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eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
Mehdi Goli 77cc4d06c7 Removing unused variables
2017-01-19 17:06:21 +00:00

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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Mehdi Goli Codeplay Software Ltd.
// Ralph Potter Codeplay Software Ltd.
// Luke Iwanski Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
// Copyright (C) 2016 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/.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H
namespace Eigen {
/** \class TensorConvolution
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor convolution class.
*
*
*/
template <typename CoeffReturnType, typename KernelType, typename HostExpr, typename FunctorExpr, typename Index,
typename InputDims, typename Kernel_accessor, typename Buffer_accessor, typename Local_accessor, typename TupleType>
struct EigenConvolutionKernel1D{
typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
internal::IndexMapper<Index, InputDims, 1, Eigen::internal::traits<HostExpr>::Layout> indexMapper;
Kernel_accessor kernel_filter;
const size_t kernelSize, range_x, range_y;
Buffer_accessor buffer_acc;
Local_accessor local_acc;
FunctorExpr functors;
TupleType tuple_of_accessors;
EigenConvolutionKernel1D(internal::IndexMapper<Index, InputDims, 1, Eigen::internal::traits<HostExpr>::Layout> indexMapper_,
Kernel_accessor kernel_filter_, const size_t kernelSize_, const size_t range_x_, const size_t range_y_,
Buffer_accessor buffer_acc_, Local_accessor local_acc_, FunctorExpr functors_, TupleType tuple_of_accessors_)
:indexMapper(indexMapper_), kernel_filter(kernel_filter_), kernelSize(kernelSize_), range_x(range_x_), range_y(range_y_),
buffer_acc(buffer_acc_), local_acc(local_acc_), functors(functors_), tuple_of_accessors(tuple_of_accessors_) {}
void operator()(cl::sycl::nd_item<2> itemID) {
typedef typename TensorSycl::internal::ConvertToDeviceExpression<HostExpr>::Type DevExpr;
auto device_expr =TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
auto device_evaluator = Eigen::TensorEvaluator<DevExpr, Eigen::DefaultDevice>(device_expr.expr, Eigen::DefaultDevice());
auto buffer_ptr = ConvertToActualTypeSycl(CoeffReturnType, buffer_acc);
auto kernel_ptr = ConvertToActualTypeSycl(KernelType, kernel_filter);
const size_t num_x_input = (itemID.get_local_range()[0] +kernelSize -1); //the required row to be calculated for the for each plane in shered memory
const size_t plane_kernel_offset = itemID.get_local(1) * num_x_input;
const size_t first_input_start = itemID.get_group(0)*itemID.get_local_range()[0];
const size_t plane_tensor_offset =indexMapper.mapCudaInputPlaneToTensorInputOffset(itemID.get_global(1));
/// fill the shared memory
for (size_t i = itemID.get_local(0); i < num_x_input ; i += itemID.get_local_range()[0]) {
const size_t local_index = i + plane_kernel_offset ;
const size_t tensor_index = plane_tensor_offset + indexMapper.mapCudaInputKernelToTensorInputOffset(i + first_input_start);
if(((i + first_input_start) < (range_x +kernelSize-1)) && itemID.get_global(1)< range_y){
local_acc[local_index] = device_evaluator.coeff(tensor_index);
}
else local_acc[local_index]=0.0f;
}
itemID.barrier(cl::sycl::access::fence_space::local_space);
// calculate the convolution
const int first_output_start =itemID.get_group(0)*(itemID.get_local_range()[0]); // output start x
if(itemID.get_global(0)< range_x && itemID.get_global(1)< range_y){
CoeffReturnType result = static_cast<CoeffReturnType>(0);
const size_t index = plane_kernel_offset+ itemID.get_local(0);
for (size_t k = 0; k < kernelSize; ++k) {
result += (local_acc[k + index] * kernel_ptr[k]);
}
const size_t tensor_index = indexMapper.mapCudaOutputPlaneToTensorOutputOffset(itemID.get_global(1))
+indexMapper.mapCudaOutputKernelToTensorOutputOffset(itemID.get_local(0) + first_output_start);
buffer_ptr[tensor_index] = result;
}
}
};
template <typename CoeffReturnType, typename KernelType, typename HostExpr, typename FunctorExpr, typename Index,
typename InputDims, typename Kernel_accessor, typename Buffer_accessor, typename Local_accessor, typename TupleType>
struct EigenConvolutionKernel2D{
typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
internal::IndexMapper<Index, InputDims, 2, Eigen::internal::traits<HostExpr>::Layout> indexMapper;
Kernel_accessor kernel_filter;
const size_t kernelSize_x, kernelSize_y, range_x, range_y , range_z;
Buffer_accessor buffer_acc;
Local_accessor local_acc;
FunctorExpr functors;
TupleType tuple_of_accessors;
EigenConvolutionKernel2D(internal::IndexMapper<Index, InputDims, 2, Eigen::internal::traits<HostExpr>::Layout> indexMapper_,
Kernel_accessor kernel_filter_, const size_t kernelSize_x_, const size_t kernelSize_y_ ,const size_t range_x_, const size_t range_y_, const size_t range_z_,
Buffer_accessor buffer_acc_, Local_accessor local_acc_, FunctorExpr functors_, TupleType tuple_of_accessors_)
:indexMapper(indexMapper_), kernel_filter(kernel_filter_), kernelSize_x(kernelSize_x_), kernelSize_y(kernelSize_y_), range_x(range_x_), range_y(range_y_), range_z(range_z_),
buffer_acc(buffer_acc_), local_acc(local_acc_), functors(functors_), tuple_of_accessors(tuple_of_accessors_) {}
void operator()(cl::sycl::nd_item<3> itemID) {
typedef typename TensorSycl::internal::ConvertToDeviceExpression<HostExpr>::Type DevExpr;
auto device_expr =TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
auto device_evaluator = Eigen::TensorEvaluator<DevExpr, Eigen::DefaultDevice>(device_expr.expr, Eigen::DefaultDevice());
auto buffer_ptr = ConvertToActualTypeSycl(CoeffReturnType, buffer_acc);
auto kernel_ptr = ConvertToActualTypeSycl(KernelType, kernel_filter);
const size_t num_x_input = (itemID.get_local_range()[0] +kernelSize_x -1); //the required row to be calculated for the for each plane in shered memory
const size_t num_y_input = (itemID.get_local_range()[1] +kernelSize_y -1); //the required row to be calculated for the for each plane in shered memory
const size_t plane_input_offset = indexMapper.mapCudaInputPlaneToTensorInputOffset(itemID.get_global(2));
const size_t plane_kernel_offset = itemID.get_local(2) * num_y_input;
/// fill the shared memory
const size_t first_x_input_start = itemID.get_group(0)*itemID.get_local_range()[0];
const size_t first_y_input_start = itemID.get_group(1)*itemID.get_local_range()[1];
for (size_t j = itemID.get_local(1); j < num_y_input; j += itemID.get_local_range()[1]) {
const size_t local_input_offset = num_x_input * (j + plane_kernel_offset);
for (size_t i = itemID.get_local(0); i < num_x_input ; i += itemID.get_local_range()[0]) {
const size_t local_index = i + local_input_offset;
const size_t tensor_index = plane_input_offset + indexMapper.mapCudaInputKernelToTensorInputOffset(i + first_x_input_start, j+ first_y_input_start );
if(((i + first_x_input_start) < (range_x +kernelSize_x-1)) &&((j + first_y_input_start) < (range_y +kernelSize_y-1)) && itemID.get_global(2)< range_z){
local_acc[local_index] = device_evaluator.coeff(tensor_index);
}
else local_acc[local_index]=0.0f;
}
}
itemID.barrier(cl::sycl::access::fence_space::local_space);
// calculate the convolution
const size_t fitst_x_output_start =itemID.get_group(0)*(itemID.get_local_range()[0]); // output start x
const size_t fitst_y_output_start =itemID.get_group(1)*(itemID.get_local_range()[1]); // output start y
if(itemID.get_global(0)< range_x && itemID.get_global(1)< range_y && itemID.get_global(2)< range_z){
CoeffReturnType result = static_cast<CoeffReturnType>(0);
for (size_t j = 0; j < kernelSize_y; j++) {
size_t kernel_offset =kernelSize_x * j;
const size_t index = (num_x_input*(plane_kernel_offset + j+ itemID.get_local(1))) + itemID.get_local(0);
for (size_t i = 0; i < kernelSize_x; i++) {
result += (local_acc[i + index] * kernel_ptr[i+kernel_offset]);
}
}
const size_t tensor_index = indexMapper.mapCudaOutputPlaneToTensorOutputOffset(itemID.get_global(2))
+indexMapper.mapCudaOutputKernelToTensorOutputOffset(itemID.get_local(0) + fitst_x_output_start, itemID.get_local(1) + fitst_y_output_start);
buffer_ptr[tensor_index] = result;
}
}
};
template <typename CoeffReturnType, typename KernelType, typename HostExpr, typename FunctorExpr, typename Index,
typename InputDims, typename Kernel_accessor, typename Buffer_accessor, typename Local_accessor, typename TupleType>
struct EigenConvolutionKernel3D{
typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
internal::IndexMapper<Index, InputDims, 3, Eigen::internal::traits<HostExpr>::Layout> indexMapper;
Kernel_accessor kernel_filter;
const size_t kernelSize_x, kernelSize_y, kernelSize_z, range_x, range_y , range_z, numP;
Buffer_accessor buffer_acc;
Local_accessor local_acc;
FunctorExpr functors;
TupleType tuple_of_accessors;
EigenConvolutionKernel3D(internal::IndexMapper<Index, InputDims, 3, Eigen::internal::traits<HostExpr>::Layout> indexMapper_,
Kernel_accessor kernel_filter_, const size_t kernelSize_x_, const size_t kernelSize_y_ , const size_t kernelSize_z_ ,
const size_t range_x_, const size_t range_y_, const size_t range_z_, const size_t numP_,
Buffer_accessor buffer_acc_, Local_accessor local_acc_, FunctorExpr functors_, TupleType tuple_of_accessors_)
:indexMapper(indexMapper_), kernel_filter(kernel_filter_), kernelSize_x(kernelSize_x_), kernelSize_y(kernelSize_y_),
kernelSize_z(kernelSize_z_), range_x(range_x_), range_y(range_y_), range_z(range_z_), numP(numP_),
buffer_acc(buffer_acc_), local_acc(local_acc_), functors(functors_), tuple_of_accessors(tuple_of_accessors_) {}
void operator()(cl::sycl::nd_item<3> itemID) {
typedef typename TensorSycl::internal::ConvertToDeviceExpression<HostExpr>::Type DevExpr;
auto device_expr =TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
auto device_evaluator = Eigen::TensorEvaluator<DevExpr, Eigen::DefaultDevice>(device_expr.expr, Eigen::DefaultDevice());
auto buffer_ptr = ConvertToActualTypeSycl(CoeffReturnType, buffer_acc);
auto kernel_ptr = ConvertToActualTypeSycl(KernelType, kernel_filter);
const size_t num_x_input = (itemID.get_local_range()[0] +kernelSize_x -1); //the required row to be calculated for the for each plane in shered memory
const size_t num_y_input = (itemID.get_local_range()[1] +kernelSize_y -1); //the required row to be calculated for the for each plane in shered memory
const size_t num_z_input = (itemID.get_local_range()[2] +kernelSize_z -1); //the required row to be calculated for the for each plane in shered memory
const size_t first_x_input_start = itemID.get_group(0)*itemID.get_local_range()[0];
const size_t first_y_input_start = itemID.get_group(1)*itemID.get_local_range()[1];
const size_t first_z_input_start = itemID.get_group(2)*itemID.get_local_range()[2];
for(size_t p=0; p<numP; p++){
/// fill the shared memory
const size_t plane_input_offset = indexMapper.mapCudaInputPlaneToTensorInputOffset(p);
for (size_t k = itemID.get_local(2); k < num_z_input; k += itemID.get_local_range()[2]) {
for (size_t j = itemID.get_local(1); j < num_y_input; j += itemID.get_local_range()[1]) {
for (size_t i = itemID.get_local(0); i < num_x_input ; i += itemID.get_local_range()[0]) {
const size_t local_index = i + (num_x_input * (j + (num_y_input * k)));
const size_t tensor_index = plane_input_offset + indexMapper.mapCudaInputKernelToTensorInputOffset(i + first_x_input_start, j+ first_y_input_start , k+ first_z_input_start );
if(((i + first_x_input_start) < (range_x +kernelSize_x-1)) && ((j + first_y_input_start) < (range_y +kernelSize_y-1)) && ((k + first_z_input_start) < (range_z +kernelSize_z-1)) ){
local_acc[local_index] = device_evaluator.coeff(tensor_index);
}
else local_acc[local_index]=0.0f;
}
}
}
itemID.barrier(cl::sycl::access::fence_space::local_space);
// calculate the convolution
const size_t fitst_x_output_start =itemID.get_group(0)*(itemID.get_local_range()[0]); // x
const size_t fitst_y_output_start =itemID.get_group(1)*(itemID.get_local_range()[1]); // y
const size_t fitst_z_output_start =itemID.get_group(2)*(itemID.get_local_range()[2]); // z
if(itemID.get_global(0)< range_x && itemID.get_global(1)< range_y && itemID.get_global(2)< range_z){
CoeffReturnType result = static_cast<CoeffReturnType>(0);
for (size_t k = 0; k < kernelSize_z; k++) {
for (size_t j = 0; j < kernelSize_y; j++) {
for (size_t i = 0; i < kernelSize_x; i++) {
const size_t kernel_index =i + kernelSize_x * (j + kernelSize_y * k);
const size_t local_index = ((i+ itemID.get_local(0))+ num_x_input*((j+ itemID.get_local(1)) + num_y_input * (k+ itemID.get_local(2))));
result += (local_acc[local_index] * kernel_ptr[kernel_index]);
}
}
}
const size_t tensor_index = indexMapper.mapCudaOutputPlaneToTensorOutputOffset(p)
+indexMapper.mapCudaOutputKernelToTensorOutputOffset(itemID.get_local(0) + fitst_x_output_start, itemID.get_local(1) + fitst_y_output_start, itemID.get_local(2) + fitst_z_output_start );
buffer_ptr[tensor_index] = result;
}
itemID.barrier(cl::sycl::access::fence_space::local_space);
}
}
};
template<typename Indices, typename InputArgType, typename KernelArgType>
struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, const Eigen::SyclDevice>
{
typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType;
static const int NumDims = internal::array_size<typename TensorEvaluator<InputArgType, const Eigen::SyclDevice>::Dimensions>::value;
static const int NumKernelDims = internal::array_size<Indices>::value;
typedef typename XprType::Index Index;
typedef DSizes<Index, NumDims> Dimensions;
typedef typename TensorEvaluator<KernelArgType, const Eigen::SyclDevice>::Dimensions KernelDimensions;
typedef const Eigen::SyclDevice Device;
enum {
IsAligned = TensorEvaluator<InputArgType, const Eigen::SyclDevice>::IsAligned & TensorEvaluator<KernelArgType, const Eigen::SyclDevice>::IsAligned,
PacketAccess = false,
Layout = TensorEvaluator<InputArgType, const Eigen::SyclDevice>::Layout,
CoordAccess = false, // to be implemented
RawAccess = false
};
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Eigen::SyclDevice& device)
: m_inputImpl(op.inputExpression(), device), m_kernelArg(op.kernelExpression()), m_kernelImpl(op.kernelExpression(), device), m_indices(op.indices()), m_buf(NULL), m_kernel(NULL), m_local_kernel(false), m_device(device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, const Eigen::SyclDevice>::Layout) == static_cast<int>(TensorEvaluator<KernelArgType, const Eigen::SyclDevice>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
const typename TensorEvaluator<InputArgType, const Eigen::SyclDevice>::Dimensions& input_dims = m_inputImpl.dimensions();
const typename TensorEvaluator<KernelArgType, const Eigen::SyclDevice>::Dimensions& kernel_dims = m_kernelImpl.dimensions();
m_dimensions = m_inputImpl.dimensions();
for (int i = 0; i < NumKernelDims; ++i) {
const Index index = op.indices()[i];
const Index input_dim = input_dims[index];
const Index kernel_dim = kernel_dims[i];
const Index result_dim = input_dim - kernel_dim + 1;
m_dimensions[index] = result_dim;
}
}
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, const Eigen::SyclDevice>::type PacketReturnType;
typedef typename InputArgType::Scalar Scalar;
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_dimensions; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
preloadKernel();
m_inputImpl.evalSubExprsIfNeeded(NULL);
if (data) {
executeEval(data);
return false;
} else {
m_buf = (Scalar*)m_device.allocate(dimensions().TotalSize() * sizeof(Scalar));
executeEval(m_buf);
return true;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_inputImpl.cleanup();
if (m_buf) {
m_device.deallocate(m_buf);
m_buf = NULL;
}
if (m_local_kernel) {
m_device.deallocate((void*)m_kernel);
m_local_kernel = false;
}
m_kernel = NULL;
}
/// used by sycl in order to build the sycl buffer
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device& device() const{return m_device;}
/// used by sycl in order to build the sycl buffer
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType* data() const { return m_buf; }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void preloadKernel() {
// Don't make a local copy of the kernel unless we have to (i.e. it's an
// expression that needs to be evaluated)
const Scalar* in_place = m_kernelImpl.data();
if (in_place) {
m_kernel = in_place;
m_local_kernel = false;
} else {
size_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar);
Scalar* local = (Scalar*)m_device.allocate(kernel_sz);
typedef TensorEvalToOp<const KernelArgType> EvalTo;
EvalTo evalToTmp(local, m_kernelArg);
const bool PacketAccess = internal::IsVectorizable<const Eigen::SyclDevice, KernelArgType>::value;
internal::TensorExecutor<const EvalTo, const Eigen::SyclDevice, PacketAccess>::run(evalToTmp, m_device);
m_kernel = local;
m_local_kernel = true;
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void executeEval(Scalar* data) const {
typedef TensorEvaluator<InputArgType, const Eigen::SyclDevice> InputEvaluator;
typedef typename InputEvaluator::Dimensions InputDims;
typedef Eigen::TensorSycl::internal::FunctorExtractor<InputEvaluator> InputFunctorExpr;
// extract input functor list
InputFunctorExpr input_functors = Eigen::TensorSycl::internal::extractFunctors(m_inputImpl);
m_device.sycl_queue().submit([&](cl::sycl::handler &cgh) {
typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> InputLocalAcc;
/// work-around for gcc 4.8 auto bug
typedef decltype(Eigen::TensorSycl::internal::createTupleOfAccessors<InputEvaluator>(cgh, m_inputImpl)) InputTupleType;
// create input tuple of accessors
InputTupleType tuple_of_accessors = Eigen::TensorSycl::internal::createTupleOfAccessors<InputEvaluator>(cgh, m_inputImpl);
typedef cl::sycl::accessor<uint8_t, 1, cl::sycl::access::mode::discard_write, cl::sycl::access::target::global_buffer> OutputAccessorType;
OutputAccessorType out_res= m_device. template get_sycl_accessor<cl::sycl::access::mode::discard_write>(cgh, data);
typedef cl::sycl::accessor<uint8_t, 1, cl::sycl::access::mode::read, cl::sycl::access::target::global_buffer> KernelAccessorType;
KernelAccessorType kernel_acc= m_device. template get_sycl_accessor<cl::sycl::access::mode::read>(cgh, m_kernel);
switch (NumKernelDims) {
case 1: {
const size_t numX = dimensions()[m_indices[0]];
const size_t numP = dimensions().TotalSize() / numX;
const size_t kernel_size = m_kernelImpl.dimensions().TotalSize();
size_t range_x, GRange_x, tileSize_x, range_y, GRange_y, tileSize_y;
m_device.parallel_for_setup(numX, numP, tileSize_x,tileSize_y,range_x,range_y, GRange_x, GRange_y );
const size_t shared_mem =(tileSize_x +kernel_size -1)*(tileSize_y);
assert(static_cast<unsigned long>(shared_mem) <= m_device.sharedMemPerBlock());
auto global_range=cl::sycl::range<2>(GRange_x, GRange_y); // global range
auto local_range=cl::sycl::range<2>(tileSize_x, tileSize_y); // local range
InputLocalAcc local_acc(cl::sycl::range<1>(shared_mem), cgh);
const array<Index, 1> indices{m_indices[0]};
const array<Index, 1> kernel_dims{{m_kernelImpl.dimensions()[0]}};
internal::IndexMapper<Index, InputDims, 1, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
cgh.parallel_for(cl::sycl::nd_range<2>(global_range, local_range),
EigenConvolutionKernel1D<CoeffReturnType, Scalar, InputArgType, InputFunctorExpr, Index,
InputDims, KernelAccessorType, OutputAccessorType, InputLocalAcc, InputTupleType>(
indexMapper,kernel_acc, kernel_size, numX, numP, out_res, local_acc, input_functors, tuple_of_accessors));
break;
}
case 2: {
const size_t idxX =static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 1;
const size_t idxY =static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 0;
const size_t kernel_size_x = m_kernelImpl.dimensions()[idxX];
const size_t kernel_size_y = m_kernelImpl.dimensions()[idxY];
const size_t numX = dimensions()[m_indices[idxX]];
const size_t numY = dimensions()[m_indices[idxY]];
const size_t numP = dimensions().TotalSize() / (numX*numY);
size_t range_x, GRange_x, tileSize_x, range_y, GRange_y, tileSize_y, range_z, GRange_z, tileSize_z;
m_device.parallel_for_setup(numX, numY, numP, tileSize_x, tileSize_y, tileSize_z, range_x, range_y, range_z, GRange_x, GRange_y, GRange_z );
const size_t shared_mem =(tileSize_x +kernel_size_x -1)*(tileSize_y +kernel_size_y -1) * tileSize_z;
assert(static_cast<unsigned long>(shared_mem) <= m_device.sharedMemPerBlock());
auto global_range=cl::sycl::range<3>(GRange_x, GRange_y, GRange_z); // global range
auto local_range=cl::sycl::range<3>(tileSize_x, tileSize_y, tileSize_z); // local range
InputLocalAcc local_acc(cl::sycl::range<1>(shared_mem), cgh);
const array<Index, 2> indices {{m_indices[idxX], m_indices[idxY]}};
const array<Index, 2> kernel_dims{{m_kernelImpl.dimensions()[idxX], m_kernelImpl.dimensions()[idxY]}};
internal::IndexMapper<Index, InputDims, 2, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
cgh.parallel_for(cl::sycl::nd_range<3>(global_range, local_range),
EigenConvolutionKernel2D<CoeffReturnType, Scalar, InputArgType, InputFunctorExpr, Index,
InputDims, KernelAccessorType, OutputAccessorType, InputLocalAcc, InputTupleType>(
indexMapper,kernel_acc, kernel_size_x, kernel_size_y, numX, numY, numP, out_res, local_acc, input_functors, tuple_of_accessors));
break;
}
case 3: {
const size_t idxX =static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 2;
const size_t idxY =static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 1;
const size_t idxZ =static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 0;
const size_t kernel_size_x = m_kernelImpl.dimensions()[idxX];
const size_t kernel_size_y = m_kernelImpl.dimensions()[idxY];
const size_t kernel_size_z = m_kernelImpl.dimensions()[idxZ];
const size_t numX = dimensions()[m_indices[idxX]];
const size_t numY = dimensions()[m_indices[idxY]];
const size_t numZ = dimensions()[m_indices[idxZ]];
const size_t numP = dimensions().TotalSize() / (numX*numY*numZ);
const array<Index, 3> indices{{m_indices[idxX], m_indices[idxY], m_indices[idxZ]}};
const array<Index, 3> kernel_dims{{m_kernelImpl.dimensions()[idxX],m_kernelImpl.dimensions()[idxY], m_kernelImpl.dimensions()[idxZ]}};
internal::IndexMapper<Index, InputDims, 3, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices);
size_t range_x, GRange_x, tileSize_x, range_y, GRange_y, tileSize_y, range_z, GRange_z, tileSize_z;
m_device.parallel_for_setup(numX, numY, numZ, tileSize_x, tileSize_y, tileSize_z, range_x, range_y, range_z, GRange_x, GRange_y, GRange_z );
const size_t shared_mem =(tileSize_x +kernel_size_x -1)*(tileSize_y +kernel_size_y -1) * (tileSize_z +kernel_size_y -1);
assert(static_cast<unsigned long>(shared_mem) <= m_device.sharedMemPerBlock());
auto global_range=cl::sycl::range<3>(GRange_x, GRange_y, GRange_z); // global range
auto local_range=cl::sycl::range<3>(tileSize_x, tileSize_y, tileSize_z); // local range
InputLocalAcc local_acc(cl::sycl::range<1>(shared_mem), cgh);
cgh.parallel_for(cl::sycl::nd_range<3>(global_range, local_range),
EigenConvolutionKernel3D<CoeffReturnType, Scalar, InputArgType, InputFunctorExpr, Index,
InputDims, KernelAccessorType, OutputAccessorType, InputLocalAcc, InputTupleType>(
indexMapper,kernel_acc, kernel_size_x, kernel_size_y, kernel_size_z, numX, numY,
numZ, numP, out_res, local_acc, input_functors, tuple_of_accessors));
break;
}
default: {
EIGEN_STATIC_ASSERT((NumKernelDims >= 1 && NumKernelDims <= 3), THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE);
}
}
});
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
eigen_assert(m_buf);
eigen_assert(index < m_dimensions.TotalSize());
return m_buf[index];
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const
{
eigen_assert(m_buf);
eigen_assert(index < m_dimensions.TotalSize());
return internal::ploadt<PacketReturnType, LoadMode>(m_buf+index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
// TODO(rmlarsen): FIXME: For now, this is just a copy of the CPU cost
// model.
const double kernel_size = m_kernelImpl.dimensions().TotalSize();
// We ignore the use of fused multiply-add.
const double convolve_compute_cost =
TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>();
const double firstIndex_compute_cost =
NumDims *
(2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() +
TensorOpCost::DivCost<Index>());
return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) +
kernel_size * (m_inputImpl.costPerCoeff(vectorized) +
m_kernelImpl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, convolve_compute_cost, vectorized,
PacketSize));
}
private:
// No assignment (copies are needed by the kernels)
TensorEvaluator& operator = (const TensorEvaluator&);
TensorEvaluator<InputArgType, const Eigen::SyclDevice> m_inputImpl;
KernelArgType m_kernelArg;
TensorEvaluator<KernelArgType, const Eigen::SyclDevice> m_kernelImpl;
Indices m_indices;
Dimensions m_dimensions;
Scalar* m_buf;
const Scalar* m_kernel;
bool m_local_kernel;
const Eigen::SyclDevice& m_device;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H
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