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Modifying TensorDeviceSycl.h to always create buffer of type uint8_t and convert them to the actual type at the execution on the device; adding the queue interface class to separate the lifespan of sycl queue and buffers,created for that queue, from Eigen::SyclDevice; modifying sycl tests to support the evaluation of the results for both row major and column major data layout on all different devices that are supported by Sycl{CPU; GPU; and Host}.

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This commit is contained in:
Mehdi Goli
2016-11-18 16:20:42 +00:00
parent 5159675c33
commit 622805a0c5
13 changed files with 439 additions and 358 deletions
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100
unsupported/test/cxx11_tensor_broadcast_sycl.cpp
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@@ -25,38 +25,47 @@ using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
template <typename DataType, int DataLayout>
static void test_broadcast_sycl_fixed(const Eigen::SyclDevice &sycl_device){
// BROADCAST test:
array<int, 4> in_range = {{2, 3, 5, 7}};
array<int, 4> broadcasts = {{2, 3, 1, 4}};
int inDim1=2;
int inDim2=3;
int inDim3=5;
int inDim4=7;
int bDim1=2;
int bDim2=3;
int bDim3=1;
int bDim4=4;
array<int, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}};
array<int, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}};
array<int, 4> out_range; // = in_range * broadcasts
for (size_t i = 0; i < out_range.size(); ++i)
out_range[i] = in_range[i] * broadcasts[i];
Tensor<float, 4> input(in_range);
Tensor<float, 4> out(out_range);
Tensor<DataType, 4, DataLayout> input(in_range);
Tensor<DataType, 4, DataLayout> out(out_range);
for (size_t i = 0; i < in_range.size(); ++i)
VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
for (int i = 0; i < input.size(); ++i)
input(i) = static_cast<float>(i);
input(i) = static_cast<DataType>(i);
float * gpu_in_data = static_cast<float*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(float)));
float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
DataType * gpu_in_data = static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(DataType)));
DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));
TensorMap<TensorFixedSize<float, Sizes<2, 3, 5, 7>>> gpu_in(gpu_in_data, in_range);
TensorMap<Tensor<float, 4>> gpu_out(gpu_out_data, out_range);
sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(float));
TensorMap<TensorFixedSize<DataType, Sizes<2, 3, 5, 7>, DataLayout>> gpu_in(gpu_in_data, in_range);
TensorMap<Tensor<DataType, 4, DataLayout>> gpu_out(gpu_out_data, out_range);
sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(DataType));
gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
for (int i = 0; i < 4; ++i) {
for (int j = 0; j < 9; ++j) {
for (int k = 0; k < 5; ++k) {
for (int l = 0; l < 28; ++l) {
for (int i = 0; i < inDim1*bDim1; ++i) {
for (int j = 0; j < inDim2*bDim2; ++j) {
for (int k = 0; k < inDim3*bDim3; ++k) {
for (int l = 0; l < inDim4*bDim4; ++l) {
VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), out(i,j,k,l));
}
}
@@ -67,40 +76,48 @@ static void test_broadcast_sycl_fixed(const Eigen::SyclDevice &sycl_device){
sycl_device.deallocate(gpu_out_data);
}
template <typename DataType, int DataLayout>
static void test_broadcast_sycl(const Eigen::SyclDevice &sycl_device){
// BROADCAST test:
array<int, 4> in_range = {{2, 3, 5, 7}};
array<int, 4> broadcasts = {{2, 3, 1, 4}};
int inDim1=2;
int inDim2=3;
int inDim3=5;
int inDim4=7;
int bDim1=2;
int bDim2=3;
int bDim3=1;
int bDim4=4;
array<int, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}};
array<int, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}};
array<int, 4> out_range; // = in_range * broadcasts
for (size_t i = 0; i < out_range.size(); ++i)
out_range[i] = in_range[i] * broadcasts[i];
Tensor<float, 4> input(in_range);
Tensor<float, 4> out(out_range);
Tensor<DataType, 4, DataLayout> input(in_range);
Tensor<DataType, 4, DataLayout> out(out_range);
for (size_t i = 0; i < in_range.size(); ++i)
VERIFY_IS_EQUAL(out.dimension(i), out_range[i]);
for (int i = 0; i < input.size(); ++i)
input(i) = static_cast<float>(i);
input(i) = static_cast<DataType>(i);
float * gpu_in_data = static_cast<float*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(float)));
float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
DataType * gpu_in_data = static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(DataType)));
DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));
TensorMap<Tensor<float, 4>> gpu_in(gpu_in_data, in_range);
TensorMap<Tensor<float, 4>> gpu_out(gpu_out_data, out_range);
sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(float));
TensorMap<Tensor<DataType, 4, DataLayout>> gpu_in(gpu_in_data, in_range);
TensorMap<Tensor<DataType, 4, DataLayout>> gpu_out(gpu_out_data, out_range);
sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(DataType));
gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
for (int i = 0; i < 4; ++i) {
for (int j = 0; j < 9; ++j) {
for (int k = 0; k < 5; ++k) {
for (int l = 0; l < 28; ++l) {
VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), out(i,j,k,l));
for (int i = 0; i < inDim1*bDim1; ++i) {
for (int j = 0; j < inDim2*bDim2; ++j) {
for (int k = 0; k < inDim3*bDim3; ++k) {
for (int l = 0; l < inDim4*bDim4; ++l) {
VERIFY_IS_APPROX(input(i%inDim1,j%inDim2,k%inDim3,l%inDim4), out(i,j,k,l));
}
}
}
@@ -110,10 +127,21 @@ static void test_broadcast_sycl(const Eigen::SyclDevice &sycl_device){
sycl_device.deallocate(gpu_out_data);
}
template<typename DataType, typename dev_Selector> void sycl_broadcast_test_per_device(dev_Selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_broadcast_sycl_fixed<DataType, RowMajor>(sycl_device);
test_broadcast_sycl<DataType, RowMajor>(sycl_device);
test_broadcast_sycl_fixed<DataType, ColMajor>(sycl_device);
test_broadcast_sycl<DataType, ColMajor>(sycl_device);
}
void test_cxx11_tensor_broadcast_sycl() {
cl::sycl::gpu_selector s;
Eigen::SyclDevice sycl_device(s);
CALL_SUBTEST(test_broadcast_sycl_fixed(sycl_device));
CALL_SUBTEST(test_broadcast_sycl(sycl_device));
printf("Test on GPU: OpenCL\n");
CALL_SUBTEST(sycl_broadcast_test_per_device<float>((cl::sycl::gpu_selector())));
printf("repeating the test on CPU: OpenCL\n");
CALL_SUBTEST(sycl_broadcast_test_per_device<float>((cl::sycl::cpu_selector())));
printf("repeating the test on CPU: HOST\n");
CALL_SUBTEST(sycl_broadcast_test_per_device<float>((cl::sycl::host_selector())));
printf("Test Passed******************\n" );
}