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https://gitlab.com/libeigen/eigen.git
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Converting all sycl buffers to uninitialised device only buffers; adding memcpyHostToDevice and memcpyDeviceToHost on syclDevice; modifying all examples to obey the new rules; moving sycl queue creating to the device based on Benoit suggestion; removing the sycl specefic condition for returning m_result in TensorReduction.h according to Benoit suggestion.
This commit is contained in:
Mehdi Goli
@@ -25,55 +25,50 @@ using Eigen::SyclDevice;
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using Eigen::Tensor;
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using Eigen::TensorMap;
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// Types used in tests:
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using TestTensor = Tensor<float, 3>;
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using TestTensorMap = TensorMap<Tensor<float, 3>>;
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static void test_broadcast_sycl(){
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static void test_broadcast_sycl(const Eigen::SyclDevice &sycl_device){
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cl::sycl::gpu_selector s;
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cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
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for (const auto& e : l) {
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try {
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std::rethrow_exception(e);
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} catch (cl::sycl::exception e) {
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std::cout << e.what() << std::endl;
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// BROADCAST test:
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array<int, 4> in_range = {{2, 3, 5, 7}};
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array<int, 4> broadcasts = {{2, 3, 1, 4}};
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array<int, 4> out_range; // = in_range * broadcasts
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for (size_t i = 0; i < out_range.size(); ++i)
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out_range[i] = in_range[i] * broadcasts[i];
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Tensor<float, 4> input(in_range);
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Tensor<float, 4> out(out_range);
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for (size_t i = 0; i < in_range.size(); ++i)
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VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
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for (int i = 0; i < input.size(); ++i)
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input(i) = static_cast<float>(i);
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float * gpu_in_data = static_cast<float*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(float)));
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float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
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TensorMap<Tensor<float, 4>> gpu_in(gpu_in_data, in_range);
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TensorMap<Tensor<float, 4>> gpu_out(gpu_out_data, out_range);
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sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(float));
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gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
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for (int i = 0; i < 4; ++i) {
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for (int j = 0; j < 9; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 28; ++l) {
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VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), out(i,j,k,l));
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}
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}
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}
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});
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SyclDevice sycl_device(q);
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// BROADCAST test:
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array<int, 4> in_range = {{2, 3, 5, 7}};
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array<int, in_range.size()> broadcasts = {{2, 3, 1, 4}};
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array<int, in_range.size()> out_range; // = in_range * broadcasts
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for (size_t i = 0; i < out_range.size(); ++i)
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out_range[i] = in_range[i] * broadcasts[i];
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Tensor<float, in_range.size()> input(in_range);
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Tensor<float, out_range.size()> output(out_range);
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for (int i = 0; i < input.size(); ++i)
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input(i) = static_cast<float>(i);
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TensorMap<decltype(input)> gpu_in(input.data(), in_range);
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TensorMap<decltype(output)> gpu_out(output.data(), out_range);
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gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
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sycl_device.deallocate(output.data());
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for (size_t i = 0; i < in_range.size(); ++i)
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VERIFY_IS_EQUAL(output.dimension(i), out_range[i]);
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for (int i = 0; i < 4; ++i) {
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for (int j = 0; j < 9; ++j) {
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for (int k = 0; k < 5; ++k) {
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for (int l = 0; l < 28; ++l) {
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VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), output(i,j,k,l));
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}
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}
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}
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}
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printf("Broadcast Test Passed\n");
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}
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printf("Broadcast Test Passed\n");
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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void test_cxx11_tensor_broadcast_sycl() {
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CALL_SUBTEST(test_broadcast_sycl());
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cl::sycl::gpu_selector s;
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Eigen::SyclDevice sycl_device(s);
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CALL_SUBTEST(test_broadcast_sycl(sycl_device));
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}
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@@ -20,20 +20,12 @@
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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void test_device_sycl() {
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cl::sycl::gpu_selector s;
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cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
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for (const auto& e : l) {
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try {
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std::rethrow_exception(e);
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} catch (cl::sycl::exception e) {
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std::cout << e.what() << std::endl;
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}
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}
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});
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Eigen::SyclDevice sycl_device(q);
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printf("Helo from ComputeCpp: Device Exists\n");
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void test_device_sycl(const Eigen::SyclDevice &sycl_device) {
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std::cout <<"Helo from ComputeCpp: the requested device exists and the device name is : "
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<< sycl_device.m_queue.get_device(). template get_info<cl::sycl::info::device::name>() <<std::endl;;
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}
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void test_cxx11_tensor_device_sycl() {
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CALL_SUBTEST(test_device_sycl());
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cl::sycl::gpu_selector s;
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Eigen::SyclDevice sycl_device(s);
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CALL_SUBTEST(test_device_sycl(sycl_device));
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}
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@@ -22,18 +22,7 @@
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using Eigen::Tensor;
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void test_forced_eval_sycl() {
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cl::sycl::gpu_selector s;
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cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
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for (const auto& e : l) {
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try {
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std::rethrow_exception(e);
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} catch (cl::sycl::exception e) {
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std::cout << e.what() << std::endl;
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}
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}
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});
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SyclDevice sycl_device(q);
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void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) {
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int sizeDim1 = 100;
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int sizeDim2 = 200;
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@@ -43,17 +32,22 @@ void test_forced_eval_sycl() {
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Eigen::Tensor<float, 3> in2(tensorRange);
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Eigen::Tensor<float, 3> out(tensorRange);
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float * gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float)));
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float * gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float)));
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float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
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in1 = in1.random() + in1.constant(10.0f);
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in2 = in2.random() + in2.constant(10.0f);
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// creating TensorMap from tensor
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(in1.data(), tensorRange);
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(in2.data(), tensorRange);
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(out.data(), tensorRange);
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// creating TensorMap from tensor
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(float));
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sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in1.dimensions().TotalSize())*sizeof(float));
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/// c=(a+b)*b
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gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2;
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sycl_device.deallocate(out.data());
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gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2;
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
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for (int i = 0; i < sizeDim1; ++i) {
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for (int j = 0; j < sizeDim2; ++j) {
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for (int k = 0; k < sizeDim3; ++k) {
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@@ -62,7 +56,15 @@ void test_forced_eval_sycl() {
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}
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}
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}
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printf("(a+b)*b Test Passed\n");
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printf("(a+b)*b Test Passed\n");
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sycl_device.deallocate(gpu_in1_data);
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sycl_device.deallocate(gpu_in2_data);
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sycl_device.deallocate(gpu_out_data);
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}
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void test_cxx11_tensor_forced_eval_sycl() { CALL_SUBTEST(test_forced_eval_sycl()); }
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void test_cxx11_tensor_forced_eval_sycl() {
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cl::sycl::gpu_selector s;
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Eigen::SyclDevice sycl_device(s);
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CALL_SUBTEST(test_forced_eval_sycl(sycl_device));
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}
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@@ -22,126 +22,117 @@
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static void test_full_reductions_sycl() {
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cl::sycl::gpu_selector s;
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cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
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for (const auto& e : l) {
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try {
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std::rethrow_exception(e);
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} catch (cl::sycl::exception e) {
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std::cout << e.what() << std::endl;
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}
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}
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});
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Eigen::SyclDevice sycl_device(q);
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static void test_full_reductions_sycl(const Eigen::SyclDevice& sycl_device) {
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const int num_rows = 452;
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const int num_cols = 765;
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array<int, 2> tensorRange = {{num_rows, num_cols}};
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Tensor<float, 2> in(tensorRange);
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Tensor<float, 0> full_redux;
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Tensor<float, 0> full_redux_gpu;
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in.setRandom();
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Tensor<float, 0> full_redux;
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Tensor<float, 0> full_redux_g;
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full_redux = in.sum();
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float* out_data = (float*)sycl_device.allocate(sizeof(float));
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TensorMap<Tensor<float, 2> > in_gpu(in.data(), tensorRange);
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TensorMap<Tensor<float, 0> > full_redux_gpu(out_data);
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full_redux_gpu.device(sycl_device) = in_gpu.sum();
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sycl_device.deallocate(out_data);
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float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float)));
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float* gpu_out_data =(float*)sycl_device.allocate(sizeof(float));
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TensorMap<Tensor<float, 2> > in_gpu(gpu_in_data, tensorRange);
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TensorMap<Tensor<float, 0> > out_gpu(gpu_out_data);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float));
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out_gpu.device(sycl_device) = in_gpu.sum();
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sycl_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_data, sizeof(float));
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// Check that the CPU and GPU reductions return the same result.
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VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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static void test_first_dim_reductions_sycl() {
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cl::sycl::gpu_selector s;
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cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
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for (const auto& e : l) {
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try {
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std::rethrow_exception(e);
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} catch (cl::sycl::exception e) {
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std::cout << e.what() << std::endl;
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}
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}
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});
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Eigen::SyclDevice sycl_device(q);
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static void test_first_dim_reductions_sycl(const Eigen::SyclDevice& sycl_device) {
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int dim_x = 145;
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int dim_y = 1;
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int dim_z = 67;
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array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}};
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Tensor<float, 3> in(tensorRange);
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in.setRandom();
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Eigen::array<int, 1> red_axis;
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red_axis[0] = 0;
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Tensor<float, 2> redux = in.sum(red_axis);
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array<int, 2> reduced_tensorRange = {{dim_y, dim_z}};
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Tensor<float, 2> redux_g(reduced_tensorRange);
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TensorMap<Tensor<float, 3> > in_gpu(in.data(), tensorRange);
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float* out_data = (float*)sycl_device.allocate(dim_y*dim_z*sizeof(float));
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TensorMap<Tensor<float, 2> > redux_gpu(out_data, dim_y, dim_z );
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redux_gpu.device(sycl_device) = in_gpu.sum(red_axis);
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sycl_device.deallocate(out_data);
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Tensor<float, 3> in(tensorRange);
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Tensor<float, 2> redux(reduced_tensorRange);
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Tensor<float, 2> redux_gpu(reduced_tensorRange);
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in.setRandom();
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redux= in.sum(red_axis);
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float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float)));
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float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float)));
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TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange);
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TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float));
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out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
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sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float));
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// Check that the CPU and GPU reductions return the same result.
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for(int j=0; j<dim_y; j++ )
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for(int k=0; k<dim_z; k++ )
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for(int j=0; j<reduced_tensorRange[0]; j++ )
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for(int k=0; k<reduced_tensorRange[1]; k++ )
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VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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static void test_last_dim_reductions_sycl() {
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cl::sycl::gpu_selector s;
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cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
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for (const auto& e : l) {
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try {
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std::rethrow_exception(e);
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} catch (cl::sycl::exception e) {
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std::cout << e.what() << std::endl;
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}
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}
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});
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Eigen::SyclDevice sycl_device(q);
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static void test_last_dim_reductions_sycl(const Eigen::SyclDevice &sycl_device) {
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int dim_x = 567;
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int dim_y = 1;
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int dim_z = 47;
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array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}};
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Tensor<float, 3> in(tensorRange);
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in.setRandom();
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Eigen::array<int, 1> red_axis;
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red_axis[0] = 2;
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Tensor<float, 2> redux = in.sum(red_axis);
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array<int, 2> reduced_tensorRange = {{dim_x, dim_y}};
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Tensor<float, 2> redux_g(reduced_tensorRange);
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TensorMap<Tensor<float, 3> > in_gpu(in.data(), tensorRange);
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float* out_data = (float*)sycl_device.allocate(dim_x*dim_y*sizeof(float));
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TensorMap<Tensor<float, 2> > redux_gpu(out_data, dim_x, dim_y );
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redux_gpu.device(sycl_device) = in_gpu.sum(red_axis);
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sycl_device.deallocate(out_data);
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Tensor<float, 3> in(tensorRange);
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Tensor<float, 2> redux(reduced_tensorRange);
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Tensor<float, 2> redux_gpu(reduced_tensorRange);
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in.setRandom();
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redux= in.sum(red_axis);
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float* gpu_in_data = static_cast<float*>(sycl_device.allocate(in.dimensions().TotalSize()*sizeof(float)));
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float* gpu_out_data = static_cast<float*>(sycl_device.allocate(redux_gpu.dimensions().TotalSize()*sizeof(float)));
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TensorMap<Tensor<float, 3> > in_gpu(gpu_in_data, tensorRange);
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TensorMap<Tensor<float, 2> > out_gpu(gpu_out_data, reduced_tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.dimensions().TotalSize())*sizeof(float));
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out_gpu.device(sycl_device) = in_gpu.sum(red_axis);
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sycl_device.memcpyDeviceToHost(redux_gpu.data(), gpu_out_data, redux_gpu.dimensions().TotalSize()*sizeof(float));
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// Check that the CPU and GPU reductions return the same result.
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for(int j=0; j<dim_x; j++ )
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for(int k=0; k<dim_y; k++ )
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for(int j=0; j<reduced_tensorRange[0]; j++ )
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for(int k=0; k<reduced_tensorRange[1]; k++ )
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VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
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sycl_device.deallocate(gpu_in_data);
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sycl_device.deallocate(gpu_out_data);
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}
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void test_cxx11_tensor_reduction_sycl() {
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CALL_SUBTEST((test_full_reductions_sycl()));
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CALL_SUBTEST((test_first_dim_reductions_sycl()));
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CALL_SUBTEST((test_last_dim_reductions_sycl()));
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cl::sycl::gpu_selector s;
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Eigen::SyclDevice sycl_device(s);
|
||||
CALL_SUBTEST((test_full_reductions_sycl(sycl_device)));
|
||||
CALL_SUBTEST((test_first_dim_reductions_sycl(sycl_device)));
|
||||
CALL_SUBTEST((test_last_dim_reductions_sycl(sycl_device)));
|
||||
|
||||
}
|
||||
|
||||
@@ -27,42 +27,33 @@ using Eigen::SyclDevice;
|
||||
using Eigen::Tensor;
|
||||
using Eigen::TensorMap;
|
||||
|
||||
// Types used in tests:
|
||||
using TestTensor = Tensor<float, 3>;
|
||||
using TestTensorMap = TensorMap<Tensor<float, 3>>;
|
||||
|
||||
void test_sycl_cpu() {
|
||||
cl::sycl::gpu_selector s;
|
||||
cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
|
||||
for (const auto& e : l) {
|
||||
try {
|
||||
std::rethrow_exception(e);
|
||||
} catch (cl::sycl::exception e) {
|
||||
std::cout << e.what() << std::endl;
|
||||
}
|
||||
}
|
||||
});
|
||||
SyclDevice sycl_device(q);
|
||||
void test_sycl_cpu(const Eigen::SyclDevice &sycl_device) {
|
||||
|
||||
int sizeDim1 = 100;
|
||||
int sizeDim2 = 100;
|
||||
int sizeDim3 = 100;
|
||||
array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
|
||||
TestTensor in1(tensorRange);
|
||||
TestTensor in2(tensorRange);
|
||||
TestTensor in3(tensorRange);
|
||||
TestTensor out(tensorRange);
|
||||
in1 = in1.random();
|
||||
Tensor<float, 3> in1(tensorRange);
|
||||
Tensor<float, 3> in2(tensorRange);
|
||||
Tensor<float, 3> in3(tensorRange);
|
||||
Tensor<float, 3> out(tensorRange);
|
||||
|
||||
in2 = in2.random();
|
||||
in3 = in3.random();
|
||||
TestTensorMap gpu_in1(in1.data(), tensorRange);
|
||||
TestTensorMap gpu_in2(in2.data(), tensorRange);
|
||||
TestTensorMap gpu_in3(in3.data(), tensorRange);
|
||||
TestTensorMap gpu_out(out.data(), tensorRange);
|
||||
|
||||
float * gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float)));
|
||||
float * gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float)));
|
||||
float * gpu_in3_data = static_cast<float*>(sycl_device.allocate(in3.dimensions().TotalSize()*sizeof(float)));
|
||||
float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
|
||||
|
||||
TensorMap<Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
|
||||
TensorMap<Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
|
||||
TensorMap<Tensor<float, 3>> gpu_in3(gpu_in3_data, tensorRange);
|
||||
TensorMap<Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);
|
||||
|
||||
/// a=1.2f
|
||||
gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
|
||||
sycl_device.deallocate(in1.data());
|
||||
sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.dimensions().TotalSize())*sizeof(float));
|
||||
for (int i = 0; i < sizeDim1; ++i) {
|
||||
for (int j = 0; j < sizeDim2; ++j) {
|
||||
for (int k = 0; k < sizeDim3; ++k) {
|
||||
@@ -74,7 +65,7 @@ void test_sycl_cpu() {
|
||||
|
||||
/// a=b*1.2f
|
||||
gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
|
||||
sycl_device.deallocate(out.data());
|
||||
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.dimensions().TotalSize())*sizeof(float));
|
||||
for (int i = 0; i < sizeDim1; ++i) {
|
||||
for (int j = 0; j < sizeDim2; ++j) {
|
||||
for (int k = 0; k < sizeDim3; ++k) {
|
||||
@@ -86,8 +77,9 @@ void test_sycl_cpu() {
|
||||
printf("a=b*1.2f Test Passed\n");
|
||||
|
||||
/// c=a*b
|
||||
sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.dimensions().TotalSize())*sizeof(float));
|
||||
gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
|
||||
sycl_device.deallocate(out.data());
|
||||
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
|
||||
for (int i = 0; i < sizeDim1; ++i) {
|
||||
for (int j = 0; j < sizeDim2; ++j) {
|
||||
for (int k = 0; k < sizeDim3; ++k) {
|
||||
@@ -101,7 +93,7 @@ void test_sycl_cpu() {
|
||||
|
||||
/// c=a+b
|
||||
gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
|
||||
sycl_device.deallocate(out.data());
|
||||
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
|
||||
for (int i = 0; i < sizeDim1; ++i) {
|
||||
for (int j = 0; j < sizeDim2; ++j) {
|
||||
for (int k = 0; k < sizeDim3; ++k) {
|
||||
@@ -115,7 +107,7 @@ void test_sycl_cpu() {
|
||||
|
||||
/// c=a*a
|
||||
gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
|
||||
sycl_device.deallocate(out.data());
|
||||
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
|
||||
for (int i = 0; i < sizeDim1; ++i) {
|
||||
for (int j = 0; j < sizeDim2; ++j) {
|
||||
for (int k = 0; k < sizeDim3; ++k) {
|
||||
@@ -125,12 +117,11 @@ void test_sycl_cpu() {
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
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.deallocate(out.data());
|
||||
sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
|
||||
for (int i = 0; i < sizeDim1; ++i) {
|
||||
for (int j = 0; j < sizeDim2; ++j) {
|
||||
for (int k = 0; k < sizeDim3; ++k) {
|
||||
@@ -143,8 +134,9 @@ void test_sycl_cpu() {
|
||||
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.dimensions().TotalSize())*sizeof(float));
|
||||
gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
|
||||
sycl_device.deallocate(out.data());
|
||||
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
|
||||
for (int i = 0; i < sizeDim1; ++i) {
|
||||
for (int j = 0; j < sizeDim2; ++j) {
|
||||
for (int k = 0; k < sizeDim3; ++k) {
|
||||
@@ -155,8 +147,13 @@ void test_sycl_cpu() {
|
||||
}
|
||||
}
|
||||
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);
|
||||
}
|
||||
void test_cxx11_tensor_sycl() {
|
||||
CALL_SUBTEST(test_sycl_cpu());
|
||||
cl::sycl::gpu_selector s;
|
||||
Eigen::SyclDevice sycl_device(s);
|
||||
CALL_SUBTEST(test_sycl_cpu(sycl_device));
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user