static void test_sycl_simple_argmax(const Eigen::SyclDevice &sycl_device){
Tensor<DataType, 3, Layout, DenseIndex> in(Eigen::array<DenseIndex, 3>{{2,2,2}});
Tensor<DenseIndex, 0, Layout, DenseIndex> out_max;
Tensor<DenseIndex, 0, Layout, DenseIndex> out_min;
in.setRandom();
in *= in.constant(100.0);
in(0, 0, 0) = -1000.0;
in(1, 1, 1) = 1000.0;
std::size_t in_bytes = in.size() * sizeof(DataType);
std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);
DataType * d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
DenseIndex* d_out_max = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
DenseIndex* d_out_min = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 3, Layout, DenseIndex> > gpu_in(d_in, Eigen::array<DenseIndex, 3>{{2,2,2}});
Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_max(d_out_max);
Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex> > gpu_out_min(d_out_min);
sycl_device.memcpyHostToDevice(d_in, in.data(),in_bytes);
gpu_out_max.device(sycl_device) = gpu_in.argmax();
gpu_out_min.device(sycl_device) = gpu_in.argmin();
sycl_device.memcpyDeviceToHost(out_max.data(), d_out_max, out_bytes);
sycl_device.memcpyDeviceToHost(out_min.data(), d_out_min, out_bytes);
VERIFY_IS_EQUAL(out_max(), 2*2*2 - 1);
VERIFY_IS_EQUAL(out_min(), 0);
sycl_device.deallocate(d_in);
sycl_device.deallocate(d_out_max);
sycl_device.deallocate(d_out_min);
}
static void test_simple_slice(const Eigen::SyclDevice &sycl_device)
{
int sizeDim1 = 2;
int sizeDim2 = 3;
int sizeDim3 = 5;
int sizeDim4 = 7;
int sizeDim5 = 11;
array<int, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
Tensor<DataType, 5,DataLayout> tensor(tensorRange);
tensor.setRandom();
array<int, 5> slice1_range ={{1, 1, 1, 1, 1}};
Tensor<DataType, 5,DataLayout> slice1(slice1_range);
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(slice1.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 5,DataLayout>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 5,DataLayout>> gpu2(gpu_data2, slice1_range);
Eigen::DSizes<ptrdiff_t, 5> indices(1,2,3,4,5);
Eigen::DSizes<ptrdiff_t, 5> sizes(1,1,1,1,1);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
gpu2.device(sycl_device)=gpu1.slice(indices, sizes);
sycl_device.memcpyDeviceToHost(slice1.data(), gpu_data2,(slice1.size())*sizeof(DataType));
VERIFY_IS_EQUAL(slice1(0,0,0,0,0), tensor(1,2,3,4,5));
array<int, 5> slice2_range ={{1,1,2,2,3}};
Tensor<DataType, 5,DataLayout> slice2(slice2_range);
DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(slice2.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 5,DataLayout>> gpu3(gpu_data3, slice2_range);
Eigen::DSizes<ptrdiff_t, 5> indices2(1,1,3,4,5);
Eigen::DSizes<ptrdiff_t, 5> sizes2(1,1,2,2,3);
gpu3.device(sycl_device)=gpu1.slice(indices2, sizes2);
sycl_device.memcpyDeviceToHost(slice2.data(), gpu_data3,(slice2.size())*sizeof(DataType));
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 2; ++j) {
for (int k = 0; k < 3; ++k) {
VERIFY_IS_EQUAL(slice2(0,0,i,j,k), tensor(1,1,3+i,4+j,5+k));
}
}
}
sycl_device.deallocate(gpu_data1);
sycl_device.deallocate(gpu_data2);
sycl_device.deallocate(gpu_data3);
}
static void test_simple_padding(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.setRandom();
array<std::pair<IndexType, IndexType>, 4> paddings;
paddings[0] = std::make_pair(0, 0);
paddings[1] = std::make_pair(2, 1);
paddings[2] = std::make_pair(3, 4);
paddings[3] = std::make_pair(0, 0);
IndexType padedSizeDim1 = 2;
IndexType padedSizeDim2 = 6;
IndexType padedSizeDim3 = 12;
IndexType padedSizeDim4 = 7;
array<IndexType, 4> padedtensorRange = {{padedSizeDim1, padedSizeDim2, padedSizeDim3, padedSizeDim4}};
Tensor<DataType, 4, DataLayout, IndexType> padded(padedtensorRange);
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(padded.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu2(gpu_data2, padedtensorRange);
VERIFY_IS_EQUAL(padded.dimension(0), 2+0);
VERIFY_IS_EQUAL(padded.dimension(1), 3+3);
VERIFY_IS_EQUAL(padded.dimension(2), 5+7);
VERIFY_IS_EQUAL(padded.dimension(3), 7+0);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
gpu2.device(sycl_device)=gpu1.pad(paddings);
sycl_device.memcpyDeviceToHost(padded.data(), gpu_data2,(padded.size())*sizeof(DataType));
for (IndexType i = 0; i < padedSizeDim1; ++i) {
for (IndexType j = 0; j < padedSizeDim2; ++j) {
for (IndexType k = 0; k < padedSizeDim3; ++k) {
for (IndexType l = 0; l < padedSizeDim4; ++l) {
if (j >= 2 && j < 5 && k >= 3 && k < 8) {
VERIFY_IS_EQUAL(padded(i,j,k,l), tensor(i,j-2,k-3,l));
} else {
VERIFY_IS_EQUAL(padded(i,j,k,l), 0.0f);
}
}
}
}
}
sycl_device.deallocate(gpu_data1);
sycl_device.deallocate(gpu_data2);
}
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}};
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);
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);
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)));
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));
gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts);
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(float));
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));
}
}
}
}
printf("Broadcast Test Passed\n");
sycl_device.deallocate(gpu_in_data);
sycl_device.deallocate(gpu_out_data);
}
void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) {
int sizeDim1 = 100;
int sizeDim2 = 20;
int sizeDim3 = 20;
Eigen::array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
Eigen::Tensor<DataType, 3, DataLayout> in1(tensorRange);
Eigen::Tensor<DataType, 3, DataLayout> in2(tensorRange);
Eigen::Tensor<DataType, 3, DataLayout> out(tensorRange);
DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(DataType)));
DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(DataType)));
DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));
in1 = in1.random() + in1.constant(10.0f);
in2 = in2.random() + in2.constant(10.0f);
// creating TensorMap from tensor
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout>> gpu_in1(gpu_in1_data, tensorRange);
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout>> gpu_in2(gpu_in2_data, tensorRange);
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout>> gpu_out(gpu_out_data, tensorRange);
sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(DataType));
sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in1.dimensions().TotalSize())*sizeof(DataType));
/// c=(a+b)*b
gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2;
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(out(i, j, k),
(in1(i, j, k) + in2(i, j, k)) * in2(i, j, k));
}
}
}
printf("(a+b)*b Test Passed\n");
sycl_device.deallocate(gpu_in1_data);
sycl_device.deallocate(gpu_in2_data);
sycl_device.deallocate(gpu_out_data);
}
static void test_padded_expr(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.setRandom();
array<std::pair<IndexType, IndexType>, 4> paddings;
paddings[0] = std::make_pair(0, 0);
paddings[1] = std::make_pair(2, 1);
paddings[2] = std::make_pair(3, 4);
paddings[3] = std::make_pair(0, 0);
Eigen::DSizes<IndexType, 2> reshape_dims;
reshape_dims[0] = 12;
reshape_dims[1] = 84;
Tensor<DataType, 2, DataLayout, IndexType> result(reshape_dims);
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(result.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 4,DataLayout,IndexType>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 2,DataLayout,IndexType>> gpu2(gpu_data2, reshape_dims);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
gpu2.device(sycl_device)=gpu1.pad(paddings).reshape(reshape_dims);
sycl_device.memcpyDeviceToHost(result.data(), gpu_data2,(result.size())*sizeof(DataType));
for (IndexType i = 0; i < 2; ++i) {
for (IndexType j = 0; j < 6; ++j) {
for (IndexType k = 0; k < 12; ++k) {
for (IndexType l = 0; l < 7; ++l) {
const float result_value = DataLayout == ColMajor ?
result(i+2*j,k+12*l) : result(j+6*i,l+7*k);
if (j >= 2 && j < 5 && k >= 3 && k < 8) {
VERIFY_IS_EQUAL(result_value, tensor(i,j-2,k-3,l));
} else {
VERIFY_IS_EQUAL(result_value, 0.0f);
}
}
}
}
}
sycl_device.deallocate(gpu_data1);
sycl_device.deallocate(gpu_data2);
}
static void test_entire_volume_patch_sycl(const Eigen::SyclDevice& sycl_device)
{
const int depth = 4;
const int patch_z = 2;
const int patch_y = 3;
const int patch_x = 5;
const int batch = 7;
array<IndexType, 5> tensorColMajorRange = {{depth, patch_z, patch_y, patch_x, batch}};
array<IndexType, 5> tensorRowMajorRange = {{batch, patch_x, patch_y, patch_z, depth}};
Tensor<DataType, 5, DataLayout,IndexType> tensor_col_major(tensorColMajorRange);
Tensor<DataType, 5, RowMajor,IndexType> tensor_row_major(tensorRowMajorRange);
tensor_col_major.setRandom();
DataType* gpu_data_col_major = static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size()*sizeof(DataType)));
DataType* gpu_data_row_major = static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 5, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),(tensor_col_major.size())*sizeof(DataType));
gpu_row_major.device(sycl_device)=gpu_col_major.swap_layout();
sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major, (tensor_col_major.size())*sizeof(DataType));
// single volume patch: ColMajor
array<IndexType, 6> patchColMajorTensorRange={{depth,patch_z, patch_y, patch_x, patch_z*patch_y*patch_x, batch}};
Tensor<DataType, 6, DataLayout,IndexType> entire_volume_patch_col_major(patchColMajorTensorRange);
size_t patchTensorBuffSize =entire_volume_patch_col_major.size()*sizeof(DataType);
DataType* gpu_data_entire_volume_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
TensorMap<Tensor<DataType, 6, DataLayout,IndexType>> gpu_entire_volume_patch_col_major(gpu_data_entire_volume_patch_col_major, patchColMajorTensorRange);
gpu_entire_volume_patch_col_major.device(sycl_device)=gpu_col_major.extract_volume_patches(patch_z, patch_y, patch_x);
sycl_device.memcpyDeviceToHost(entire_volume_patch_col_major.data(), gpu_data_entire_volume_patch_col_major, patchTensorBuffSize);
// Tensor<float, 5> tensor(depth, patch_z, patch_y, patch_x, batch);
// tensor.setRandom();
// Tensor<float, 5, RowMajor> tensor_row_major = tensor.swap_layout();
//Tensor<float, 6> entire_volume_patch;
//entire_volume_patch = tensor.extract_volume_patches(patch_z, patch_y, patch_x);
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(0), depth);
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(1), patch_z);
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(2), patch_y);
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(3), patch_x);
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(4), patch_z * patch_y * patch_x);
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(5), batch);
// Tensor<float, 6, RowMajor> entire_volume_patch_row_major;
//entire_volume_patch_row_major = tensor_row_major.extract_volume_patches(patch_z, patch_y, patch_x);
array<IndexType, 6> patchRowMajorTensorRange={{batch,patch_z*patch_y*patch_x, patch_x, patch_y, patch_z, depth}};
Tensor<DataType, 6, RowMajor,IndexType> entire_volume_patch_row_major(patchRowMajorTensorRange);
patchTensorBuffSize =entire_volume_patch_row_major.size()*sizeof(DataType);
DataType* gpu_data_entire_volume_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
TensorMap<Tensor<DataType, 6, RowMajor,IndexType>> gpu_entire_volume_patch_row_major(gpu_data_entire_volume_patch_row_major, patchRowMajorTensorRange);
gpu_entire_volume_patch_row_major.device(sycl_device)=gpu_row_major.extract_volume_patches(patch_z, patch_y, patch_x);
sycl_device.memcpyDeviceToHost(entire_volume_patch_row_major.data(), gpu_data_entire_volume_patch_row_major, patchTensorBuffSize);
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(0), batch);
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(1), patch_z * patch_y * patch_x);
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(2), patch_x);
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(3), patch_y);
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(4), patch_z);
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(5), depth);
const int dz = patch_z - 1;
const int dy = patch_y - 1;
const int dx = patch_x - 1;
const int forward_pad_z = dz - dz / 2;
const int forward_pad_y = dy - dy / 2;
const int forward_pad_x = dx - dx / 2;
for (int pz = 0; pz < patch_z; pz++) {
for (int py = 0; py < patch_y; py++) {
for (int px = 0; px < patch_x; px++) {
const int patchId = pz + patch_z * (py + px * patch_y);
for (int z = 0; z < patch_z; z++) {
for (int y = 0; y < patch_y; y++) {
for (int x = 0; x < patch_x; x++) {
for (int b = 0; b < batch; b++) {
for (int d = 0; d < depth; d++) {
float expected = 0.0f;
float expected_row_major = 0.0f;
const int eff_z = z - forward_pad_z + pz;
const int eff_y = y - forward_pad_y + py;
const int eff_x = x - forward_pad_x + px;
if (eff_z >= 0 && eff_y >= 0 && eff_x >= 0 &&
eff_z < patch_z && eff_y < patch_y && eff_x < patch_x) {
expected = tensor_col_major(d, eff_z, eff_y, eff_x, b);
expected_row_major = tensor_row_major(b, eff_x, eff_y, eff_z, d);
}
VERIFY_IS_EQUAL(entire_volume_patch_col_major(d, z, y, x, patchId, b), expected);
VERIFY_IS_EQUAL(entire_volume_patch_row_major(b, patchId, x, y, z, d), expected_row_major);
}
}
}
}
}
}
}
}
sycl_device.deallocate(gpu_data_col_major);
sycl_device.deallocate(gpu_data_row_major);
sycl_device.deallocate(gpu_data_entire_volume_patch_col_major);
sycl_device.deallocate(gpu_data_entire_volume_patch_row_major);
}
static void test_single_voxel_patch_sycl(const Eigen::SyclDevice& sycl_device)
{
IndexType sizeDim0 = 4;
IndexType sizeDim1 = 2;
IndexType sizeDim2 = 3;
IndexType sizeDim3 = 5;
IndexType sizeDim4 = 7;
array<IndexType, 5> tensorColMajorRange = {{sizeDim0, sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
array<IndexType, 5> tensorRowMajorRange = {{sizeDim4, sizeDim3, sizeDim2, sizeDim1, sizeDim0}};
Tensor<DataType, 5, DataLayout,IndexType> tensor_col_major(tensorColMajorRange);
Tensor<DataType, 5, RowMajor,IndexType> tensor_row_major(tensorRowMajorRange);
tensor_col_major.setRandom();
DataType* gpu_data_col_major = static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size()*sizeof(DataType)));
DataType* gpu_data_row_major = static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 5, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),(tensor_col_major.size())*sizeof(DataType));
gpu_row_major.device(sycl_device)=gpu_col_major.swap_layout();
// single volume patch: ColMajor
array<IndexType, 6> patchColMajorTensorRange={{sizeDim0,1, 1, 1, sizeDim1*sizeDim2*sizeDim3, sizeDim4}};
Tensor<DataType, 6, DataLayout,IndexType> single_voxel_patch_col_major(patchColMajorTensorRange);
size_t patchTensorBuffSize =single_voxel_patch_col_major.size()*sizeof(DataType);
DataType* gpu_data_single_voxel_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
TensorMap<Tensor<DataType, 6, DataLayout,IndexType>> gpu_single_voxel_patch_col_major(gpu_data_single_voxel_patch_col_major, patchColMajorTensorRange);
gpu_single_voxel_patch_col_major.device(sycl_device)=gpu_col_major.extract_volume_patches(1, 1, 1);
sycl_device.memcpyDeviceToHost(single_voxel_patch_col_major.data(), gpu_data_single_voxel_patch_col_major, patchTensorBuffSize);
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(0), 4);
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(1), 1);
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(2), 1);
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(3), 1);
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(4), 2 * 3 * 5);
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(5), 7);
array<IndexType, 6> patchRowMajorTensorRange={{sizeDim4, sizeDim1*sizeDim2*sizeDim3, 1, 1, 1, sizeDim0}};
Tensor<DataType, 6, RowMajor,IndexType> single_voxel_patch_row_major(patchRowMajorTensorRange);
patchTensorBuffSize =single_voxel_patch_row_major.size()*sizeof(DataType);
DataType* gpu_data_single_voxel_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
TensorMap<Tensor<DataType, 6, RowMajor,IndexType>> gpu_single_voxel_patch_row_major(gpu_data_single_voxel_patch_row_major, patchRowMajorTensorRange);
gpu_single_voxel_patch_row_major.device(sycl_device)=gpu_row_major.extract_volume_patches(1, 1, 1);
sycl_device.memcpyDeviceToHost(single_voxel_patch_row_major.data(), gpu_data_single_voxel_patch_row_major, patchTensorBuffSize);
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(0), 7);
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(1), 2 * 3 * 5);
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(2), 1);
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(3), 1);
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(4), 1);
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(5), 4);
sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major, (tensor_col_major.size())*sizeof(DataType));
for (IndexType i = 0; i < tensor_col_major.size(); ++i) {
VERIFY_IS_EQUAL(tensor_col_major.data()[i], single_voxel_patch_col_major.data()[i]);
VERIFY_IS_EQUAL(tensor_row_major.data()[i], single_voxel_patch_row_major.data()[i]);
VERIFY_IS_EQUAL(tensor_col_major.data()[i], tensor_row_major.data()[i]);
}
sycl_device.deallocate(gpu_data_col_major);
sycl_device.deallocate(gpu_data_row_major);
sycl_device.deallocate(gpu_data_single_voxel_patch_col_major);
sycl_device.deallocate(gpu_data_single_voxel_patch_row_major);
}
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}};
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();
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.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) {
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.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) {
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.dimensions().TotalSize())*sizeof(float));
gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
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) {
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.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) {
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.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) {
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.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) {
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.dimensions().TotalSize())*sizeof(float));
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.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) {
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);
}
static void test_sycl_argmax_dim(const Eigen::SyclDevice &sycl_device)
{
DenseIndex sizeDim0=9;
DenseIndex sizeDim1=3;
DenseIndex sizeDim2=5;
DenseIndex sizeDim3=7;
Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0,sizeDim1,sizeDim2,sizeDim3);
std::vector<DenseIndex> dims;
dims.push_back(sizeDim0); dims.push_back(sizeDim1); dims.push_back(sizeDim2); dims.push_back(sizeDim3);
for (DenseIndex dim = 0; dim < 4; ++dim) {
array<DenseIndex, 3> out_shape;
for (DenseIndex d = 0; d < 3; ++d) out_shape[d] = (d < dim) ? dims[d] : dims[d+1];
Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);
array<DenseIndex, 4> ix;
for (DenseIndex i = 0; i < sizeDim0; ++i) {
for (DenseIndex j = 0; j < sizeDim1; ++j) {
for (DenseIndex k = 0; k < sizeDim2; ++k) {
for (DenseIndex l = 0; l < sizeDim3; ++l) {
ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
// suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = 10.0
tensor(ix)=(ix[dim] != 0)?-1.0:10.0;
}
}
}
}
std::size_t in_bytes = tensor.size() * sizeof(DataType);
std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);
DataType * d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
DenseIndex* d_out= static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex> > gpu_in(d_in, Eigen::array<DenseIndex, 4>{{sizeDim0,sizeDim1,sizeDim2,sizeDim3}});
Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex> > gpu_out(d_out, out_shape);
sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
gpu_out.device(sycl_device) = gpu_in.argmax(dim);
sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
size_t(sizeDim0*sizeDim1*sizeDim2*sizeDim3 / tensor.dimension(dim)));
for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
// Expect max to be in the first index of the reduced dimension
VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
}
sycl_device.synchronize();
for (DenseIndex i = 0; i < sizeDim0; ++i) {
for (DenseIndex j = 0; j < sizeDim1; ++j) {
for (DenseIndex k = 0; k < sizeDim2; ++k) {
for (DenseIndex l = 0; l < sizeDim3; ++l) {
ix[0] = i; ix[1] = j; ix[2] = k; ix[3] = l;
// suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
tensor(ix)=(ix[dim] != tensor.dimension(dim) - 1)?-1.0:20.0;
}
}
}
}
sycl_device.memcpyHostToDevice(d_in, tensor.data(),in_bytes);
gpu_out.device(sycl_device) = gpu_in.argmax(dim);
sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);
for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
// Expect max to be in the last index of the reduced dimension
VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
}
sycl_device.deallocate(d_in);
sycl_device.deallocate(d_out);
}
}