mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-21 07:19:46 +08:00
277 lines
11 KiB
C++
277 lines
11 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2016
|
|
// Mehdi Goli Codeplay Software Ltd.
|
|
// Ralph Potter Codeplay Software Ltd.
|
|
// Luke Iwanski Codeplay Software Ltd.
|
|
// Contact: <eigen@codeplay.com>
|
|
// Benoit Steiner <benoit.steiner.goog@gmail.com>
|
|
//
|
|
// This Source Code Form is subject to the terms of the Mozilla
|
|
// 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_TEST_FUNC cxx11_tensor_sycl
|
|
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
|
|
#define EIGEN_USE_SYCL
|
|
|
|
#include "main.h"
|
|
#include <unsupported/Eigen/CXX11/Tensor>
|
|
|
|
using Eigen::array;
|
|
using Eigen::SyclDevice;
|
|
using Eigen::Tensor;
|
|
using Eigen::TensorMap;
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
void test_sycl_mem_transfers(const Eigen::SyclDevice &sycl_device) {
|
|
IndexType sizeDim1 = 100;
|
|
IndexType sizeDim2 = 10;
|
|
IndexType sizeDim3 = 20;
|
|
array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
|
|
Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
|
|
Tensor<DataType, 3, DataLayout, IndexType> out1(tensorRange);
|
|
Tensor<DataType, 3, DataLayout, IndexType> out2(tensorRange);
|
|
Tensor<DataType, 3, DataLayout, IndexType> out3(tensorRange);
|
|
|
|
in1 = in1.random();
|
|
|
|
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
|
|
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(out1.size()*sizeof(DataType)));
|
|
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
|
|
|
|
sycl_device.memcpyHostToDevice(gpu_data1, in1.data(),(in1.size())*sizeof(DataType));
|
|
sycl_device.memcpyHostToDevice(gpu_data2, in1.data(),(in1.size())*sizeof(DataType));
|
|
gpu1.device(sycl_device) = gpu1 * 3.14f;
|
|
gpu2.device(sycl_device) = gpu2 * 2.7f;
|
|
sycl_device.memcpyDeviceToHost(out1.data(), gpu_data1,(out1.size())*sizeof(DataType));
|
|
sycl_device.memcpyDeviceToHost(out2.data(), gpu_data1,(out2.size())*sizeof(DataType));
|
|
sycl_device.memcpyDeviceToHost(out3.data(), gpu_data2,(out3.size())*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
|
|
for (IndexType i = 0; i < in1.size(); ++i) {
|
|
VERIFY_IS_APPROX(out1(i), in1(i) * 3.14f);
|
|
VERIFY_IS_APPROX(out2(i), in1(i) * 3.14f);
|
|
VERIFY_IS_APPROX(out3(i), in1(i) * 2.7f);
|
|
}
|
|
|
|
sycl_device.deallocate(gpu_data1);
|
|
sycl_device.deallocate(gpu_data2);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
void test_sycl_mem_sync(const Eigen::SyclDevice &sycl_device) {
|
|
IndexType size = 20;
|
|
array<IndexType, 1> tensorRange = {{size}};
|
|
Tensor<DataType, 1, DataLayout, IndexType> in1(tensorRange);
|
|
Tensor<DataType, 1, DataLayout, IndexType> in2(tensorRange);
|
|
Tensor<DataType, 1, DataLayout, IndexType> out(tensorRange);
|
|
|
|
in1 = in1.random();
|
|
in2 = in1;
|
|
|
|
DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
|
|
|
|
TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> gpu1(gpu_data, tensorRange);
|
|
sycl_device.memcpyHostToDevice(gpu_data, in1.data(),(in1.size())*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
in1.setZero();
|
|
|
|
sycl_device.memcpyDeviceToHost(out.data(), gpu_data, out.size()*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
|
|
for (IndexType i = 0; i < in1.size(); ++i) {
|
|
VERIFY_IS_APPROX(out(i), in2(i));
|
|
}
|
|
|
|
sycl_device.deallocate(gpu_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout, typename IndexType>
|
|
void test_sycl_computations(const Eigen::SyclDevice &sycl_device) {
|
|
|
|
IndexType sizeDim1 = 100;
|
|
IndexType sizeDim2 = 10;
|
|
IndexType sizeDim3 = 20;
|
|
array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
|
|
Tensor<DataType, 3,DataLayout, IndexType> in1(tensorRange);
|
|
Tensor<DataType, 3,DataLayout, IndexType> in2(tensorRange);
|
|
Tensor<DataType, 3,DataLayout, IndexType> in3(tensorRange);
|
|
Tensor<DataType, 3,DataLayout, IndexType> out(tensorRange);
|
|
|
|
in2 = in2.random();
|
|
in3 = in3.random();
|
|
|
|
DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
|
|
DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.size()*sizeof(DataType)));
|
|
DataType * gpu_in3_data = static_cast<DataType*>(sycl_device.allocate(in3.size()*sizeof(DataType)));
|
|
DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.size()*sizeof(DataType)));
|
|
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in3(gpu_in3_data, tensorRange);
|
|
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
|
|
|
|
/// a=1.2f
|
|
gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
|
|
sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.size())*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
|
|
for (IndexType i = 0; i < sizeDim1; ++i) {
|
|
for (IndexType j = 0; j < sizeDim2; ++j) {
|
|
for (IndexType k = 0; k < sizeDim3; ++k) {
|
|
VERIFY_IS_APPROX(in1(i,j,k), 1.2f);
|
|
}
|
|
}
|
|
}
|
|
printf("a=1.2f Test passed\n");
|
|
|
|
/// a=b*1.2f
|
|
gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
|
|
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.size())*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
|
|
for (IndexType i = 0; i < sizeDim1; ++i) {
|
|
for (IndexType j = 0; j < sizeDim2; ++j) {
|
|
for (IndexType k = 0; k < sizeDim3; ++k) {
|
|
VERIFY_IS_APPROX(out(i,j,k),
|
|
in1(i,j,k) * 1.2f);
|
|
}
|
|
}
|
|
}
|
|
printf("a=b*1.2f Test Passed\n");
|
|
|
|
/// c=a*b
|
|
sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.size())*sizeof(DataType));
|
|
gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
|
|
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
|
|
for (IndexType i = 0; i < sizeDim1; ++i) {
|
|
for (IndexType j = 0; j < sizeDim2; ++j) {
|
|
for (IndexType k = 0; k < sizeDim3; ++k) {
|
|
VERIFY_IS_APPROX(out(i,j,k),
|
|
in1(i,j,k) *
|
|
in2(i,j,k));
|
|
}
|
|
}
|
|
}
|
|
printf("c=a*b Test Passed\n");
|
|
|
|
/// c=a+b
|
|
gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
|
|
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
for (IndexType i = 0; i < sizeDim1; ++i) {
|
|
for (IndexType j = 0; j < sizeDim2; ++j) {
|
|
for (IndexType k = 0; k < sizeDim3; ++k) {
|
|
VERIFY_IS_APPROX(out(i,j,k),
|
|
in1(i,j,k) +
|
|
in2(i,j,k));
|
|
}
|
|
}
|
|
}
|
|
printf("c=a+b Test Passed\n");
|
|
|
|
/// c=a*a
|
|
gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
|
|
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
for (IndexType i = 0; i < sizeDim1; ++i) {
|
|
for (IndexType j = 0; j < sizeDim2; ++j) {
|
|
for (IndexType k = 0; k < sizeDim3; ++k) {
|
|
VERIFY_IS_APPROX(out(i,j,k),
|
|
in1(i,j,k) *
|
|
in1(i,j,k));
|
|
}
|
|
}
|
|
}
|
|
printf("c= a*a Test Passed\n");
|
|
|
|
//a*3.14f + b*2.7f
|
|
gpu_out.device(sycl_device) = gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f);
|
|
sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.size())*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
for (IndexType i = 0; i < sizeDim1; ++i) {
|
|
for (IndexType j = 0; j < sizeDim2; ++j) {
|
|
for (IndexType k = 0; k < sizeDim3; ++k) {
|
|
VERIFY_IS_APPROX(out(i,j,k),
|
|
in1(i,j,k) * 3.14f
|
|
+ in2(i,j,k) * 2.7f);
|
|
}
|
|
}
|
|
}
|
|
printf("a*3.14f + b*2.7f Test Passed\n");
|
|
|
|
///d= (a>0.5? b:c)
|
|
sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(),(in3.size())*sizeof(DataType));
|
|
gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
|
|
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*sizeof(DataType));
|
|
sycl_device.synchronize();
|
|
for (IndexType i = 0; i < sizeDim1; ++i) {
|
|
for (IndexType j = 0; j < sizeDim2; ++j) {
|
|
for (IndexType k = 0; k < sizeDim3; ++k) {
|
|
VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f)
|
|
? in2(i, j, k)
|
|
: in3(i, j, k));
|
|
}
|
|
}
|
|
}
|
|
printf("d= (a>0.5? b:c) Test Passed\n");
|
|
sycl_device.deallocate(gpu_in1_data);
|
|
sycl_device.deallocate(gpu_in2_data);
|
|
sycl_device.deallocate(gpu_in3_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
template<typename Scalar1, typename Scalar2, int DataLayout, typename IndexType>
|
|
static void test_sycl_cast(const Eigen::SyclDevice& sycl_device){
|
|
IndexType size = 20;
|
|
array<IndexType, 1> tensorRange = {{size}};
|
|
Tensor<Scalar1, 1, DataLayout, IndexType> in(tensorRange);
|
|
Tensor<Scalar2, 1, DataLayout, IndexType> out(tensorRange);
|
|
Tensor<Scalar2, 1, DataLayout, IndexType> out_host(tensorRange);
|
|
|
|
in = in.random();
|
|
|
|
Scalar1* gpu_in_data = static_cast<Scalar1*>(sycl_device.allocate(in.size()*sizeof(Scalar1)));
|
|
Scalar2 * gpu_out_data = static_cast<Scalar2*>(sycl_device.allocate(out.size()*sizeof(Scalar2)));
|
|
|
|
TensorMap<Tensor<Scalar1, 1, DataLayout, IndexType>> gpu_in(gpu_in_data, tensorRange);
|
|
TensorMap<Tensor<Scalar2, 1, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
|
|
sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.size())*sizeof(Scalar1));
|
|
gpu_out.device(sycl_device) = gpu_in. template cast<Scalar2>();
|
|
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, out.size()*sizeof(Scalar2));
|
|
out_host = in. template cast<Scalar2>();
|
|
for(IndexType i=0; i< size; i++)
|
|
{
|
|
VERIFY_IS_APPROX(out(i), out_host(i));
|
|
}
|
|
printf("cast Test Passed\n");
|
|
sycl_device.deallocate(gpu_in_data);
|
|
sycl_device.deallocate(gpu_out_data);
|
|
}
|
|
template<typename DataType, typename dev_Selector> void sycl_computing_test_per_device(dev_Selector s){
|
|
QueueInterface queueInterface(s);
|
|
auto sycl_device = Eigen::SyclDevice(&queueInterface);
|
|
test_sycl_mem_transfers<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_sycl_computations<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_sycl_mem_sync<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_sycl_mem_transfers<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_sycl_computations<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_sycl_mem_sync<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_sycl_cast<DataType, int, RowMajor, int64_t>(sycl_device);
|
|
test_sycl_cast<DataType, int, ColMajor, int64_t>(sycl_device);
|
|
}
|
|
|
|
void test_cxx11_tensor_sycl() {
|
|
for (const auto& device :Eigen::get_sycl_supported_devices()) {
|
|
CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
|
|
}
|
|
}
|