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.

3 files changed, 211 insertions, 0 deletions

diff --git a/unsupported/test/CMakeLists.txt b/unsupported/test/CMakeLists.txt
index 0ffa329f5..2fe03e24f 100644
--- a/unsupported/test/CMakeLists.txt
+++ b/unsupported/test/CMakeLists.txt
@@ -148,6 +148,7 @@ if(EIGEN_TEST_CXX11)
     ei_add_test_sycl(cxx11_tensor_reduction_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_morphing_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_shuffling_sycl "-std=c++11")
+    ei_add_test_sycl(cxx11_tensor_padding_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_builtins_sycl "-std=c++11")
   endif(EIGEN_TEST_SYCL)
   # It should be safe to always run these tests as there is some fallback code for
diff --git a/unsupported/test/cxx11_tensor_morphing_sycl.cpp b/unsupported/test/cxx11_tensor_morphing_sycl.cpp
index d7f4e8cff..91353b81a 100644
--- a/unsupported/test/cxx11_tensor_morphing_sycl.cpp
+++ b/unsupported/test/cxx11_tensor_morphing_sycl.cpp
@@ -180,6 +180,53 @@ static void test_simple_slice(const Eigen::SyclDevice &sycl_device)
   sycl_device.deallocate(gpu_data3);
 }
 
+template<typename DataType, int DataLayout, typename IndexType>
+static void test_strided_slice_write_sycl(const Eigen::SyclDevice& sycl_device)
+{
+  typedef Tensor<DataType, 2, DataLayout, IndexType> Tensor2f;
+  typedef Eigen::DSizes<IndexType, 2> Index2;
+  IndexType sizeDim1 = 7L;
+  IndexType sizeDim2 = 11L;
+  array<IndexType, 2> tensorRange = {{sizeDim1, sizeDim2}};
+  Tensor<DataType, 2, DataLayout, IndexType> tensor(tensorRange),tensor2(tensorRange);
+  IndexType sliceDim1 = 2;
+  IndexType sliceDim2 = 3;
+  array<IndexType, 2> sliceRange = {{sliceDim1, sliceDim2}};
+  Tensor2f slice(sliceRange);
+  Index2 strides(1L,1L);
+  Index2 indicesStart(3L,4L);
+  Index2 indicesStop(5L,7L);
+  Index2 lengths(2L,3L);
+
+  DataType* gpu_data1  = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType)));
+  DataType* gpu_data2  = static_cast<DataType*>(sycl_device.allocate(tensor2.size()*sizeof(DataType)));
+  DataType* gpu_data3  = static_cast<DataType*>(sycl_device.allocate(slice.size()*sizeof(DataType)));
+  TensorMap<Tensor<DataType, 2,DataLayout,IndexType>> gpu1(gpu_data1, tensorRange);
+  TensorMap<Tensor<DataType, 2,DataLayout,IndexType>> gpu2(gpu_data2, tensorRange);
+  TensorMap<Tensor<DataType, 2,DataLayout,IndexType>> gpu3(gpu_data3, sliceRange);
+
+
+  tensor.setRandom();
+  sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
+  gpu2.device(sycl_device)=gpu1;
+
+  slice.setRandom();
+  sycl_device.memcpyHostToDevice(gpu_data3, slice.data(),(slice.size())*sizeof(DataType));
+
+
+  gpu1.slice(indicesStart,lengths).device(sycl_device)=gpu3;
+  gpu2.stridedSlice(indicesStart,indicesStop,strides).device(sycl_device)=gpu3;
+  sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data1,(tensor.size())*sizeof(DataType));
+  sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor2.size())*sizeof(DataType));
+
+  for(int i=0;i<sizeDim1;i++) for(int j=0;j<sizeDim2;j++){
+    VERIFY_IS_EQUAL(tensor(i,j), tensor2(i,j));
+  }
+  sycl_device.deallocate(gpu_data1);
+  sycl_device.deallocate(gpu_data2);
+  sycl_device.deallocate(gpu_data3);
+}
+
 template<typename DataType, typename dev_Selector> void sycl_morphing_test_per_device(dev_Selector s){
   QueueInterface queueInterface(s);
   auto sycl_device = Eigen::SyclDevice(&queueInterface);
@@ -189,6 +236,8 @@ template<typename DataType, typename dev_Selector> void sycl_morphing_test_per_d
   test_simple_reshape<DataType, ColMajor>(sycl_device);
   test_reshape_as_lvalue<DataType, RowMajor>(sycl_device);
   test_reshape_as_lvalue<DataType, ColMajor>(sycl_device);
+  test_strided_slice_write_sycl<DataType, ColMajor, int64_t>(sycl_device);
+  test_strided_slice_write_sycl<DataType, RowMajor, int64_t>(sycl_device);
 }
 void test_cxx11_tensor_morphing_sycl()
 {
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
@@ -0,0 +1,161 @@
+// 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));
+  }
+}