.pad(paddings).reshape(reshape_dims);
sycl_device.memcpyDeviceToHost(result.data(), gpu_data2,(result.size())*sizeof(DataType));
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), (tensor.size()) * sizeof(DataType));
gpu2.device(sycl_device) = gpu1.pad(paddings).reshape(reshape_dims);
sycl_device.memcpyDeviceToHost(result.data(), gpu_data2, (result.size()) * sizeof(DataType));
for (IndexType i = 0; i < 2; ++i) {
for (IndexType j = 0; j < 6; ++j) {
for (IndexType k = 0; k < 12; ++k) {
for (IndexType l = 0; l < 7; ++l) {
const float result_value = DataLayout == ColMajor ?
result(i+2*j,k+12*l) : result(j+6*i,l+7*k);
const float result_value =
DataLayout == ColMajor ? result(i + 2 * j, k + 12 * l) : result(j + 6 * i, l + 7 * k);
if (j >= 2 && j < 5 && k >= 3 && k < 8) {
VERIFY_IS_EQUAL(result_value, tensor(i,j-2,k-3,l));
VERIFY_IS_EQUAL(result_value, tensor(i, j - 2, k - 3, l));
} else {
VERIFY_IS_EQUAL(result_value, 0.0f);
}
@@ -140,18 +131,17 @@ static void test_padded_expr(const Eigen::SyclDevice& sycl_device)
sycl_device.deallocate(gpu_data2);
}
template<typename DataType, typename dev_Selector> void sycl_padding_test_per_device(dev_Selector s){
template <typename DataType, typename dev_Selector>
void sycl_padding_test_per_device(dev_Selector s) {
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_simple_padding<DataType, RowMajor, int64_t>(sycl_device);
test_simple_padding<DataType, ColMajor, int64_t>(sycl_device);
test_padded_expr<DataType, RowMajor, int64_t>(sycl_device);
test_padded_expr<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_padding_sycl)
{
for (const auto& device :Eigen::get_sycl_supported_devices()) {
EIGEN_DECLARE_TEST(cxx11_tensor_padding_sycl) {
for (const auto& device : Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_padding_test_per_device<half>(device));
CALL_SUBTEST(sycl_padding_test_per_device<float>(device));
}