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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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49
unsupported/test/cxx11_tensor_inflation_sycl.cpp
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@@ -30,16 +30,14 @@ using Eigen::Tensor;
// (4, 0, 0, 4, 0, 0, 4).
template <typename DataType, int DataLayout, typename IndexType>
void test_simple_inflation_sycl(const Eigen::SyclDevice &sycl_device) {
void test_simple_inflation_sycl(const Eigen::SyclDevice& sycl_device) {
IndexType sizeDim1 = 2;
IndexType sizeDim2 = 3;
IndexType sizeDim3 = 5;
IndexType sizeDim4 = 7;
array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
Tensor<DataType, 4, DataLayout,IndexType> tensor(tensorRange);
Tensor<DataType, 4, DataLayout,IndexType> no_stride(tensorRange);
Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
Tensor<DataType, 4, DataLayout, IndexType> no_stride(tensorRange);
tensor.setRandom();
array<IndexType, 4> strides;
@@ -48,16 +46,15 @@ void test_simple_inflation_sycl(const Eigen::SyclDevice &sycl_device) {
strides[2] = 1;
strides[3] = 1;
const size_t tensorBuffSize = tensor.size() * sizeof(DataType);
DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
DataType* gpu_data_no_stride = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
DataType* gpu_data_no_stride = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_no_stride(gpu_data_no_stride, tensorRange);
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_no_stride(gpu_data_no_stride, tensorRange);
sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
gpu_no_stride.device(sycl_device)=gpu_tensor.inflate(strides);
gpu_no_stride.device(sycl_device) = gpu_tensor.inflate(strides);
sycl_device.memcpyDeviceToHost(no_stride.data(), gpu_data_no_stride, tensorBuffSize);
VERIFY_IS_EQUAL(no_stride.dimension(0), sizeDim1);
@@ -69,13 +66,12 @@ void test_simple_inflation_sycl(const Eigen::SyclDevice &sycl_device) {
for (IndexType j = 0; j < 3; ++j) {
for (IndexType k = 0; k < 5; ++k) {
for (IndexType l = 0; l < 7; ++l) {
VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(i,j,k,l));
VERIFY_IS_EQUAL(tensor(i, j, k, l), no_stride(i, j, k, l));
}
}
}
}
strides[0] = 2;
strides[1] = 4;
strides[2] = 2;
@@ -89,10 +85,10 @@ void test_simple_inflation_sycl(const Eigen::SyclDevice &sycl_device) {
Tensor<DataType, 4, DataLayout, IndexType> inflated(inflatedTensorRange);
const size_t inflatedTensorBuffSize =inflated.size()*sizeof(DataType);
DataType* gpu_data_inflated = static_cast<DataType*>(sycl_device.allocate(inflatedTensorBuffSize));
const size_t inflatedTensorBuffSize = inflated.size() * sizeof(DataType);
DataType* gpu_data_inflated = static_cast<DataType*>(sycl_device.allocate(inflatedTensorBuffSize));
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_inflated(gpu_data_inflated, inflatedTensorRange);
gpu_inflated.device(sycl_device)=gpu_tensor.inflate(strides);
gpu_inflated.device(sycl_device) = gpu_tensor.inflate(strides);
sycl_device.memcpyDeviceToHost(inflated.data(), gpu_data_inflated, inflatedTensorBuffSize);
VERIFY_IS_EQUAL(inflated.dimension(0), inflatedSizeDim1);
@@ -104,14 +100,11 @@ void test_simple_inflation_sycl(const Eigen::SyclDevice &sycl_device) {
for (IndexType j = 0; j < inflatedSizeDim2; ++j) {
for (IndexType k = 0; k < inflatedSizeDim3; ++k) {
for (IndexType l = 0; l < inflatedSizeDim4; ++l) {
if (i % strides[0] == 0 &&
j % strides[1] == 0 &&
k % strides[2] == 0 &&
l % strides[3] == 0) {
VERIFY_IS_EQUAL(inflated(i,j,k,l),
tensor(i/strides[0], j/strides[1], k/strides[2], l/strides[3]));
if (i % strides[0] == 0 && j % strides[1] == 0 && k % strides[2] == 0 && l % strides[3] == 0) {
VERIFY_IS_EQUAL(inflated(i, j, k, l),
tensor(i / strides[0], j / strides[1], k / strides[2], l / strides[3]));
} else {
VERIFY_IS_EQUAL(0, inflated(i,j,k,l));
VERIFY_IS_EQUAL(0, inflated(i, j, k, l));
}
}
}
@@ -122,15 +115,15 @@ void test_simple_inflation_sycl(const Eigen::SyclDevice &sycl_device) {
sycl_device.deallocate(gpu_data_inflated);
}
template<typename DataType, typename dev_Selector> void sycl_inflation_test_per_device(dev_Selector s){
template <typename DataType, typename dev_Selector>
void sycl_inflation_test_per_device(dev_Selector s) {
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_simple_inflation_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_simple_inflation_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_inflation_sycl)
{
for (const auto& device :Eigen::get_sycl_supported_devices()) {
EIGEN_DECLARE_TEST(cxx11_tensor_inflation_sycl) {
for (const auto& device : Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_inflation_test_per_device<half>(device));
CALL_SUBTEST(sycl_inflation_test_per_device<float>(device));
}