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authorGravatar Mehdi Goli <mehdi.goli@example.com>2016-12-01 13:02:27 +0000
committerGravatar Mehdi Goli <mehdi.goli@example.com>2016-12-01 13:02:27 +0000
commit79aa2b784ecc26d6a8ef6fb2b2b053f4ad81593b (patch)
tree626e91024c30ad3caa510ca2e06548dbd6ffadce /unsupported/test/cxx11_tensor_padding_sycl.cpp
parenta70393fd02fb56f432c6258ab1744e6d299797e3 (diff)
Adding sycl backend for TensorPadding.h; disbaling __unit128 for sycl in TensorIntDiv.h; disabling cashsize for sycl in tensorDeviceDefault.h; adding sycl backend for StrideSliceOP ; removing sycl compiler warning for creating an array of size 0 in CXX11Meta.h; cleaning up the sycl backend code.
Diffstat (limited to 'unsupported/test/cxx11_tensor_padding_sycl.cpp')
-rw-r--r--unsupported/test/cxx11_tensor_padding_sycl.cpp161
1 files changed, 161 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_padding_sycl.cpp b/unsupported/test/cxx11_tensor_padding_sycl.cpp
new file mode 100644
index 000000000..9e86e4b52
--- /dev/null
+++ b/unsupported/test/cxx11_tensor_padding_sycl.cpp
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+// 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_padding_sycl
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+#define EIGEN_USE_SYCL
+
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::array;
+using Eigen::SyclDevice;
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+
+template<typename DataType, int DataLayout, typename IndexType>
+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 (int i = 0; i < padedSizeDim1; ++i) {
+ for (int j = 0; j < padedSizeDim2; ++j) {
+ for (int k = 0; k < padedSizeDim3; ++k) {
+ for (int 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);
+}
+
+template<typename DataType, int DataLayout, typename IndexType>
+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 (int i = 0; i < 2; ++i) {
+ for (int j = 0; j < 6; ++j) {
+ for (int k = 0; k < 12; ++k) {
+ for (int 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);
+}
+
+template<typename DataType, typename dev_Selector> void sycl_padding_test_per_device(dev_Selector s){
+ QueueInterface queueInterface(s);
+ auto sycl_device = Eigen::SyclDevice(&queueInterface);
+ test_simple_padding<DataType, RowMajor, int>(sycl_device);
+ test_simple_padding<DataType, ColMajor, int>(sycl_device);
+ test_padded_expr<DataType, RowMajor, int>(sycl_device);
+ test_padded_expr<DataType, ColMajor, int>(sycl_device);
+ test_simple_padding<DataType, RowMajor, int64_t>(sycl_device);
+ test_simple_padding<DataType, ColMajor, int64_t>(sycl_device);
+ test_padded_expr<DataType, RowMajor, int64_t>(sycl_device);
+ test_padded_expr<DataType, ColMajor, int64_t>(sycl_device);
+
+}
+void test_cxx11_tensor_padding_sycl()
+{
+ for (const auto& device :Eigen::get_sycl_supported_devices()) {
+ CALL_SUBTEST(sycl_padding_test_per_device<float>(device));
+ }
+}