Adding TensorVolumePatchOP.h for sycl

unsupported/Eigen/CXX11/src/Tensor/TensorSyclConvertToDeviceExpression.h

@@ -177,6 +177,16 @@ KERNELBROKERCONVERTIMAGEPATCHOP()
 #undef KERNELBROKERCONVERTIMAGEPATCHOP
 
 
+/// specialisation of the \ref ConvertToDeviceExpression struct when the node type is TensorVolumePatchOp
+#define KERNELBROKERCONVERTVOLUMEPATCHOP(CVQual)\
+template<DenseIndex Plannes, DenseIndex Rows, DenseIndex Cols, typename XprType>\
+struct ConvertToDeviceExpression<CVQual TensorVolumePatchOp<Plannes, Rows, Cols, XprType> >{\
+  typedef CVQual TensorVolumePatchOp<Plannes, Rows, Cols, typename ConvertToDeviceExpression<XprType>::Type> Type;\
+};
+KERNELBROKERCONVERTVOLUMEPATCHOP(const)
+KERNELBROKERCONVERTVOLUMEPATCHOP()
+#undef KERNELBROKERCONVERTVOLUMEPATCHOP
+
 
 
 }  // namespace internal

unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h

@@ -404,6 +404,28 @@ SYCLTENSORIMAGEPATCHOPEXPR(const)
 SYCLTENSORIMAGEPATCHOPEXPR()
 #undef SYCLTENSORIMAGEPATCHOPEXPR
 
+// TensorVolumePatchOp
+#define SYCLTENSORVOLUMEPATCHOPEXPR(CVQual)\
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename OrigXprType, typename XprType, typename... Params>\
+struct  ExprConstructor<CVQual TensorVolumePatchOp<Planes, Rows, Cols, OrigXprType>, CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Params... > {\
+  typedef ExprConstructor<OrigXprType, XprType, Params...> my_xpr_type;\
+  typedef CVQual TensorVolumePatchOp<Planes, Rows, Cols, typename my_xpr_type::Type> Type;\
+  my_xpr_type xprExpr;\
+  Type expr;\
+  template <typename FuncDetector>\
+  ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\
+  : xprExpr(funcD.xprExpr, t), expr(xprExpr.expr, funcD.m_patch_planes, funcD.m_patch_rows, funcD.m_patch_cols, funcD.m_plane_strides, funcD.m_row_strides, funcD.m_col_strides,\
+    funcD.m_in_plane_strides, funcD.m_in_row_strides, funcD.m_in_col_strides,funcD.m_plane_inflate_strides, funcD.m_row_inflate_strides, funcD.m_col_inflate_strides, \
+    funcD.m_padding_top_z, funcD.m_padding_bottom_z, funcD.m_padding_top, funcD.m_padding_bottom, funcD.m_padding_left, funcD.m_padding_right, funcD.m_padding_value,\
+    funcD.m_padding_type, funcD.m_padding_explicit){\
+    }\
+};
+
+SYCLTENSORVOLUMEPATCHOPEXPR(const)
+SYCLTENSORVOLUMEPATCHOPEXPR()
+#undef SYCLTENSORVOLUMEPATCHOPEXPR
+
+
 
 // TensorLayoutSwapOp
 #define SYCLTENSORLAYOUTSWAPOPEXPR(CVQual)\

unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h

@@ -240,6 +240,21 @@ SYCLTENSORIMAGEPATCHOPEXTACC()
 #undef SYCLTENSORIMAGEPATCHOPEXTACC
 
 
+
+// specialisation of the \ref ExtractAccessor struct when the node type is
+/// TensorVolumePatchOp.
+#define SYCLTENSORVOLUMEPATCHOPEXTACC(CVQual)\
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType, typename Dev>\
+struct ExtractAccessor<TensorEvaluator<CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Dev> >{\
+  static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Dev>& eval)\
+  RETURN_CPP11(AccessorConstructor::getTuple(cgh, eval.impl()))\
+};
+
+SYCLTENSORVOLUMEPATCHOPEXTACC(const)
+SYCLTENSORVOLUMEPATCHOPEXTACC()
+#undef SYCLTENSORVOLUMEPATCHOPEXTACC
+
+
 // specialisation of the \ref ExtractAccessor struct when the node type is
 /// TensorLayoutSwapOp.
 #define SYCLTENSORLAYOUTSWAPOPEXTACC(CVQual)\

unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h

@@ -344,6 +344,50 @@ FunctorExtractor(const TensorEvaluator<Self, Device>& expr)\
 SYCLEXTRFUNCIMAGEPATCHOP(const)
 SYCLEXTRFUNCIMAGEPATCHOP()
 #undef SYCLEXTRFUNCIMAGEPATCHOP
+
+/// TensorVolumePatchOp
+#define SYCLEXTRFUNCVOLUMEPATCHOP(CVQual)\
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType, typename Device>\
+struct FunctorExtractor<TensorEvaluator<CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Device> >{\
+typedef CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType> Self;\
+FunctorExtractor<Eigen::TensorEvaluator<XprType, Device> > xprExpr;\
+const DenseIndex m_patch_planes;\
+const DenseIndex m_patch_rows;\
+const DenseIndex m_patch_cols;\
+const DenseIndex m_plane_strides;\
+const DenseIndex m_row_strides;\
+const DenseIndex m_col_strides;\
+const DenseIndex m_in_plane_strides;\
+const DenseIndex m_in_row_strides;\
+const DenseIndex m_in_col_strides;\
+const DenseIndex m_plane_inflate_strides;\
+const DenseIndex m_row_inflate_strides;\
+const DenseIndex m_col_inflate_strides;\
+const bool m_padding_explicit;\
+const DenseIndex m_padding_top_z;\
+const DenseIndex m_padding_bottom_z;\
+const DenseIndex m_padding_top;\
+const DenseIndex m_padding_bottom;\
+const DenseIndex m_padding_left;\
+const DenseIndex m_padding_right;\
+const PaddingType m_padding_type;\
+const typename Self::Scalar m_padding_value;\
+FunctorExtractor(const TensorEvaluator<Self, Device>& expr)\
+: xprExpr(expr.impl()), m_patch_planes(expr.xpr().patch_planes()), m_patch_rows(expr.xpr().patch_rows()), m_patch_cols(expr.xpr().patch_cols()),\
+  m_plane_strides(expr.xpr().plane_strides()), m_row_strides(expr.xpr().row_strides()), m_col_strides(expr.xpr().col_strides()),\
+  m_in_plane_strides(expr.xpr().in_plane_strides()), m_in_row_strides(expr.xpr().in_row_strides()), m_in_col_strides(expr.xpr().in_col_strides()),\
+  m_plane_inflate_strides(expr.xpr().plane_inflate_strides()),m_row_inflate_strides(expr.xpr().row_inflate_strides()),\
+  m_col_inflate_strides(expr.xpr().col_inflate_strides()), m_padding_explicit(expr.xpr().padding_explicit()),\
+  m_padding_top_z(expr.xpr().padding_top_z()), m_padding_bottom_z(expr.xpr().padding_bottom_z()), \
+  m_padding_top(expr.xpr().padding_top()), m_padding_bottom(expr.xpr().padding_bottom()), m_padding_left(expr.xpr().padding_left()),\
+  m_padding_right(expr.xpr().padding_right()), m_padding_type(expr.xpr().padding_type()),m_padding_value(expr.xpr().padding_value()){}\
+};
+SYCLEXTRFUNCVOLUMEPATCHOP(const)
+SYCLEXTRFUNCVOLUMEPATCHOP()
+#undef SYCLEXTRFUNCVOLUMEPATCHOP
+
+
+
 /// template deduction function for FunctorExtractor
 template <typename Evaluator>
 auto inline extractFunctors(const Evaluator& evaluator)-> FunctorExtractor<Evaluator> {

unsupported/Eigen/CXX11/src/Tensor/TensorSyclLeafCount.h

@@ -151,7 +151,7 @@ CHIPPINGOPLEAFCOUNT(const)
 CHIPPINGOPLEAFCOUNT()
 #undef CHIPPINGOPLEAFCOUNT
 
-
+///TensorStridingSlicingOp
 #define SLICESTRIDEOPLEAFCOUNT(CVQual)\
 template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>\
 struct LeafCount<CVQual TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> >:CategoryCount<XprType>{};
@@ -160,7 +160,7 @@ SLICESTRIDEOPLEAFCOUNT(const)
 SLICESTRIDEOPLEAFCOUNT()
 #undef SLICESTRIDEOPLEAFCOUNT
 
-
+//TensorImagePatchOp
 #define TENSORIMAGEPATCHOPLEAFCOUNT(CVQual)\
 template<DenseIndex Rows, DenseIndex Cols, typename XprType>\
 struct LeafCount<CVQual TensorImagePatchOp<Rows, Cols, XprType> >:CategoryCount<XprType>{};
@@ -170,6 +170,15 @@ TENSORIMAGEPATCHOPLEAFCOUNT(const)
 TENSORIMAGEPATCHOPLEAFCOUNT()
 #undef TENSORIMAGEPATCHOPLEAFCOUNT
 
+// TensorVolumePatchOp
+#define TENSORVOLUMEPATCHOPLEAFCOUNT(CVQual)\
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>\
+struct LeafCount<CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType> >:CategoryCount<XprType>{};
+
+
+TENSORVOLUMEPATCHOPLEAFCOUNT(const)
+TENSORVOLUMEPATCHOPLEAFCOUNT()
+#undef TENSORVOLUMEPATCHOPLEAFCOUNT
 
 } /// namespace TensorSycl
 } /// namespace internal

unsupported/Eigen/CXX11/src/Tensor/TensorSyclPlaceHolderExpr.h

@@ -235,6 +235,20 @@ SYCLTENSORIMAGEPATCHOP()
 #undef SYCLTENSORIMAGEPATCHOP
 
 
+
+/// specialisation of the \ref PlaceHolderExpression when the node is
+/// TensorVolumePatchOp
+#define SYCLTENSORVOLUMEPATCHOP(CVQual)\
+template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType, size_t N>\
+struct PlaceHolderExpression<CVQual TensorVolumePatchOp<Planes,Rows, Cols, XprType>, N> {\
+  typedef CVQual TensorVolumePatchOp<Planes,Rows, Cols, typename CalculateIndex <N, XprType>::ArgType> Type;\
+};
+
+SYCLTENSORVOLUMEPATCHOP(const)
+SYCLTENSORVOLUMEPATCHOP()
+#undef SYCLTENSORVOLUMEPATCHOP
+
+
 /// template deduction for \ref PlaceHolderExpression struct
 template <typename Expr>
 struct createPlaceHolderExpression {

unsupported/Eigen/CXX11/src/Tensor/TensorVolumePatch.h

@@ -65,12 +65,8 @@ class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows,
                                                             DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides,
                                                             DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
                                                             PaddingType padding_type, Scalar padding_value)
-      : m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
+      : TensorVolumePatchOp(expr, patch_planes, patch_rows, patch_cols, plane_strides, row_strides, col_strides, in_plane_strides, in_row_strides, in_col_strides,
-        m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
+      plane_inflate_strides, row_inflate_strides, col_inflate_strides, 0,0,0,0,0,0,padding_value, padding_type, false) {}
-        m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
-        m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
-        m_padding_explicit(false), m_padding_top_z(0), m_padding_bottom_z(0), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
-        m_padding_type(padding_type), m_padding_value(padding_value) {}
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
                                                            DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides,
@@ -79,14 +75,14 @@ class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows,
                                                            DenseIndex padding_top_z, DenseIndex padding_bottom_z,
                                                            DenseIndex padding_top, DenseIndex padding_bottom,
                                                            DenseIndex padding_left, DenseIndex padding_right,
-                                                           Scalar padding_value)
+                                                           Scalar padding_value, PaddingType padding_type=PADDING_VALID, bool padding_explicit=true)
       : m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
         m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
         m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
         m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
-        m_padding_explicit(true), m_padding_top_z(padding_top_z), m_padding_bottom_z(padding_bottom_z), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
+        m_padding_explicit(padding_explicit), m_padding_top_z(padding_top_z), m_padding_bottom_z(padding_bottom_z), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
         m_padding_left(padding_left), m_padding_right(padding_right),
-        m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
+        m_padding_type(padding_type), m_padding_value(padding_value) {}
 
     EIGEN_DEVICE_FUNC
     DenseIndex patch_planes() const { return m_patch_planes; }
@@ -183,9 +179,13 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
     CoordAccess = false,
     RawAccess = false
   };
+#ifdef __SYCL_DEVICE_ONLY__
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator( const XprType op, const Device& device)
+#else
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator( const XprType& op, const Device& device)
+#endif
 
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
+      : m_impl(op.expression(), device), m_op(op)
-      : m_impl(op.expression(), device)
   {
     EIGEN_STATIC_ASSERT((NumDims >= 5), YOU_MADE_A_PROGRAMMING_MISTAKE);
 
@@ -321,7 +321,9 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
     m_outputPlanesRows = m_outputPlanes * m_outputRows;
 
     // Fast representations of different variables.
+  //  printf("THis is m_otherStride: %lu\n", m_otherStride );
     m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
+
     m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
     m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
     m_fastRowStride = internal::TensorIntDivisor<Index>(m_rowStride);
@@ -338,7 +340,6 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
       m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
     }
   }
-
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
@@ -352,6 +353,7 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
 
   EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
   {
+
     // Patch index corresponding to the passed in index.
     const Index patchIndex = index / m_fastPatchStride;
 
@@ -505,6 +507,8 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
   EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
 
   const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
+  // required by sycl
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const XprType& xpr() const { return m_op; }
 
   Index planePaddingTop() const { return m_planePaddingTop; }
   Index rowPaddingTop() const { return m_rowPaddingTop; }
@@ -600,6 +604,8 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
   Scalar m_paddingValue;
 
   TensorEvaluator<ArgType, Device> m_impl;
+// required by sycl
+  XprType m_op;
 };
 
 

unsupported/test/CMakeLists.txt

@@ -167,11 +167,12 @@ if(EIGEN_TEST_CXX11)
     ei_add_test_sycl(cxx11_tensor_convolution_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_striding_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_chipping_sycl "-std=c++11")
-    ei_add_test_sycl(cxx11_tensor_patch_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_layout_swap_sycl "-std=c++11")
-    ei_add_test_sycl(cxx11_tensor_image_patchOP_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_inflation_sycl "-std=c++11")
     ei_add_test_sycl(cxx11_tensor_generator_sycl "-std=c++11")
+    ei_add_test_sycl(cxx11_tensor_patch_sycl "-std=c++11")
+    ei_add_test_sycl(cxx11_tensor_image_patchOP_sycl "-std=c++11")
+    ei_add_test_sycl(cxx11_tensor_volume_patchOP_sycl "-std=c++11")
   endif(EIGEN_TEST_SYCL)
   # It should be safe to always run these tests as there is some fallback code for
   # older compiler that don't support cxx11.

unsupported/test/cxx11_tensor_volume_patchOP_sycl.cpp

@@ -0,0 +1,222 @@
+// 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>
+//
+// 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_volume_patchOP_sycl
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
+#define EIGEN_USE_SYCL
+
+#include "main.h"
+#include <unsupported/Eigen/CXX11/Tensor>
+
+using Eigen::Tensor;
+static const int DataLayout = ColMajor;
+
+template <typename DataType, typename IndexType>
+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);
+}
+
+template <typename DataType, typename IndexType>
+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);
+}
+
+
+
+template<typename DataType, typename dev_Selector> void sycl_tensor_volume_patch_test_per_device(dev_Selector s){
+QueueInterface queueInterface(s);
+auto sycl_device = Eigen::SyclDevice(&queueInterface);
+std::cout << "Running on " << s.template get_info<cl::sycl::info::device::name>() << std::endl;
+test_single_voxel_patch_sycl<DataType, int64_t>(sycl_device);
+test_entire_volume_patch_sycl<DataType, int64_t>(sycl_device);
+}
+void test_cxx11_tensor_volume_patchOP_sycl()
+{
+for (const auto& device :Eigen::get_sycl_supported_devices()) {
+  CALL_SUBTEST(sycl_tensor_volume_patch_test_per_device<float>(device));
+}
+}