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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.

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This commit is contained in:
Mehdi Goli
2016-11-08 17:08:02 +00:00
parent dad177be01
commit d57430dd73
11 changed files with 328 additions and 341 deletions
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67
unsupported/test/cxx11_tensor_sycl.cpp
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@@ -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));
}