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[SYCL] Rebasing the SYCL support branch on top of the Einge upstream master branch.

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* Unifying all loadLocalTile from lhs and rhs to an extract_block function.
* Adding get_tensor operation which was missing in TensorContractionMapper.
* Adding the -D method missing from cmake for Disable_Skinny Contraction operation.
* Wrapping all the indices in TensorScanSycl into Scan parameter struct.
* Fixing typo in Device SYCL
* Unifying load to private register for tall/skinny no shared
* Unifying load to vector tile for tensor-vector/vector-tensor operation
* Removing all the LHS/RHS class for extracting data from global
* Removing Outputfunction from TensorContractionSkinnyNoshared.
* Combining the local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining the no-local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining General Tensor-Vector and VectorTensor contraction into one kernel.
* Making double buffering optional for Tensor contraction when local memory is version is used.
* Modifying benchmark to accept custom Reduction Sizes
* Disabling AVX optimization for SYCL backend on the host to allow SSE optimization to the host
* Adding Test for SYCL
* Modifying SYCL CMake
This commit is contained in:
Mehdi Goli
2019-11-28 10:08:54 +00:00
parent ea51a9eace
commit 00f32752f7
56 changed files with 7336 additions and 4826 deletions
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50
unsupported/test/cxx11_tensor_shuffling_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>
@@ -29,33 +27,33 @@ using Eigen::Tensor;
using Eigen::TensorMap;
template <typename DataType, int DataLayout, typename IndexType>
static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device)
{
static void test_simple_shuffling_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_shuffle(tensorRange);
Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
Tensor<DataType, 4, DataLayout, IndexType> no_shuffle(tensorRange);
tensor.setRandom();
const size_t buffSize =tensor.size()*sizeof(DataType);
const size_t buffSize = tensor.size() * sizeof(DataType);
array<IndexType, 4> shuffles;
shuffles[0] = 0;
shuffles[1] = 1;
shuffles[2] = 2;
shuffles[3] = 3;
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(buffSize));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(buffSize));
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(buffSize));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(buffSize));
TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu2(gpu_data2, tensorRange);
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1,
tensorRange);
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu2(gpu_data2,
tensorRange);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), buffSize);
gpu2.device(sycl_device)=gpu1.shuffle(shuffles);
gpu2.device(sycl_device) = gpu1.shuffle(shuffles);
sycl_device.memcpyDeviceToHost(no_shuffle.data(), gpu_data2, buffSize);
sycl_device.synchronize();
@@ -68,7 +66,7 @@ static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device)
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
for (IndexType l = 0; l < sizeDim4; ++l) {
VERIFY_IS_EQUAL(tensor(i,j,k,l), no_shuffle(i,j,k,l));
VERIFY_IS_EQUAL(tensor(i, j, k, l), no_shuffle(i, j, k, l));
}
}
}
@@ -78,12 +76,14 @@ static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device)
shuffles[1] = 3;
shuffles[2] = 1;
shuffles[3] = 0;
array<IndexType, 4> tensorrangeShuffle = {{sizeDim3, sizeDim4, sizeDim2, sizeDim1}};
Tensor<DataType, 4, DataLayout,IndexType> shuffle(tensorrangeShuffle);
DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(buffSize));
TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu3(gpu_data3, tensorrangeShuffle);
array<IndexType, 4> tensorrangeShuffle = {
{sizeDim3, sizeDim4, sizeDim2, sizeDim1}};
Tensor<DataType, 4, DataLayout, IndexType> shuffle(tensorrangeShuffle);
DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(buffSize));
TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu3(
gpu_data3, tensorrangeShuffle);
gpu3.device(sycl_device)=gpu1.shuffle(shuffles);
gpu3.device(sycl_device) = gpu1.shuffle(shuffles);
sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize);
sycl_device.synchronize();
@@ -96,24 +96,22 @@ static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device)
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
for (IndexType l = 0; l < sizeDim4; ++l) {
VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(k,l,j,i));
VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i));
}
}
}
}
}
template<typename DataType, typename dev_Selector> void sycl_shuffling_test_per_device(dev_Selector s){
template <typename DataType, typename dev_Selector>
void sycl_shuffling_test_per_device(dev_Selector s) {
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_simple_shuffling_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_simple_shuffling_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_shuffling_sycl)
{
for (const auto& device :Eigen::get_sycl_supported_devices()) {
EIGEN_DECLARE_TEST(cxx11_tensor_shuffling_sycl) {
for (const auto& device : Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_shuffling_test_per_device<float>(device));
}
}