aboutsummaryrefslogtreecommitdiffhomepage
path: root/unsupported/test/cxx11_tensor_patch_sycl.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'unsupported/test/cxx11_tensor_patch_sycl.cpp')
-rw-r--r--unsupported/test/cxx11_tensor_patch_sycl.cpp249
1 files changed, 249 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_patch_sycl.cpp b/unsupported/test/cxx11_tensor_patch_sycl.cpp
new file mode 100644
index 000000000..88a29cb31
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_patch_sycl.cpp
@@ -0,0 +1,249 @@
+// 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_patch_sycl
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+
+#include <Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+
+template <typename DataType, int DataLayout, typename IndexType>
+static void test_simple_patch_sycl(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}};
+ array<IndexType, 5> patchTensorRange;
+ if (DataLayout == ColMajor) {
+ patchTensorRange = {{1, 1, 1, 1, sizeDim1*sizeDim2*sizeDim3*sizeDim4}};
+ }else{
+ patchTensorRange = {{sizeDim1*sizeDim2*sizeDim3*sizeDim4,1, 1, 1, 1}};
+ }
+
+ Tensor<DataType, 4, DataLayout,IndexType> tensor(tensorRange);
+ Tensor<DataType, 5, DataLayout,IndexType> no_patch(patchTensorRange);
+
+ tensor.setRandom();
+
+ array<ptrdiff_t, 4> patch_dims;
+ patch_dims[0] = 1;
+ patch_dims[1] = 1;
+ patch_dims[2] = 1;
+ patch_dims[3] = 1;
+
+ const size_t tensorBuffSize =tensor.size()*sizeof(DataType);
+ size_t patchTensorBuffSize =no_patch.size()*sizeof(DataType);
+ DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize));
+ DataType* gpu_data_no_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+
+ TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange);
+ TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_no_patch(gpu_data_no_patch, patchTensorRange);
+
+ sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize);
+ gpu_no_patch.device(sycl_device)=gpu_tensor.extract_patches(patch_dims);
+ sycl_device.memcpyDeviceToHost(no_patch.data(), gpu_data_no_patch, patchTensorBuffSize);
+
+ if (DataLayout == ColMajor) {
+ VERIFY_IS_EQUAL(no_patch.dimension(0), 1);
+ VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
+ VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
+ VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
+ VERIFY_IS_EQUAL(no_patch.dimension(4), tensor.size());
+ } else {
+ VERIFY_IS_EQUAL(no_patch.dimension(0), tensor.size());
+ VERIFY_IS_EQUAL(no_patch.dimension(1), 1);
+ VERIFY_IS_EQUAL(no_patch.dimension(2), 1);
+ VERIFY_IS_EQUAL(no_patch.dimension(3), 1);
+ VERIFY_IS_EQUAL(no_patch.dimension(4), 1);
+ }
+
+ for (int i = 0; i < tensor.size(); ++i) {
+ VERIFY_IS_EQUAL(tensor.data()[i], no_patch.data()[i]);
+ }
+
+ patch_dims[0] = 2;
+ patch_dims[1] = 3;
+ patch_dims[2] = 5;
+ patch_dims[3] = 7;
+
+ if (DataLayout == ColMajor) {
+ patchTensorRange = {{sizeDim1,sizeDim2,sizeDim3,sizeDim4,1}};
+ }else{
+ patchTensorRange = {{1,sizeDim1,sizeDim2,sizeDim3,sizeDim4}};
+ }
+ Tensor<DataType, 5, DataLayout,IndexType> single_patch(patchTensorRange);
+ patchTensorBuffSize =single_patch.size()*sizeof(DataType);
+ DataType* gpu_data_single_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+ TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_single_patch(gpu_data_single_patch, patchTensorRange);
+
+ gpu_single_patch.device(sycl_device)=gpu_tensor.extract_patches(patch_dims);
+ sycl_device.memcpyDeviceToHost(single_patch.data(), gpu_data_single_patch, patchTensorBuffSize);
+
+ if (DataLayout == ColMajor) {
+ VERIFY_IS_EQUAL(single_patch.dimension(0), 2);
+ VERIFY_IS_EQUAL(single_patch.dimension(1), 3);
+ VERIFY_IS_EQUAL(single_patch.dimension(2), 5);
+ VERIFY_IS_EQUAL(single_patch.dimension(3), 7);
+ VERIFY_IS_EQUAL(single_patch.dimension(4), 1);
+ } else {
+ VERIFY_IS_EQUAL(single_patch.dimension(0), 1);
+ VERIFY_IS_EQUAL(single_patch.dimension(1), 2);
+ VERIFY_IS_EQUAL(single_patch.dimension(2), 3);
+ VERIFY_IS_EQUAL(single_patch.dimension(3), 5);
+ VERIFY_IS_EQUAL(single_patch.dimension(4), 7);
+ }
+
+ for (int i = 0; i < tensor.size(); ++i) {
+ VERIFY_IS_EQUAL(tensor.data()[i], single_patch.data()[i]);
+ }
+ patch_dims[0] = 1;
+ patch_dims[1] = 2;
+ patch_dims[2] = 2;
+ patch_dims[3] = 1;
+
+ if (DataLayout == ColMajor) {
+ patchTensorRange = {{1,2,2,1,2*2*4*7}};
+ }else{
+ patchTensorRange = {{2*2*4*7, 1, 2,2,1}};
+ }
+ Tensor<DataType, 5, DataLayout,IndexType> twod_patch(patchTensorRange);
+ patchTensorBuffSize =twod_patch.size()*sizeof(DataType);
+ DataType* gpu_data_twod_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+ TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_twod_patch(gpu_data_twod_patch, patchTensorRange);
+
+ gpu_twod_patch.device(sycl_device)=gpu_tensor.extract_patches(patch_dims);
+ sycl_device.memcpyDeviceToHost(twod_patch.data(), gpu_data_twod_patch, patchTensorBuffSize);
+
+ if (DataLayout == ColMajor) {
+ VERIFY_IS_EQUAL(twod_patch.dimension(0), 1);
+ VERIFY_IS_EQUAL(twod_patch.dimension(1), 2);
+ VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
+ VERIFY_IS_EQUAL(twod_patch.dimension(3), 1);
+ VERIFY_IS_EQUAL(twod_patch.dimension(4), 2*2*4*7);
+ } else {
+ VERIFY_IS_EQUAL(twod_patch.dimension(0), 2*2*4*7);
+ VERIFY_IS_EQUAL(twod_patch.dimension(1), 1);
+ VERIFY_IS_EQUAL(twod_patch.dimension(2), 2);
+ VERIFY_IS_EQUAL(twod_patch.dimension(3), 2);
+ VERIFY_IS_EQUAL(twod_patch.dimension(4), 1);
+ }
+
+ for (int i = 0; i < 2; ++i) {
+ for (int j = 0; j < 2; ++j) {
+ for (int k = 0; k < 4; ++k) {
+ for (int l = 0; l < 7; ++l) {
+ int patch_loc;
+ if (DataLayout == ColMajor) {
+ patch_loc = i + 2 * (j + 2 * (k + 4 * l));
+ } else {
+ patch_loc = l + 7 * (k + 4 * (j + 2 * i));
+ }
+ for (int x = 0; x < 2; ++x) {
+ for (int y = 0; y < 2; ++y) {
+ if (DataLayout == ColMajor) {
+ VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l), twod_patch(0,x,y,0,patch_loc));
+ } else {
+ VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l), twod_patch(patch_loc,0,x,y,0));
+ }
+ }
+ }
+ }
+ }
+ }
+ }
+
+ patch_dims[0] = 1;
+ patch_dims[1] = 2;
+ patch_dims[2] = 3;
+ patch_dims[3] = 5;
+
+ if (DataLayout == ColMajor) {
+ patchTensorRange = {{1,2,3,5,2*2*3*3}};
+ }else{
+ patchTensorRange = {{2*2*3*3, 1, 2,3,5}};
+ }
+ Tensor<DataType, 5, DataLayout,IndexType> threed_patch(patchTensorRange);
+ patchTensorBuffSize =threed_patch.size()*sizeof(DataType);
+ DataType* gpu_data_threed_patch = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
+ TensorMap<Tensor<DataType, 5, DataLayout,IndexType>> gpu_threed_patch(gpu_data_threed_patch, patchTensorRange);
+
+ gpu_threed_patch.device(sycl_device)=gpu_tensor.extract_patches(patch_dims);
+ sycl_device.memcpyDeviceToHost(threed_patch.data(), gpu_data_threed_patch, patchTensorBuffSize);
+
+ if (DataLayout == ColMajor) {
+ VERIFY_IS_EQUAL(threed_patch.dimension(0), 1);
+ VERIFY_IS_EQUAL(threed_patch.dimension(1), 2);
+ VERIFY_IS_EQUAL(threed_patch.dimension(2), 3);
+ VERIFY_IS_EQUAL(threed_patch.dimension(3), 5);
+ VERIFY_IS_EQUAL(threed_patch.dimension(4), 2*2*3*3);
+ } else {
+ VERIFY_IS_EQUAL(threed_patch.dimension(0), 2*2*3*3);
+ VERIFY_IS_EQUAL(threed_patch.dimension(1), 1);
+ VERIFY_IS_EQUAL(threed_patch.dimension(2), 2);
+ VERIFY_IS_EQUAL(threed_patch.dimension(3), 3);
+ VERIFY_IS_EQUAL(threed_patch.dimension(4), 5);
+ }
+
+ for (int i = 0; i < 2; ++i) {
+ for (int j = 0; j < 2; ++j) {
+ for (int k = 0; k < 3; ++k) {
+ for (int l = 0; l < 3; ++l) {
+ int patch_loc;
+ if (DataLayout == ColMajor) {
+ patch_loc = i + 2 * (j + 2 * (k + 3 * l));
+ } else {
+ patch_loc = l + 3 * (k + 3 * (j + 2 * i));
+ }
+ for (int x = 0; x < 2; ++x) {
+ for (int y = 0; y < 3; ++y) {
+ for (int z = 0; z < 5; ++z) {
+ if (DataLayout == ColMajor) {
+ VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l+z), threed_patch(0,x,y,z,patch_loc));
+ } else {
+ VERIFY_IS_EQUAL(tensor(i,j+x,k+y,l+z), threed_patch(patch_loc,0,x,y,z));
+ }
+ }
+ }
+ }
+ }
+ }
+ }
+ }
+ sycl_device.deallocate(gpu_data_tensor);
+ sycl_device.deallocate(gpu_data_no_patch);
+ sycl_device.deallocate(gpu_data_single_patch);
+ sycl_device.deallocate(gpu_data_twod_patch);
+ sycl_device.deallocate(gpu_data_threed_patch);
+}
+
+template<typename DataType, typename dev_Selector> void sycl_tensor_patch_test_per_device(dev_Selector s){
+ QueueInterface queueInterface(s);
+ auto sycl_device = Eigen::SyclDevice(&queueInterface);
+ test_simple_patch_sycl<DataType, RowMajor, int64_t>(sycl_device);
+ test_simple_patch_sycl<DataType, ColMajor, int64_t>(sycl_device);
+}
+void test_cxx11_tensor_patch_sycl()
+{
+ for (const auto& device :Eigen::get_sycl_supported_devices()) {
+ CALL_SUBTEST(sycl_tensor_patch_test_per_device<float>(device));
+ }
+}