mirror of
https://gitlab.com/libeigen/eigen.git
synced 2025-01-24 14:45:14 +08:00
Removed the sycl include from Eigen/Core and moved it to Unsupported/Eigen/CXX11/Tensor; added TensorReduction for sycl (full reduction and partial reduction); added TensorReduction test case for sycl (full reduction and partial reduction); fixed the tile size on TensorSyclRun.h based on the device max work group size;
This commit is contained in:
Mehdi Goli
parent
0585b2965d
commit
0ebe3808ca
10
Eigen/Core
10
Eigen/Core
@ -14,16 +14,6 @@
|
||||
// first thing Eigen does: stop the compiler from committing suicide
|
||||
#include "src/Core/util/DisableStupidWarnings.h"
|
||||
|
||||
/// This will no longer be needed after the next release of the computecppCE
|
||||
#ifdef EIGEN_USE_SYCL
|
||||
#undef min
|
||||
#undef max
|
||||
#undef isnan
|
||||
#undef isinf
|
||||
#undef isfinite
|
||||
#include <SYCL/sycl.hpp>
|
||||
#endif
|
||||
|
||||
// Handle NVCC/CUDA/SYCL
|
||||
#if defined(__CUDACC__) || defined(__SYCL_DEVICE_ONLY__)
|
||||
// Do not try asserts on CUDA and SYCL!
|
||||
|
||||
@ -13,6 +13,15 @@
|
||||
|
||||
#include "../../../Eigen/Core"
|
||||
|
||||
#ifdef EIGEN_USE_SYCL
|
||||
#undef min
|
||||
#undef max
|
||||
#undef isnan
|
||||
#undef isinf
|
||||
#undef isfinite
|
||||
#include <SYCL/sycl.hpp>
|
||||
#endif
|
||||
|
||||
#include <Eigen/src/Core/util/DisableStupidWarnings.h>
|
||||
|
||||
#include "../SpecialFunctions"
|
||||
@ -69,10 +78,6 @@ typedef unsigned __int64 uint64_t;
|
||||
#endif
|
||||
#endif
|
||||
|
||||
#ifdef EIGEN_USE_SYCL
|
||||
#include <SYCL/sycl.hpp>
|
||||
#endif
|
||||
|
||||
#include "src/Tensor/TensorMacros.h"
|
||||
#include "src/Tensor/TensorForwardDeclarations.h"
|
||||
#include "src/Tensor/TensorMeta.h"
|
||||
@ -81,7 +86,6 @@ typedef unsigned __int64 uint64_t;
|
||||
#include "src/Tensor/TensorDeviceDefault.h"
|
||||
#include "src/Tensor/TensorDeviceThreadPool.h"
|
||||
#include "src/Tensor/TensorDeviceCuda.h"
|
||||
#include "src/Tensor/TensorSycl.h"
|
||||
#include "src/Tensor/TensorDeviceSycl.h"
|
||||
#include "src/Tensor/TensorIndexList.h"
|
||||
#include "src/Tensor/TensorDimensionList.h"
|
||||
@ -128,6 +132,7 @@ typedef unsigned __int64 uint64_t;
|
||||
#include "src/Tensor/TensorAssign.h"
|
||||
#include "src/Tensor/TensorScan.h"
|
||||
|
||||
#include "src/Tensor/TensorSycl.h"
|
||||
#include "src/Tensor/TensorExecutor.h"
|
||||
#include "src/Tensor/TensorDevice.h"
|
||||
|
||||
|
||||
@ -1,12 +1,11 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
|
||||
// Mehdi Goli Codeplay Software Ltd.
|
||||
// Ralph Potter Codeplay Software Ltd.
|
||||
// Luke Iwanski Codeplay Software Ltd.
|
||||
// Cummins Chris PhD student at The University of Edinburgh.
|
||||
// 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
|
||||
@ -25,12 +24,8 @@ namespace Eigen {
|
||||
template <typename T, bool MapAllocator>
|
||||
struct BufferT {
|
||||
using Type = cl::sycl::buffer<T, 1, cl::sycl::map_allocator<T>>;
|
||||
static inline void add_sycl_buffer(
|
||||
const T *ptr, size_t num_bytes,
|
||||
std::map<const void *, std::shared_ptr<void>> &buffer_map) {
|
||||
buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(
|
||||
ptr, std::shared_ptr<void>(std::make_shared<Type>(
|
||||
Type(const_cast<T *>(ptr), cl::sycl::range<1>(num_bytes))))));
|
||||
static inline void add_sycl_buffer(const T *ptr, size_t num_bytes,std::map<const void *, std::shared_ptr<void>> &buffer_map) {
|
||||
buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(ptr, std::shared_ptr<void>(std::make_shared<Type>(Type(const_cast<T *>(ptr), cl::sycl::range<1>(num_bytes))))));
|
||||
}
|
||||
};
|
||||
|
||||
@ -39,12 +34,8 @@ struct BufferT {
|
||||
template <typename T>
|
||||
struct BufferT<T, false> {
|
||||
using Type = cl::sycl::buffer<T, 1>;
|
||||
static inline void add_sycl_buffer(
|
||||
const T *ptr, size_t num_bytes,
|
||||
std::map<const void *, std::shared_ptr<void>> &buffer_map) {
|
||||
buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(
|
||||
ptr, std::shared_ptr<void>(
|
||||
std::make_shared<Type>(Type(cl::sycl::range<1>(num_bytes))))));
|
||||
static inline void add_sycl_buffer(const T *ptr, size_t num_bytes, std::map<const void *, std::shared_ptr<void>> &buffer_map) {
|
||||
buffer_map.insert(std::pair<const void *, std::shared_ptr<void>>(ptr, std::shared_ptr<void>(std::make_shared<Type>(Type(cl::sycl::range<1>(num_bytes))))));
|
||||
}
|
||||
};
|
||||
|
||||
@ -78,15 +69,20 @@ struct SyclDevice {
|
||||
/// for that particular pointer.
|
||||
template <cl::sycl::access::mode AcMd, bool MapAllocator, typename T>
|
||||
inline cl::sycl::accessor<T, 1, AcMd, cl::sycl::access::target::global_buffer>
|
||||
get_sycl_accessor(size_t num_bytes, cl::sycl::handler &cgh,
|
||||
const T *ptr) const {
|
||||
get_sycl_accessor(size_t num_bytes, cl::sycl::handler &cgh, const T * ptr) const {
|
||||
return (get_sycl_buffer<MapAllocator,T>(num_bytes, ptr).template get_access<AcMd, cl::sycl::access::target::global_buffer>(cgh));
|
||||
}
|
||||
|
||||
template <bool MapAllocator, typename T>
|
||||
inline typename BufferT<T, MapAllocator>::Type
|
||||
get_sycl_buffer(size_t num_bytes,const T * ptr) const {
|
||||
if(MapAllocator && !ptr){
|
||||
eigen_assert("pointer with map_Allocator cannot be null. Please initialise the input pointer"); }
|
||||
auto it = buffer_map.find(ptr);
|
||||
if (it == buffer_map.end()) {
|
||||
BufferT<T, MapAllocator>::add_sycl_buffer(ptr, num_bytes, buffer_map);
|
||||
}
|
||||
return (
|
||||
((typename BufferT<T, MapAllocator>::Type *)(buffer_map.at(ptr).get()))
|
||||
->template get_access<AcMd>(cgh));
|
||||
return (*((typename BufferT<T, MapAllocator>::Type*)((buffer_map.at(ptr).get()))));
|
||||
}
|
||||
|
||||
/// allocating memory on the cpu
|
||||
@ -100,22 +96,21 @@ struct SyclDevice {
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void deallocate(void *buffer) const {
|
||||
internal::aligned_free(buffer);
|
||||
}
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void *dst, const void *src,
|
||||
size_t n) const {
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpy(void *dst, const void *src, size_t n) const {
|
||||
::memcpy(dst, src, n);
|
||||
}
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(
|
||||
void *dst, const void *src, size_t n) const {
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyHostToDevice(void *dst, const void *src, size_t n) const {
|
||||
memcpy(dst, src, n);
|
||||
}
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(
|
||||
void *dst, const void *src, size_t n) const {
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memcpyDeviceToHost(void *dst, const void *src, size_t n) const {
|
||||
memcpy(dst, src, n);
|
||||
}
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void *buffer, int c,
|
||||
size_t n) const {
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void memset(void *buffer, int c, size_t n) const {
|
||||
::memset(buffer, c, n);
|
||||
}
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE int majorDeviceVersion() const {
|
||||
return 1;
|
||||
}
|
||||
};
|
||||
} // end namespace Eigen
|
||||
|
||||
|
||||
@ -47,13 +47,13 @@ struct traits<TensorEvalToOp<XprType, MakePointer_> >
|
||||
template<typename XprType, template <class> class MakePointer_>
|
||||
struct eval<TensorEvalToOp<XprType, MakePointer_>, Eigen::Dense>
|
||||
{
|
||||
typedef const TensorEvalToOp<XprType>& type;
|
||||
typedef const TensorEvalToOp<XprType, MakePointer_>& type;
|
||||
};
|
||||
|
||||
template<typename XprType, template <class> class MakePointer_>
|
||||
struct nested<TensorEvalToOp<XprType, MakePointer_>, 1, typename eval<TensorEvalToOp<XprType, MakePointer_> >::type>
|
||||
{
|
||||
typedef TensorEvalToOp<XprType> type;
|
||||
typedef TensorEvalToOp<XprType, MakePointer_> type;
|
||||
};
|
||||
|
||||
} // end namespace internal
|
||||
|
||||
@ -33,7 +33,7 @@ template<typename UnaryOp, typename XprType> class TensorCwiseUnaryOp;
|
||||
template<typename BinaryOp, typename LeftXprType, typename RightXprType> class TensorCwiseBinaryOp;
|
||||
template<typename TernaryOp, typename Arg1XprType, typename Arg2XprType, typename Arg3XprType> class TensorCwiseTernaryOp;
|
||||
template<typename IfXprType, typename ThenXprType, typename ElseXprType> class TensorSelectOp;
|
||||
template<typename Op, typename Dims, typename XprType> class TensorReductionOp;
|
||||
template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_ = MakePointer > class TensorReductionOp;
|
||||
template<typename XprType> class TensorIndexTupleOp;
|
||||
template<typename ReduceOp, typename Dims, typename XprType> class TensorTupleReducerOp;
|
||||
template<typename Axis, typename LeftXprType, typename RightXprType> class TensorConcatenationOp;
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
|
||||
// Copyright (C) 2016 Mehdi Goli, Codeplay Software Ltd <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
|
||||
@ -20,8 +21,8 @@ namespace Eigen {
|
||||
*/
|
||||
|
||||
namespace internal {
|
||||
template<typename Op, typename Dims, typename XprType>
|
||||
struct traits<TensorReductionOp<Op, Dims, XprType> >
|
||||
template<typename Op, typename Dims, typename XprType,template <class> class MakePointer_ >
|
||||
struct traits<TensorReductionOp<Op, Dims, XprType, MakePointer_> >
|
||||
: traits<XprType>
|
||||
{
|
||||
typedef traits<XprType> XprTraits;
|
||||
@ -31,18 +32,24 @@ struct traits<TensorReductionOp<Op, Dims, XprType> >
|
||||
typedef typename XprType::Nested Nested;
|
||||
static const int NumDimensions = XprTraits::NumDimensions - array_size<Dims>::value;
|
||||
static const int Layout = XprTraits::Layout;
|
||||
|
||||
template <class T> struct MakePointer {
|
||||
// Intermediate typedef to workaround MSVC issue.
|
||||
typedef MakePointer_<T> MakePointerT;
|
||||
typedef typename MakePointerT::Type Type;
|
||||
};
|
||||
};
|
||||
|
||||
template<typename Op, typename Dims, typename XprType>
|
||||
struct eval<TensorReductionOp<Op, Dims, XprType>, Eigen::Dense>
|
||||
template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
|
||||
struct eval<TensorReductionOp<Op, Dims, XprType, MakePointer_>, Eigen::Dense>
|
||||
{
|
||||
typedef const TensorReductionOp<Op, Dims, XprType>& type;
|
||||
typedef const TensorReductionOp<Op, Dims, XprType, MakePointer_>& type;
|
||||
};
|
||||
|
||||
template<typename Op, typename Dims, typename XprType>
|
||||
struct nested<TensorReductionOp<Op, Dims, XprType>, 1, typename eval<TensorReductionOp<Op, Dims, XprType> >::type>
|
||||
template<typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
|
||||
struct nested<TensorReductionOp<Op, Dims, XprType, MakePointer_>, 1, typename eval<TensorReductionOp<Op, Dims, XprType, MakePointer_> >::type>
|
||||
{
|
||||
typedef TensorReductionOp<Op, Dims, XprType> type;
|
||||
typedef TensorReductionOp<Op, Dims, XprType, MakePointer_> type;
|
||||
};
|
||||
|
||||
|
||||
@ -339,8 +346,8 @@ __global__ void OuterReductionKernel(R, const S, I, I, typename S::CoeffReturnTy
|
||||
} // end namespace internal
|
||||
|
||||
|
||||
template <typename Op, typename Dims, typename XprType>
|
||||
class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType>, ReadOnlyAccessors> {
|
||||
template <typename Op, typename Dims, typename XprType, template <class> class MakePointer_>
|
||||
class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType, MakePointer_>, ReadOnlyAccessors> {
|
||||
public:
|
||||
typedef typename Eigen::internal::traits<TensorReductionOp>::Scalar Scalar;
|
||||
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
|
||||
@ -371,18 +378,19 @@ class TensorReductionOp : public TensorBase<TensorReductionOp<Op, Dims, XprType>
|
||||
|
||||
|
||||
// Eval as rvalue
|
||||
template<typename Op, typename Dims, typename ArgType, typename Device>
|
||||
struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
|
||||
template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
|
||||
struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>
|
||||
{
|
||||
typedef TensorReductionOp<Op, Dims, ArgType> XprType;
|
||||
typedef TensorReductionOp<Op, Dims, ArgType, MakePointer_> XprType;
|
||||
typedef typename XprType::Index Index;
|
||||
typedef ArgType ChildType;
|
||||
typedef typename TensorEvaluator<ArgType, Device>::Dimensions InputDimensions;
|
||||
static const int NumInputDims = internal::array_size<InputDimensions>::value;
|
||||
static const int NumReducedDims = internal::array_size<Dims>::value;
|
||||
static const int NumOutputDims = NumInputDims - NumReducedDims;
|
||||
typedef typename internal::conditional<NumOutputDims==0, Sizes<>, DSizes<Index, NumOutputDims> >::type Dimensions;
|
||||
typedef typename XprType::Scalar Scalar;
|
||||
typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device> Self;
|
||||
typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Self;
|
||||
static const bool InputPacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess;
|
||||
typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
|
||||
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
|
||||
@ -401,7 +409,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
|
||||
static const bool RunningFullReduction = (NumOutputDims==0);
|
||||
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
|
||||
: m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device)
|
||||
: m_impl(op.expression(), device), m_reducer(op.reducer()), m_result(NULL), m_device(device), m_xpr_dims(op.dims())
|
||||
{
|
||||
EIGEN_STATIC_ASSERT((NumInputDims >= NumReducedDims), YOU_MADE_A_PROGRAMMING_MISTAKE);
|
||||
EIGEN_STATIC_ASSERT((!ReducingInnerMostDims | !PreservingInnerMostDims | (NumReducedDims == NumInputDims)),
|
||||
@ -471,25 +479,35 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
|
||||
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
|
||||
|
||||
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool evalSubExprsIfNeeded(CoeffReturnType* data) {
|
||||
EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool evalSubExprsIfNeeded(typename MakePointer_<CoeffReturnType>::Type data) {
|
||||
m_impl.evalSubExprsIfNeeded(NULL);
|
||||
|
||||
// Use the FullReducer if possible.
|
||||
if (RunningFullReduction &&
|
||||
if ((RunningFullReduction && RunningOnSycl) ||(RunningFullReduction &&
|
||||
internal::FullReducer<Self, Op, Device>::HasOptimizedImplementation &&
|
||||
((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) ||
|
||||
!RunningOnGPU)) {
|
||||
!RunningOnGPU))) {
|
||||
bool need_assign = false;
|
||||
if (!data) {
|
||||
m_result = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType)));
|
||||
data = m_result;
|
||||
need_assign = true;
|
||||
}
|
||||
|
||||
Op reducer(m_reducer);
|
||||
internal::FullReducer<Self, Op, Device>::run(*this, reducer, m_device, data);
|
||||
return need_assign;
|
||||
}
|
||||
else if(RunningOnSycl){
|
||||
const Index num_values_to_reduce = internal::array_prod(m_reducedDims);
|
||||
const Index num_coeffs_to_preserve = internal::array_prod(m_dimensions);
|
||||
if (!data) {
|
||||
data = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType) * num_coeffs_to_preserve));
|
||||
m_result = data;
|
||||
}
|
||||
Op reducer(m_reducer);
|
||||
internal::InnerReducer<Self, Op, Device>::run(*this, reducer, m_device, data, num_values_to_reduce, num_coeffs_to_preserve);
|
||||
return (m_result != NULL);
|
||||
}
|
||||
|
||||
// Attempt to use an optimized reduction.
|
||||
else if (RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) {
|
||||
@ -572,7 +590,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
|
||||
|
||||
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
|
||||
{
|
||||
if ((RunningFullReduction || RunningOnGPU) && m_result) {
|
||||
if ((RunningOnSycl || RunningFullReduction || RunningOnGPU) && m_result) {
|
||||
return *(m_result + index);
|
||||
}
|
||||
Op reducer(m_reducer);
|
||||
@ -644,7 +662,20 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
|
||||
}
|
||||
}
|
||||
|
||||
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
|
||||
/// required by sycl in order to extract the output accessor
|
||||
#ifndef EIGEN_USE_SYCL
|
||||
EIGEN_DEVICE_FUNC typename MakePointer_<Scalar>::Type data() const { return NULL; }
|
||||
#else
|
||||
EIGEN_DEVICE_FUNC typename MakePointer_<Scalar>::Type data() const {
|
||||
return m_result; }
|
||||
#endif
|
||||
/// required by sycl in order to extract the accessor
|
||||
const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
|
||||
/// added for sycl in order to construct the buffer from the sycl device
|
||||
const Device& device() const{return m_device;}
|
||||
/// added for sycl in order to re-construct the reduction eval on the device for the sub-kernel
|
||||
const Dims& xprDims() const {return m_xpr_dims;}
|
||||
|
||||
|
||||
private:
|
||||
template <int, typename, typename> friend struct internal::GenericDimReducer;
|
||||
@ -737,12 +768,18 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
|
||||
// For full reductions
|
||||
#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
|
||||
static const bool RunningOnGPU = internal::is_same<Device, Eigen::GpuDevice>::value;
|
||||
static const bool RunningOnSycl=false;
|
||||
#elif defined(EIGEN_USE_SYCL)
|
||||
static const bool RunningOnSycl = internal::is_same<typename internal::remove_all<Device>::type, Eigen::SyclDevice>::value;
|
||||
static const bool RunningOnGPU = false;
|
||||
#else
|
||||
static const bool RunningOnGPU = false;
|
||||
static const bool RunningOnSycl=false;
|
||||
#endif
|
||||
CoeffReturnType* m_result;
|
||||
typename MakePointer_<CoeffReturnType>::Type m_result;
|
||||
|
||||
const Device& m_device;
|
||||
const Dims& m_xpr_dims;
|
||||
};
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
242
unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
Normal file
242
unsupported/Eigen/CXX11/src/Tensor/TensorReductionSycl.h
Normal file
@ -0,0 +1,242 @@
|
||||
// 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>
|
||||
//
|
||||
// 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/.
|
||||
|
||||
/*****************************************************************
|
||||
* TensorSyclPlaceHolderExpr.h
|
||||
*
|
||||
* \brief:
|
||||
* This is the specialisation of the placeholder expression based on the
|
||||
* operation type
|
||||
*
|
||||
*****************************************************************/
|
||||
|
||||
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
|
||||
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
|
||||
|
||||
namespace Eigen {
|
||||
namespace internal {
|
||||
|
||||
template<typename CoeffReturnType, typename KernelName> struct syclGenericBufferReducer{
|
||||
template<typename BufferTOut, typename BufferTIn>
|
||||
static void run(BufferTOut& bufOut, BufferTIn& bufI, const Eigen::SyclDevice& dev, size_t length, size_t local){
|
||||
do {
|
||||
auto f = [length, local, &bufOut, &bufI](cl::sycl::handler& h) mutable {
|
||||
cl::sycl::nd_range<1> r{cl::sycl::range<1>{std::max(length, local)},
|
||||
cl::sycl::range<1>{std::min(length, local)}};
|
||||
/* Two accessors are used: one to the buffer that is being reduced,
|
||||
* and a second to local memory, used to store intermediate data. */
|
||||
auto aI =
|
||||
bufI.template get_access<cl::sycl::access::mode::read_write>(h);
|
||||
auto aOut =
|
||||
bufOut.template get_access<cl::sycl::access::mode::discard_write>(h);
|
||||
cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write,
|
||||
cl::sycl::access::target::local>
|
||||
scratch(cl::sycl::range<1>(local), h);
|
||||
|
||||
/* The parallel_for invocation chosen is the variant with an nd_item
|
||||
* parameter, since the code requires barriers for correctness. */
|
||||
h.parallel_for<KernelName>(
|
||||
r, [aOut, aI, scratch, local, length](cl::sycl::nd_item<1> id) {
|
||||
size_t globalid = id.get_global(0);
|
||||
size_t localid = id.get_local(0);
|
||||
/* All threads collectively read from global memory into local.
|
||||
* The barrier ensures all threads' IO is resolved before
|
||||
* execution continues (strictly speaking, all threads within
|
||||
* a single work-group - there is no co-ordination between
|
||||
* work-groups, only work-items). */
|
||||
if (globalid < length) {
|
||||
scratch[localid] = aI[globalid];
|
||||
}
|
||||
id.barrier(cl::sycl::access::fence_space::local_space);
|
||||
|
||||
/* Apply the reduction operation between the current local
|
||||
* id and the one on the other half of the vector. */
|
||||
if (globalid < length) {
|
||||
int min = (length < local) ? length : local;
|
||||
for (size_t offset = min / 2; offset > 0; offset /= 2) {
|
||||
if (localid < offset) {
|
||||
scratch[localid] += scratch[localid + offset];
|
||||
}
|
||||
id.barrier(cl::sycl::access::fence_space::local_space);
|
||||
}
|
||||
/* The final result will be stored in local id 0. */
|
||||
if (localid == 0) {
|
||||
aI[id.get_group(0)] = scratch[localid];
|
||||
if((length<=local) && globalid ==0){
|
||||
aOut[globalid]=scratch[localid];
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
};
|
||||
dev.m_queue.submit(f);
|
||||
dev.m_queue.throw_asynchronous();
|
||||
|
||||
/* At this point, you could queue::wait_and_throw() to ensure that
|
||||
* errors are caught quickly. However, this would likely impact
|
||||
* performance negatively. */
|
||||
length = length / local;
|
||||
|
||||
} while (length > 1);
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
/// For now let's start with a full reducer
|
||||
/// Self is useless here because in expression construction we are going to treat reduction as a leafnode.
|
||||
/// we want to take reduction child and then build a construction and apply the full reducer function on it. Fullreducre applies the
|
||||
/// reduction operation on the child of the reduction. once it is done the reduction is an empty shell and can be thrown away and treated as
|
||||
// a leafNode.
|
||||
template <typename Self, typename Op, bool Vectorizable>
|
||||
struct FullReducer<Self, Op, const Eigen::SyclDevice, Vectorizable> {
|
||||
|
||||
typedef typename Self::CoeffReturnType CoeffReturnType;
|
||||
static const bool HasOptimizedImplementation = false;
|
||||
|
||||
static void run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output) {
|
||||
typedef const typename Self::ChildType HostExpr; /// this is the child of reduction
|
||||
typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
|
||||
auto functors = TensorSycl::internal::extractFunctors(self.impl());
|
||||
int red_factor =256; /// initial reduction. If the size is less than red_factor we only creates one thread.
|
||||
size_t inputSize =self.impl().dimensions().TotalSize();
|
||||
size_t rng = inputSize/red_factor; // the total number of thread initially is half the size of the input
|
||||
size_t remaining = inputSize% red_factor;
|
||||
if(rng ==0) {
|
||||
red_factor=1;
|
||||
};
|
||||
size_t tileSize =dev.m_queue.get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2;
|
||||
size_t GRange=std::max((size_t )1, rng);
|
||||
|
||||
// convert global range to power of 2 for redecution
|
||||
GRange--;
|
||||
GRange |= GRange >> 1;
|
||||
GRange |= GRange >> 2;
|
||||
GRange |= GRange >> 4;
|
||||
GRange |= GRange >> 8;
|
||||
GRange |= GRange >> 16;
|
||||
#if __x86_64__ || __ppc64__ || _WIN64
|
||||
GRange |= GRange >> 32;
|
||||
#endif
|
||||
GRange++;
|
||||
size_t outTileSize = tileSize;
|
||||
/// if the shared memory is less than the GRange, we set shared_mem size to the TotalSize and in this case one kernel would be created for recursion to reduce all to one.
|
||||
if (GRange < outTileSize) outTileSize=GRange;
|
||||
// getting final out buffer at the moment the created buffer is true because there is no need for assign
|
||||
auto out_buffer =dev.template get_sycl_buffer<true, typename Eigen::internal::remove_all<CoeffReturnType>::type>(self.dimensions().TotalSize(), output);
|
||||
/// creating the shared memory for calculating reduction.
|
||||
/// This one is used to collect all the reduced value of shared memory as we dont have global barrier on GPU. Once it is saved we can
|
||||
/// recursively apply reduction on it in order to reduce the whole.
|
||||
auto temp_global_buffer =cl::sycl::buffer<CoeffReturnType, 1>(cl::sycl::range<1>(GRange));
|
||||
typedef typename Eigen::internal::remove_all<decltype(self.xprDims())>::type Dims;
|
||||
Dims dims= self.xprDims();
|
||||
Op functor = reducer;
|
||||
dev.m_queue.submit([&](cl::sycl::handler &cgh) {
|
||||
// create a tuple of accessors from Evaluator
|
||||
auto tuple_of_accessors = TensorSycl::internal::createTupleOfAccessors(cgh, self.impl());
|
||||
auto tmp_global_accessor = temp_global_buffer. template get_access<cl::sycl::access::mode::read_write, cl::sycl::access::target::global_buffer>(cgh);
|
||||
|
||||
cgh.parallel_for<PlaceHolderExpr>( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(outTileSize)), [=](cl::sycl::nd_item<1> itemID) {
|
||||
typedef typename TensorSycl::internal::ConvertToDeviceExpression<const HostExpr>::Type DevExpr;
|
||||
auto device_expr = TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
|
||||
/// reduction cannot be captured automatically through our device conversion recursion. The reason is that reduction has two behaviour
|
||||
/// the first behaviour is when it is used as a root to lauch the sub-kernel. The second one is when it is treated as a leafnode to pass the
|
||||
/// calculated result to its parent kernel. While the latter is automatically detected through our device expression generator. The former is created here.
|
||||
const auto device_self_expr= TensorReductionOp<Op, Dims, decltype(device_expr.expr) ,MakeGlobalPointer>(device_expr.expr, dims, functor);
|
||||
/// This is the evaluator for device_self_expr. This is exactly similar to the self which has been passed to run function. The difference is
|
||||
/// the device_evaluator is detectable and recognisable on the device.
|
||||
auto device_self_evaluator = Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice>(device_self_expr, Eigen::DefaultDevice());
|
||||
/// const cast added as a naive solution to solve the qualifier drop error
|
||||
auto globalid=itemID.get_global_linear_id();
|
||||
|
||||
if(globalid<rng)
|
||||
tmp_global_accessor.get_pointer()[globalid]=InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, red_factor*globalid, red_factor, const_cast<Op&>(functor));
|
||||
else
|
||||
tmp_global_accessor.get_pointer()[globalid]=static_cast<CoeffReturnType>(0);
|
||||
|
||||
if(remaining!=0 && globalid==0 )
|
||||
// this will add the rest of input buffer when the input size is not devidable to red_factor.
|
||||
tmp_global_accessor.get_pointer()[globalid]+=InnerMostDimReducer<decltype(device_self_evaluator), Op, false>::reduce(device_self_evaluator, red_factor*(rng), remaining, const_cast<Op&>(functor));
|
||||
});
|
||||
});
|
||||
dev.m_queue.throw_asynchronous();
|
||||
|
||||
/// This is used to recursively reduce the tmp value to an element of 1;
|
||||
syclGenericBufferReducer<CoeffReturnType,HostExpr>::run(out_buffer, temp_global_buffer,dev, GRange, outTileSize);
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
template <typename Self, typename Op>
|
||||
struct InnerReducer<Self, Op, const Eigen::SyclDevice> {
|
||||
|
||||
typedef typename Self::CoeffReturnType CoeffReturnType;
|
||||
static const bool HasOptimizedImplementation = false;
|
||||
|
||||
static bool run(const Self& self, Op& reducer, const Eigen::SyclDevice& dev, CoeffReturnType* output, typename Self::Index , typename Self::Index num_coeffs_to_preserve) {
|
||||
typedef const typename Self::ChildType HostExpr; /// this is the child of reduction
|
||||
typedef typename TensorSycl::internal::createPlaceHolderExpression<HostExpr>::Type PlaceHolderExpr;
|
||||
auto functors = TensorSycl::internal::extractFunctors(self.impl());
|
||||
|
||||
size_t tileSize =dev.m_queue.get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2;
|
||||
|
||||
size_t GRange=num_coeffs_to_preserve;
|
||||
if (tileSize>GRange) tileSize=GRange;
|
||||
else if(GRange>tileSize){
|
||||
size_t xMode = GRange % tileSize;
|
||||
if (xMode != 0) GRange += (tileSize - xMode);
|
||||
}
|
||||
// getting final out buffer at the moment the created buffer is true because there is no need for assign
|
||||
/// creating the shared memory for calculating reduction.
|
||||
/// This one is used to collect all the reduced value of shared memory as we dont have global barrier on GPU. Once it is saved we can
|
||||
/// recursively apply reduction on it in order to reduce the whole.
|
||||
typedef typename Eigen::internal::remove_all<decltype(self.xprDims())>::type Dims;
|
||||
Dims dims= self.xprDims();
|
||||
Op functor = reducer;
|
||||
|
||||
dev.m_queue.submit([&](cl::sycl::handler &cgh) {
|
||||
// create a tuple of accessors from Evaluator
|
||||
auto tuple_of_accessors = TensorSycl::internal::createTupleOfAccessors(cgh, self.impl());
|
||||
auto output_accessor = dev.template get_sycl_accessor<cl::sycl::access::mode::discard_write, true>(num_coeffs_to_preserve,cgh, output);
|
||||
|
||||
cgh.parallel_for<Self>( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), [=](cl::sycl::nd_item<1> itemID) {
|
||||
typedef typename TensorSycl::internal::ConvertToDeviceExpression<const HostExpr>::Type DevExpr;
|
||||
auto device_expr = TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
|
||||
/// reduction cannot be captured automatically through our device conversion recursion. The reason is that reduction has two behaviour
|
||||
/// the first behaviour is when it is used as a root to lauch the sub-kernel. The second one is when it is treated as a leafnode to pass the
|
||||
/// calculated result to its parent kernel. While the latter is automatically detected through our device expression generator. The former is created here.
|
||||
const auto device_self_expr= TensorReductionOp<Op, Dims, decltype(device_expr.expr) ,MakeGlobalPointer>(device_expr.expr, dims, functor);
|
||||
/// This is the evaluator for device_self_expr. This is exactly similar to the self which has been passed to run function. The difference is
|
||||
/// the device_evaluator is detectable and recognisable on the device.
|
||||
typedef Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice> DeiceSelf;
|
||||
auto device_self_evaluator = Eigen::TensorEvaluator<decltype(device_self_expr), Eigen::DefaultDevice>(device_self_expr, Eigen::DefaultDevice());
|
||||
/// const cast added as a naive solution to solve the qualifier drop error
|
||||
auto globalid=itemID.get_global_linear_id();
|
||||
if (globalid< static_cast<size_t>(num_coeffs_to_preserve)) {
|
||||
typename DeiceSelf::CoeffReturnType accum = functor.initialize();
|
||||
GenericDimReducer<DeiceSelf::NumReducedDims-1, DeiceSelf, Op>::reduce(device_self_evaluator, device_self_evaluator.firstInput(globalid),const_cast<Op&>(functor), &accum);
|
||||
functor.finalize(accum);
|
||||
output_accessor.get_pointer()[globalid]= accum;
|
||||
}
|
||||
});
|
||||
});
|
||||
dev.m_queue.throw_asynchronous();
|
||||
return false;
|
||||
}
|
||||
};
|
||||
|
||||
} // end namespace internal
|
||||
} // namespace Eigen
|
||||
|
||||
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSOR_REDUCTION_SYCL_HPP
|
||||
@ -22,6 +22,13 @@ struct MakeGlobalPointer {
|
||||
typedef typename cl::sycl::global_ptr<T>::pointer_t Type;
|
||||
};
|
||||
|
||||
// global pointer to set different attribute state for a class
|
||||
template <class T>
|
||||
struct MakeLocalPointer {
|
||||
typedef typename cl::sycl::local_ptr<T>::pointer_t Type;
|
||||
};
|
||||
|
||||
|
||||
namespace Eigen {
|
||||
namespace TensorSycl {
|
||||
namespace internal {
|
||||
@ -43,9 +50,7 @@ template<typename T> struct GetType<false, T>{
|
||||
// tuple construction
|
||||
#include "TensorSyclTuple.h"
|
||||
|
||||
// This file contains the PlaceHolder that replaces the actual data
|
||||
#include "TensorSyclPlaceHolder.h"
|
||||
|
||||
// counting number of leaf at compile time
|
||||
#include "TensorSyclLeafCount.h"
|
||||
|
||||
// The index PlaceHolder takes the actual expression and replaces the actual
|
||||
@ -57,9 +62,6 @@ template<typename T> struct GetType<false, T>{
|
||||
// creation of an accessor tuple from a tuple of SYCL buffers
|
||||
#include "TensorSyclExtractAccessor.h"
|
||||
|
||||
// actual data extraction using accessors
|
||||
//#include "GetDeviceData.h"
|
||||
|
||||
// this is used to change the address space type in tensor map for GPU
|
||||
#include "TensorSyclConvertToDeviceExpression.h"
|
||||
|
||||
@ -70,6 +72,9 @@ template<typename T> struct GetType<false, T>{
|
||||
// this is used to construct the expression on the device
|
||||
#include "TensorSyclExprConstructor.h"
|
||||
|
||||
/// this is used for extracting tensor reduction
|
||||
#include "TensorReductionSycl.h"
|
||||
|
||||
// kernel execution using fusion
|
||||
#include "TensorSyclRun.h"
|
||||
|
||||
|
||||
@ -102,6 +102,18 @@ KERNELBROKERCONVERT(, false, TensorForcedEvalOp)
|
||||
KERNELBROKERCONVERT(const, true, TensorEvalToOp)
|
||||
KERNELBROKERCONVERT(, false, TensorEvalToOp)
|
||||
#undef KERNELBROKERCONVERT
|
||||
|
||||
/// specialisation of the \ref ConvertToDeviceExpression struct when the node type is TensorReductionOp
|
||||
#define KERNELBROKERCONVERTREDUCTION(CVQual)\
|
||||
template <typename OP, typename Dim, typename subExpr, template <class> class MakePointer_>\
|
||||
struct ConvertToDeviceExpression<CVQual TensorReductionOp<OP, Dim, subExpr, MakePointer_> > {\
|
||||
typedef CVQual TensorReductionOp<OP, Dim, typename ConvertToDeviceExpression<subExpr>::Type, MakeGlobalPointer> Type;\
|
||||
};
|
||||
|
||||
KERNELBROKERCONVERTREDUCTION(const)
|
||||
KERNELBROKERCONVERTREDUCTION()
|
||||
#undef KERNELBROKERCONVERTREDUCTION
|
||||
|
||||
} // namespace internal
|
||||
} // namespace TensorSycl
|
||||
} // namespace Eigen
|
||||
|
||||
@ -33,8 +33,7 @@ struct EvalToLHSConstructor {
|
||||
EvalToLHSConstructor(const utility::tuple::Tuple<Params...> &t): expr((&(*(utility::tuple::get<N>(t).get_pointer())))) {}
|
||||
};
|
||||
|
||||
/// \struct ExprConstructor is used to reconstruct the expression on the device
|
||||
/// and
|
||||
/// \struct ExprConstructor is used to reconstruct the expression on the device and
|
||||
/// recreate the expression with MakeGlobalPointer containing the device address
|
||||
/// space for the TensorMap pointers used in eval function.
|
||||
/// It receives the original expression type, the functor of the node, the tuple
|
||||
@ -49,7 +48,7 @@ struct ExprConstructor;
|
||||
template <typename Scalar_, int Options_, int Options2_, int Options3_, int NumIndices_, typename IndexType_,\
|
||||
template <class> class MakePointer_, size_t N, typename... Params>\
|
||||
struct ExprConstructor< CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer>,\
|
||||
CVQual Eigen::internal::PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options3_, MakePointer_>, N>, Params...>{\
|
||||
CVQual PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options3_, MakePointer_>, N>, Params...>{\
|
||||
typedef CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakeGlobalPointer> Type;\
|
||||
Type expr;\
|
||||
template <typename FuncDetector>\
|
||||
@ -187,7 +186,7 @@ EVALTO()
|
||||
#define FORCEDEVAL(CVQual)\
|
||||
template <typename OrigExpr, typename DevExpr, size_t N, typename... Params>\
|
||||
struct ExprConstructor<CVQual TensorForcedEvalOp<OrigExpr, MakeGlobalPointer>,\
|
||||
CVQual Eigen::internal::PlaceHolder<CVQual TensorForcedEvalOp<DevExpr>, N>, Params...> {\
|
||||
CVQual PlaceHolder<CVQual TensorForcedEvalOp<DevExpr>, N>, Params...> {\
|
||||
typedef CVQual TensorMap<Tensor<typename TensorForcedEvalOp<DevExpr, MakeGlobalPointer>::Scalar,\
|
||||
TensorForcedEvalOp<DevExpr, MakeGlobalPointer>::NumDimensions, 0, typename TensorForcedEvalOp<DevExpr>::Index>, 0, MakeGlobalPointer> Type;\
|
||||
Type expr;\
|
||||
@ -200,14 +199,41 @@ FORCEDEVAL(const)
|
||||
FORCEDEVAL()
|
||||
#undef FORCEDEVAL
|
||||
|
||||
template <bool Conds, size_t X , size_t Y > struct ValueCondition {
|
||||
static const size_t Res =X;
|
||||
};
|
||||
template<size_t X, size_t Y> struct ValueCondition<false, X , Y> {
|
||||
static const size_t Res =Y;
|
||||
};
|
||||
|
||||
/// specialisation of the \ref ExprConstructor struct when the node type is TensorReductionOp
|
||||
#define SYCLREDUCTIONEXPR(CVQual)\
|
||||
template <typename OP, typename Dim, typename OrigExpr, typename DevExpr, size_t N, typename... Params>\
|
||||
struct ExprConstructor<CVQual TensorReductionOp<OP, Dim, OrigExpr, MakeGlobalPointer>,\
|
||||
CVQual PlaceHolder<CVQual TensorReductionOp<OP, Dim, DevExpr>, N>, Params...> {\
|
||||
static const size_t NumIndices= ValueCondition< TensorReductionOp<OP, Dim, DevExpr, MakeGlobalPointer>::NumDimensions==0, 1, TensorReductionOp<OP, Dim, DevExpr, MakeGlobalPointer>::NumDimensions >::Res;\
|
||||
typedef CVQual TensorMap<Tensor<typename TensorReductionOp<OP, Dim, DevExpr, MakeGlobalPointer>::Scalar,\
|
||||
NumIndices, 0, typename TensorReductionOp<OP, Dim, DevExpr>::Index>, 0, MakeGlobalPointer> Type;\
|
||||
Type expr;\
|
||||
template <typename FuncDetector>\
|
||||
ExprConstructor(FuncDetector &fd, const utility::tuple::Tuple<Params...> &t)\
|
||||
: expr(Type((&(*(utility::tuple::get<N>(t).get_pointer()))), fd.dimensions())) {}\
|
||||
};
|
||||
|
||||
SYCLREDUCTIONEXPR(const)
|
||||
SYCLREDUCTIONEXPR()
|
||||
#undef SYCLREDUCTIONEXPR
|
||||
|
||||
/// template deduction for \ref ExprConstructor struct
|
||||
template <typename OrigExpr, typename IndexExpr, typename FuncD, typename... Params>
|
||||
auto createDeviceExpression(FuncD &funcD, const utility::tuple::Tuple<Params...> &t)
|
||||
-> decltype(ExprConstructor<OrigExpr, IndexExpr, Params...>(funcD, t)) {
|
||||
return ExprConstructor<OrigExpr, IndexExpr, Params...>(funcD, t);
|
||||
}
|
||||
}
|
||||
}
|
||||
} // namespace Eigen
|
||||
|
||||
} /// namespace TensorSycl
|
||||
} /// namespace internal
|
||||
} /// namespace Eigen
|
||||
|
||||
|
||||
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXPR_CONSTRUCTOR_HPP
|
||||
|
||||
@ -56,10 +56,10 @@ struct AccessorConstructor{
|
||||
-> decltype(utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1),utility::tuple::append(ExtractAccessor<Arg2>::getTuple(cgh, eval2), ExtractAccessor<Arg3>::getTuple(cgh, eval3)))) {
|
||||
return utility::tuple::append(ExtractAccessor<Arg1>::getTuple(cgh, eval1),utility::tuple::append(ExtractAccessor<Arg2>::getTuple(cgh, eval2), ExtractAccessor<Arg3>::getTuple(cgh, eval3)));
|
||||
}
|
||||
template< cl::sycl::access::mode AcM, typename Arg> static inline auto getAccessor(cl::sycl::handler& cgh, Arg eval)
|
||||
-> decltype(utility::tuple::make_tuple( eval.device().template get_sycl_accessor<AcM, true,
|
||||
template< cl::sycl::access::mode AcM, bool MapAllocator, typename Arg> static inline auto getAccessor(cl::sycl::handler& cgh, Arg eval)
|
||||
-> decltype(utility::tuple::make_tuple( eval.device().template get_sycl_accessor<AcM, MapAllocator,
|
||||
typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data()))){
|
||||
return utility::tuple::make_tuple(eval.device().template get_sycl_accessor<AcM, true, typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data()));
|
||||
return utility::tuple::make_tuple(eval.device().template get_sycl_accessor<AcM, MapAllocator, typename Eigen::internal::remove_all<typename Arg::CoeffReturnType>::type>(eval.dimensions().TotalSize(), cgh,eval.data()));
|
||||
}
|
||||
};
|
||||
|
||||
@ -73,14 +73,12 @@ struct ExtractAccessor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> >
|
||||
}
|
||||
};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// TensorCwiseNullaryOp, TensorCwiseUnaryOp and TensorBroadcastingOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is TensorCwiseNullaryOp, TensorCwiseUnaryOp and TensorBroadcastingOp
|
||||
template <template<class, class> class UnaryCategory, typename OP, typename RHSExpr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<UnaryCategory<OP, RHSExpr>, Dev> >
|
||||
: ExtractAccessor<TensorEvaluator<const UnaryCategory<OP, RHSExpr>, Dev> > {};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// const TensorCwiseBinaryOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorCwiseBinaryOp
|
||||
template <template<class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> > {
|
||||
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> eval)
|
||||
@ -88,9 +86,7 @@ struct ExtractAccessor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr
|
||||
return AccessorConstructor::getTuple(cgh, eval.left_impl(), eval.right_impl());
|
||||
}
|
||||
};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// TensorCwiseBinaryOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is TensorCwiseBinaryOp
|
||||
template <template<class, class, class> class BinaryCategory, typename OP, typename LHSExpr, typename RHSExpr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >
|
||||
: ExtractAccessor<TensorEvaluator<const BinaryCategory<OP, LHSExpr, RHSExpr>, Dev> >{};
|
||||
@ -105,8 +101,7 @@ struct ExtractAccessor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2E
|
||||
}
|
||||
};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// TensorCwiseTernaryOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is TensorCwiseTernaryOp
|
||||
template <template<class, class, class, class> class TernaryCategory, typename OP, typename Arg1Expr, typename Arg2Expr, typename Arg3Expr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >
|
||||
: ExtractAccessor<TensorEvaluator<const TernaryCategory<OP, Arg1Expr, Arg2Expr, Arg3Expr>, Dev> >{};
|
||||
@ -127,8 +122,7 @@ template <typename IfExpr, typename ThenExpr, typename ElseExpr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> >
|
||||
: ExtractAccessor<TensorEvaluator<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr>, Dev> >{};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// const TensorAssignOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorAssignOp
|
||||
template <typename LHSExpr, typename RHSExpr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> > {
|
||||
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> eval)
|
||||
@ -137,65 +131,74 @@ struct ExtractAccessor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, D
|
||||
}
|
||||
};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// TensorAssignOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is TensorAssignOp
|
||||
template <typename LHSExpr, typename RHSExpr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<TensorAssignOp<LHSExpr, RHSExpr>, Dev> >
|
||||
: ExtractAccessor<TensorEvaluator<const TensorAssignOp<LHSExpr, RHSExpr>, Dev> >{};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// const TensorMap
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorMap
|
||||
#define TENSORMAPEXPR(CVQual, ACCType)\
|
||||
template <typename PlainObjectType, int Options_, typename Dev>\
|
||||
struct ExtractAccessor<TensorEvaluator<CVQual TensorMap<PlainObjectType, Options_>, Dev> > {\
|
||||
static inline auto getTuple(cl::sycl::handler& cgh,const TensorEvaluator<CVQual TensorMap<PlainObjectType, Options_>, Dev> eval)\
|
||||
-> decltype(AccessorConstructor::template getAccessor<ACCType>(cgh, eval)){\
|
||||
return AccessorConstructor::template getAccessor<ACCType>(cgh, eval);\
|
||||
-> decltype(AccessorConstructor::template getAccessor<ACCType, true>(cgh, eval)){\
|
||||
return AccessorConstructor::template getAccessor<ACCType, true>(cgh, eval);\
|
||||
}\
|
||||
};
|
||||
TENSORMAPEXPR(const, cl::sycl::access::mode::read)
|
||||
TENSORMAPEXPR(, cl::sycl::access::mode::read_write)
|
||||
#undef TENSORMAPEXPR
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// const TensorForcedEvalOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorForcedEvalOp
|
||||
template <typename Expr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> > {
|
||||
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> eval)
|
||||
-> decltype(AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval)){
|
||||
return AccessorConstructor::template getAccessor<cl::sycl::access::mode::read>(cgh, eval);
|
||||
-> decltype(AccessorConstructor::template getAccessor<cl::sycl::access::mode::read, false>(cgh, eval)){
|
||||
return AccessorConstructor::template getAccessor<cl::sycl::access::mode::read, false>(cgh, eval);
|
||||
}
|
||||
};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// TensorForcedEvalOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is TensorForcedEvalOp
|
||||
template <typename Expr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<TensorForcedEvalOp<Expr>, Dev> >
|
||||
: ExtractAccessor<TensorEvaluator<const TensorForcedEvalOp<Expr>, Dev> >{};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// const TensorEvalToOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorEvalToOp
|
||||
template <typename Expr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<const TensorEvalToOp<Expr>, Dev> > {
|
||||
static inline auto getTuple(cl::sycl::handler& cgh,const TensorEvaluator<const TensorEvalToOp<Expr>, Dev> eval)
|
||||
-> decltype(utility::tuple::append(AccessorConstructor::template getAccessor<cl::sycl::access::mode::write>(cgh, eval), AccessorConstructor::getTuple(cgh, eval.impl()))){
|
||||
return utility::tuple::append(AccessorConstructor::template getAccessor<cl::sycl::access::mode::write>(cgh, eval), AccessorConstructor::getTuple(cgh, eval.impl()));
|
||||
-> decltype(utility::tuple::append(AccessorConstructor::template getAccessor<cl::sycl::access::mode::write, false>(cgh, eval), AccessorConstructor::getTuple(cgh, eval.impl()))){
|
||||
return utility::tuple::append(AccessorConstructor::template getAccessor<cl::sycl::access::mode::write, false>(cgh, eval), AccessorConstructor::getTuple(cgh, eval.impl()));
|
||||
}
|
||||
};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is
|
||||
/// TensorEvalToOp
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is TensorEvalToOp
|
||||
template <typename Expr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<TensorEvalToOp<Expr>, Dev> >
|
||||
: ExtractAccessor<TensorEvaluator<const TensorEvalToOp<Expr>, Dev> >{};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is const TensorReductionOp
|
||||
template <typename OP, typename Dim, typename Expr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<const TensorReductionOp<OP, Dim, Expr>, Dev> > {
|
||||
static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<const TensorReductionOp<OP, Dim, Expr>, Dev> eval)
|
||||
-> decltype(AccessorConstructor::template getAccessor<cl::sycl::access::mode::read, false>(cgh, eval)){
|
||||
return AccessorConstructor::template getAccessor<cl::sycl::access::mode::read, false>(cgh, eval);
|
||||
}
|
||||
};
|
||||
|
||||
/// specialisation of the \ref ExtractAccessor struct when the node type is TensorReductionOp
|
||||
template <typename OP, typename Dim, typename Expr, typename Dev>
|
||||
struct ExtractAccessor<TensorEvaluator<TensorReductionOp<OP, Dim, Expr>, Dev> >
|
||||
: ExtractAccessor<TensorEvaluator<const TensorReductionOp<OP, Dim, Expr>, Dev> >{};
|
||||
|
||||
/// template deduction for \ref ExtractAccessor
|
||||
template <typename Evaluator>
|
||||
auto createTupleOfAccessors(cl::sycl::handler& cgh, const Evaluator& expr)
|
||||
-> decltype(ExtractAccessor<Evaluator>::getTuple(cgh, expr)) {
|
||||
return ExtractAccessor<Evaluator>::getTuple(cgh, expr);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
} /// namespace TensorSycl
|
||||
} /// namespace internal
|
||||
} /// namespace Eigen
|
||||
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_EXTRACT_ACCESSOR_HPP
|
||||
|
||||
@ -141,7 +141,30 @@ template <typename RHSExpr, typename Dev>
|
||||
struct FunctorExtractor<TensorEvaluator<TensorEvalToOp<RHSExpr>, Dev> >
|
||||
: FunctorExtractor<TensorEvaluator<const TensorEvalToOp<RHSExpr>, Dev> > {};
|
||||
|
||||
template<typename Dim, size_t NumOutputDim> struct DimConstr {
|
||||
template<typename InDim>
|
||||
static inline Dim getDim(InDim dims ) {return dims;}
|
||||
};
|
||||
|
||||
template<typename Dim> struct DimConstr<Dim, 0> {
|
||||
template<typename InDim>
|
||||
static inline Dim getDim(InDim dims ) {return Dim(dims.TotalSize());}
|
||||
};
|
||||
|
||||
template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
|
||||
struct FunctorExtractor<TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>>{
|
||||
typedef TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device> Evaluator;
|
||||
typedef typename Eigen::internal::conditional<Evaluator::NumOutputDims==0, DSizes<typename Evaluator::Index, 1>, typename Evaluator::Dimensions >::type Dimensions;
|
||||
const Dimensions m_dimensions;
|
||||
const Dimensions& dimensions() const { return m_dimensions; }
|
||||
FunctorExtractor(const TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>& expr)
|
||||
: m_dimensions(DimConstr<Dimensions, Evaluator::NumOutputDims>::getDim(expr.dimensions())) {}
|
||||
};
|
||||
|
||||
|
||||
template<typename Op, typename Dims, typename ArgType, template <class> class MakePointer_, typename Device>
|
||||
struct FunctorExtractor<TensorEvaluator<TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>>
|
||||
: FunctorExtractor<TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType, MakePointer_>, Device>>{};
|
||||
/// template deduction function for FunctorExtractor
|
||||
template <typename Evaluator>
|
||||
auto inline extractFunctors(const Evaluator& evaluator)-> FunctorExtractor<Evaluator> {
|
||||
|
||||
@ -43,8 +43,7 @@ struct CategoryCount<Arg,Args...>{
|
||||
static const size_t Count = LeafCount<Arg>::Count + CategoryCount<Args...>::Count;
|
||||
};
|
||||
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const
|
||||
/// TensorMap
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const TensorMap
|
||||
template <typename PlainObjectType, int Options_, template <class> class MakePointer_>
|
||||
struct LeafCount<const TensorMap<PlainObjectType, Options_, MakePointer_> > {
|
||||
static const size_t Count =1;
|
||||
@ -61,18 +60,15 @@ struct LeafCount<const CategoryExpr<OP, RHSExpr...> >: CategoryCount<RHSExpr...>
|
||||
template <template <class, class...> class CategoryExpr, typename OP, typename... RHSExpr>
|
||||
struct LeafCount<CategoryExpr<OP, RHSExpr...> > :LeafCount<const CategoryExpr<OP, RHSExpr...> >{};
|
||||
|
||||
/// specialisation of the \ref LeafCount struct when the node type is
|
||||
/// const TensorSelectOp is an exception
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const TensorSelectOp is an exception
|
||||
template <typename IfExpr, typename ThenExpr, typename ElseExpr>
|
||||
struct LeafCount<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr> > : CategoryCount<IfExpr, ThenExpr, ElseExpr> {};
|
||||
/// specialisation of the \ref LeafCount struct when the node type is
|
||||
/// TensorSelectOp
|
||||
/// specialisation of the \ref LeafCount struct when the node type is TensorSelectOp
|
||||
template <typename IfExpr, typename ThenExpr, typename ElseExpr>
|
||||
struct LeafCount<TensorSelectOp<IfExpr, ThenExpr, ElseExpr> >: LeafCount<const TensorSelectOp<IfExpr, ThenExpr, ElseExpr> > {};
|
||||
|
||||
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const
|
||||
/// TensorAssignOp
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const TensorAssignOp
|
||||
template <typename LHSExpr, typename RHSExpr>
|
||||
struct LeafCount<const TensorAssignOp<LHSExpr, RHSExpr> >: CategoryCount<LHSExpr,RHSExpr> {};
|
||||
|
||||
@ -81,31 +77,38 @@ struct LeafCount<const TensorAssignOp<LHSExpr, RHSExpr> >: CategoryCount<LHSExpr
|
||||
template <typename LHSExpr, typename RHSExpr>
|
||||
struct LeafCount<TensorAssignOp<LHSExpr, RHSExpr> > :LeafCount<const TensorAssignOp<LHSExpr, RHSExpr> >{};
|
||||
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const
|
||||
/// TensorForcedEvalOp
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const TensorForcedEvalOp
|
||||
template <typename Expr>
|
||||
struct LeafCount<const TensorForcedEvalOp<Expr> > {
|
||||
static const size_t Count =1;
|
||||
};
|
||||
|
||||
/// specialisation of the \ref LeafCount struct when the node type is
|
||||
/// TensorForcedEvalOp
|
||||
/// specialisation of the \ref LeafCount struct when the node type is TensorForcedEvalOp
|
||||
template <typename Expr>
|
||||
struct LeafCount<TensorForcedEvalOp<Expr> >: LeafCount<const TensorForcedEvalOp<Expr> > {};
|
||||
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const
|
||||
/// TensorEvalToOp
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const TensorEvalToOp
|
||||
template <typename Expr>
|
||||
struct LeafCount<const TensorEvalToOp<Expr> > {
|
||||
static const size_t Count = 1 + CategoryCount<Expr>::Count;
|
||||
};
|
||||
|
||||
/// specialisation of the \ref LeafCount struct when the node type is
|
||||
/// TensorEvalToOp
|
||||
/// specialisation of the \ref LeafCount struct when the node type is const TensorReductionOp
|
||||
template <typename OP, typename Dim, typename Expr>
|
||||
struct LeafCount<const TensorReductionOp<OP, Dim, Expr> > {
|
||||
static const size_t Count =1;
|
||||
};
|
||||
|
||||
/// specialisation of the \ref LeafCount struct when the node type is TensorReductionOp
|
||||
template <typename OP, typename Dim, typename Expr>
|
||||
struct LeafCount<TensorReductionOp<OP, Dim, Expr> >: LeafCount<const TensorReductionOp<OP, Dim, Expr> >{};
|
||||
|
||||
/// specialisation of the \ref LeafCount struct when the node type is TensorEvalToOp
|
||||
template <typename Expr>
|
||||
struct LeafCount<TensorEvalToOp<Expr> >: LeafCount<const TensorEvalToOp<Expr> >{};
|
||||
}
|
||||
}
|
||||
} // namespace Eigen
|
||||
|
||||
} /// namespace TensorSycl
|
||||
} /// namespace internal
|
||||
} /// namespace Eigen
|
||||
|
||||
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_LEAF_COUNT_HPP
|
||||
|
||||
@ -1,99 +0,0 @@
|
||||
// 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>
|
||||
//
|
||||
// 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/.
|
||||
|
||||
/*****************************************************************
|
||||
* TensorSyclPlaceHolder.h
|
||||
*
|
||||
* \brief:
|
||||
* The PlaceHolder expression are nothing but a container preserving
|
||||
* the order of actual data in the tuple of sycl buffer.
|
||||
*
|
||||
*****************************************************************/
|
||||
|
||||
#ifndef UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_HPP
|
||||
#define UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_HPP
|
||||
|
||||
namespace Eigen {
|
||||
namespace internal {
|
||||
/// \struct PlaceHolder
|
||||
/// \brief PlaceHolder is used to replace the \ref TensorMap in the expression
|
||||
/// tree.
|
||||
/// PlaceHolder contains the order of the leaf node in the expression tree.
|
||||
template <typename Scalar, size_t N>
|
||||
struct PlaceHolder {
|
||||
static constexpr size_t I = N;
|
||||
typedef Scalar Type;
|
||||
};
|
||||
|
||||
/// \brief specialisation of the PlaceHolder node for const TensorMap
|
||||
#define TENSORMAPPLACEHOLDER(CVQual)\
|
||||
template <typename PlainObjectType, int Options_, template <class> class MakePointer_, size_t N>\
|
||||
struct PlaceHolder<CVQual TensorMap<PlainObjectType, Options_, MakePointer_>, N> {\
|
||||
static const size_t I = N;\
|
||||
typedef CVQual TensorMap<PlainObjectType, Options_, MakePointer_> Type;\
|
||||
typedef typename Type::Self Self;\
|
||||
typedef typename Type::Base Base;\
|
||||
typedef typename Type::Nested Nested;\
|
||||
typedef typename Type::StorageKind StorageKind;\
|
||||
typedef typename Type::Index Index;\
|
||||
typedef typename Type::Scalar Scalar;\
|
||||
typedef typename Type::RealScalar RealScalar;\
|
||||
typedef typename Type::CoeffReturnType CoeffReturnType;\
|
||||
};
|
||||
|
||||
TENSORMAPPLACEHOLDER(const)
|
||||
TENSORMAPPLACEHOLDER()
|
||||
#undef TENSORMAPPLACEHOLDER
|
||||
|
||||
/// \brief specialisation of the PlaceHolder node for TensorForcedEvalOp. The
|
||||
/// TensorForcedEvalOp acts as a leaf node for its parent node.
|
||||
#define TENSORFORCEDEVALPLACEHOLDER(CVQual)\
|
||||
template <typename Expression, size_t N>\
|
||||
struct PlaceHolder<CVQual TensorForcedEvalOp<Expression>, N> {\
|
||||
static const size_t I = N;\
|
||||
typedef CVQual TensorForcedEvalOp<Expression> Type;\
|
||||
typedef typename Type::Nested Nested;\
|
||||
typedef typename Type::StorageKind StorageKind;\
|
||||
typedef typename Type::Index Index;\
|
||||
typedef typename Type::Scalar Scalar;\
|
||||
typedef typename Type::Packet Packet;\
|
||||
typedef typename Type::RealScalar RealScalar;\
|
||||
typedef typename Type::CoeffReturnType CoeffReturnType;\
|
||||
typedef typename Type::PacketReturnType PacketReturnType;\
|
||||
};
|
||||
|
||||
TENSORFORCEDEVALPLACEHOLDER(const)
|
||||
TENSORFORCEDEVALPLACEHOLDER()
|
||||
#undef TENSORFORCEDEVALPLACEHOLDER
|
||||
|
||||
template <typename PlainObjectType, int Options_, template <class> class Makepointer_, size_t N>
|
||||
struct traits<PlaceHolder<const TensorMap<PlainObjectType, Options_, Makepointer_>, N> >: public traits<PlainObjectType> {
|
||||
typedef traits<PlainObjectType> BaseTraits;
|
||||
typedef typename BaseTraits::Scalar Scalar;
|
||||
typedef typename BaseTraits::StorageKind StorageKind;
|
||||
typedef typename BaseTraits::Index Index;
|
||||
static const int NumDimensions = BaseTraits::NumDimensions;
|
||||
static const int Layout = BaseTraits::Layout;
|
||||
enum {
|
||||
Options = Options_,
|
||||
Flags = BaseTraits::Flags,
|
||||
};
|
||||
};
|
||||
|
||||
template <typename PlainObjectType, int Options_, template <class> class Makepointer_, size_t N>
|
||||
struct traits<PlaceHolder<TensorMap<PlainObjectType, Options_, Makepointer_>, N> >
|
||||
: traits<PlaceHolder<const TensorMap<PlainObjectType, Options_, Makepointer_>, N> > {};
|
||||
|
||||
} // end namespace internal
|
||||
} // end namespoace Eigen
|
||||
|
||||
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_HPP
|
||||
@ -25,6 +25,17 @@
|
||||
namespace Eigen {
|
||||
namespace TensorSycl {
|
||||
namespace internal {
|
||||
|
||||
/// \struct PlaceHolder
|
||||
/// \brief PlaceHolder is used to replace the \ref TensorMap in the expression
|
||||
/// tree.
|
||||
/// PlaceHolder contains the order of the leaf node in the expression tree.
|
||||
template <typename Scalar, size_t N>
|
||||
struct PlaceHolder {
|
||||
static constexpr size_t I = N;
|
||||
typedef Scalar Type;
|
||||
};
|
||||
|
||||
/// \sttruct PlaceHolderExpression
|
||||
/// \brief it is used to create the PlaceHolder expression. The PlaceHolder
|
||||
/// expression is a copy of expression type in which the TensorMap of the has
|
||||
@ -113,7 +124,7 @@ ASSIGNEXPR()
|
||||
#define TENSORMAPEXPR(CVQual)\
|
||||
template <typename Scalar_, int Options_, int Options2_, int NumIndices_, typename IndexType_, template <class> class MakePointer_, size_t N>\
|
||||
struct PlaceHolderExpression< CVQual TensorMap< Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> {\
|
||||
typedef CVQual Eigen::internal::PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> Type;\
|
||||
typedef CVQual PlaceHolder<CVQual TensorMap<Tensor<Scalar_, NumIndices_, Options_, IndexType_>, Options2_, MakePointer_>, N> Type;\
|
||||
};
|
||||
|
||||
TENSORMAPEXPR(const)
|
||||
@ -125,7 +136,7 @@ TENSORMAPEXPR()
|
||||
#define FORCEDEVAL(CVQual)\
|
||||
template <typename Expr, size_t N>\
|
||||
struct PlaceHolderExpression<CVQual TensorForcedEvalOp<Expr>, N> {\
|
||||
typedef CVQual Eigen::internal::PlaceHolder<CVQual TensorForcedEvalOp<Expr>, N> Type;\
|
||||
typedef CVQual PlaceHolder<CVQual TensorForcedEvalOp<Expr>, N> Type;\
|
||||
};
|
||||
|
||||
FORCEDEVAL(const)
|
||||
@ -144,6 +155,18 @@ EVALTO(const)
|
||||
EVALTO()
|
||||
#undef EVALTO
|
||||
|
||||
|
||||
/// specialisation of the \ref PlaceHolderExpression when the node is
|
||||
/// TensorReductionOp
|
||||
#define SYCLREDUCTION(CVQual)\
|
||||
template <typename OP, typename Dims, typename Expr, size_t N>\
|
||||
struct PlaceHolderExpression<CVQual TensorReductionOp<OP, Dims, Expr>, N>{\
|
||||
typedef CVQual PlaceHolder<CVQual TensorReductionOp<OP, Dims,Expr>, N> Type;\
|
||||
};
|
||||
SYCLREDUCTION(const)
|
||||
SYCLREDUCTION()
|
||||
#undef SYCLREDUCTION
|
||||
|
||||
/// template deduction for \ref PlaceHolderExpression struct
|
||||
template <typename Expr>
|
||||
struct createPlaceHolderExpression {
|
||||
@ -151,8 +174,8 @@ struct createPlaceHolderExpression {
|
||||
typedef typename PlaceHolderExpression<Expr, TotalLeaves - 1>::Type Type;
|
||||
};
|
||||
|
||||
}
|
||||
}
|
||||
} // internal
|
||||
} // TensorSycl
|
||||
} // namespace Eigen
|
||||
|
||||
#endif // UNSUPPORTED_EIGEN_CXX11_SRC_TENSOR_TENSORSYCL_PLACEHOLDER_EXPR_HPP
|
||||
|
||||
@ -37,20 +37,20 @@ void run(Expr &expr, Dev &dev) {
|
||||
typedef typename internal::createPlaceHolderExpression<Expr>::Type PlaceHolderExpr;
|
||||
auto functors = internal::extractFunctors(evaluator);
|
||||
|
||||
size_t tileSize =dev.m_queue.get_device(). template get_info<cl::sycl::info::device::max_work_group_size>()/2;
|
||||
dev.m_queue.submit([&](cl::sycl::handler &cgh) {
|
||||
|
||||
// create a tuple of accessors from Evaluator
|
||||
auto tuple_of_accessors = internal::createTupleOfAccessors<decltype(evaluator)>(cgh, evaluator);
|
||||
const auto range = utility::tuple::get<0>(tuple_of_accessors).get_range()[0];
|
||||
|
||||
size_t outTileSize = range;
|
||||
if (range > 64) outTileSize = 64;
|
||||
size_t yMode = range % outTileSize;
|
||||
int yRange = static_cast<int>(range);
|
||||
if (yMode != 0) yRange += (outTileSize - yMode);
|
||||
|
||||
size_t GRange=range;
|
||||
if (tileSize>GRange) tileSize=GRange;
|
||||
else if(GRange>tileSize){
|
||||
size_t xMode = GRange % tileSize;
|
||||
if (xMode != 0) GRange += (tileSize - xMode);
|
||||
}
|
||||
// run the kernel
|
||||
cgh.parallel_for<PlaceHolderExpr>( cl::sycl::nd_range<1>(cl::sycl::range<1>(yRange), cl::sycl::range<1>(outTileSize)), [=](cl::sycl::nd_item<1> itemID) {
|
||||
cgh.parallel_for<PlaceHolderExpr>( cl::sycl::nd_range<1>(cl::sycl::range<1>(GRange), cl::sycl::range<1>(tileSize)), [=](cl::sycl::nd_item<1> itemID) {
|
||||
typedef typename internal::ConvertToDeviceExpression<Expr>::Type DevExpr;
|
||||
auto device_expr =internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
|
||||
auto device_evaluator = Eigen::TensorEvaluator<decltype(device_expr.expr), Eigen::DefaultDevice>(device_expr.expr, Eigen::DefaultDevice());
|
||||
@ -61,6 +61,7 @@ void run(Expr &expr, Dev &dev) {
|
||||
});
|
||||
dev.m_queue.throw_asynchronous();
|
||||
}
|
||||
|
||||
evaluator.cleanup();
|
||||
}
|
||||
} // namespace TensorSycl
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
# generate split test header file only if it does not yet exist
|
||||
# in order to prevent a rebuild everytime cmake is configured
|
||||
if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h)
|
||||
if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h)
|
||||
file(WRITE ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h "")
|
||||
foreach(i RANGE 1 999)
|
||||
file(APPEND ${CMAKE_CURRENT_BINARY_DIR}/split_test_helper.h
|
||||
@ -16,11 +16,11 @@ endif()
|
||||
set_property(GLOBAL PROPERTY EIGEN_CURRENT_SUBPROJECT "Unsupported")
|
||||
add_custom_target(BuildUnsupported)
|
||||
|
||||
include_directories(../../test ../../unsupported ../../Eigen
|
||||
include_directories(../../test ../../unsupported ../../Eigen
|
||||
${CMAKE_CURRENT_BINARY_DIR}/../../test)
|
||||
|
||||
find_package (Threads)
|
||||
|
||||
|
||||
find_package(GoogleHash)
|
||||
if(GOOGLEHASH_FOUND)
|
||||
add_definitions("-DEIGEN_GOOGLEHASH_SUPPORT")
|
||||
@ -134,7 +134,7 @@ ei_add_test(cxx11_tensor_roundings)
|
||||
ei_add_test(cxx11_tensor_layout_swap)
|
||||
ei_add_test(cxx11_tensor_io)
|
||||
if("${CMAKE_SIZEOF_VOID_P}" EQUAL "8")
|
||||
# This test requires __uint128_t which is only available on 64bit systems
|
||||
# This test requires __uint128_t which is only available on 64bit systems
|
||||
ei_add_test(cxx11_tensor_uint128)
|
||||
endif()
|
||||
endif()
|
||||
@ -145,6 +145,7 @@ if(EIGEN_TEST_CXX11)
|
||||
ei_add_test_sycl(cxx11_tensor_forced_eval_sycl "-std=c++11")
|
||||
ei_add_test_sycl(cxx11_tensor_broadcast_sycl "-std=c++11")
|
||||
ei_add_test_sycl(cxx11_tensor_device_sycl "-std=c++11")
|
||||
ei_add_test_sycl(cxx11_tensor_reduction_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.
|
||||
|
||||
147
unsupported/test/cxx11_tensor_reduction_sycl.cpp
Normal file
147
unsupported/test/cxx11_tensor_reduction_sycl.cpp
Normal file
@ -0,0 +1,147 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2015
|
||||
// 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_reduction_sycl
|
||||
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
|
||||
#define EIGEN_USE_SYCL
|
||||
|
||||
#include "main.h"
|
||||
#include <unsupported/Eigen/CXX11/Tensor>
|
||||
|
||||
|
||||
|
||||
static void test_full_reductions_sycl() {
|
||||
|
||||
|
||||
cl::sycl::gpu_selector s;
|
||||
cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
|
||||
for (const auto& e : l) {
|
||||
try {
|
||||
std::rethrow_exception(e);
|
||||
} catch (cl::sycl::exception e) {
|
||||
std::cout << e.what() << std::endl;
|
||||
}
|
||||
}
|
||||
});
|
||||
Eigen::SyclDevice sycl_device(q);
|
||||
|
||||
const int num_rows = 452;
|
||||
const int num_cols = 765;
|
||||
array<int, 2> tensorRange = {{num_rows, num_cols}};
|
||||
|
||||
Tensor<float, 2> in(tensorRange);
|
||||
in.setRandom();
|
||||
|
||||
Tensor<float, 0> full_redux;
|
||||
Tensor<float, 0> full_redux_g;
|
||||
full_redux = in.sum();
|
||||
float* out_data = (float*)sycl_device.allocate(sizeof(float));
|
||||
TensorMap<Tensor<float, 2> > in_gpu(in.data(), tensorRange);
|
||||
TensorMap<Tensor<float, 0> > full_redux_gpu(out_data);
|
||||
full_redux_gpu.device(sycl_device) = in_gpu.sum();
|
||||
sycl_device.deallocate(out_data);
|
||||
// Check that the CPU and GPU reductions return the same result.
|
||||
VERIFY_IS_APPROX(full_redux_gpu(), full_redux());
|
||||
|
||||
}
|
||||
|
||||
|
||||
static void test_first_dim_reductions_sycl() {
|
||||
|
||||
|
||||
cl::sycl::gpu_selector s;
|
||||
cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
|
||||
for (const auto& e : l) {
|
||||
try {
|
||||
std::rethrow_exception(e);
|
||||
} catch (cl::sycl::exception e) {
|
||||
std::cout << e.what() << std::endl;
|
||||
}
|
||||
}
|
||||
});
|
||||
Eigen::SyclDevice sycl_device(q);
|
||||
|
||||
int dim_x = 145;
|
||||
int dim_y = 1;
|
||||
int dim_z = 67;
|
||||
|
||||
array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}};
|
||||
|
||||
Tensor<float, 3> in(tensorRange);
|
||||
in.setRandom();
|
||||
Eigen::array<int, 1> red_axis;
|
||||
red_axis[0] = 0;
|
||||
Tensor<float, 2> redux = in.sum(red_axis);
|
||||
array<int, 2> reduced_tensorRange = {{dim_y, dim_z}};
|
||||
Tensor<float, 2> redux_g(reduced_tensorRange);
|
||||
TensorMap<Tensor<float, 3> > in_gpu(in.data(), tensorRange);
|
||||
float* out_data = (float*)sycl_device.allocate(dim_y*dim_z*sizeof(float));
|
||||
TensorMap<Tensor<float, 2> > redux_gpu(out_data, dim_y, dim_z );
|
||||
redux_gpu.device(sycl_device) = in_gpu.sum(red_axis);
|
||||
|
||||
sycl_device.deallocate(out_data);
|
||||
// Check that the CPU and GPU reductions return the same result.
|
||||
for(int j=0; j<dim_y; j++ )
|
||||
for(int k=0; k<dim_z; k++ )
|
||||
VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
|
||||
}
|
||||
|
||||
|
||||
static void test_last_dim_reductions_sycl() {
|
||||
|
||||
|
||||
cl::sycl::gpu_selector s;
|
||||
cl::sycl::queue q(s, [=](cl::sycl::exception_list l) {
|
||||
for (const auto& e : l) {
|
||||
try {
|
||||
std::rethrow_exception(e);
|
||||
} catch (cl::sycl::exception e) {
|
||||
std::cout << e.what() << std::endl;
|
||||
}
|
||||
}
|
||||
});
|
||||
Eigen::SyclDevice sycl_device(q);
|
||||
|
||||
int dim_x = 567;
|
||||
int dim_y = 1;
|
||||
int dim_z = 47;
|
||||
|
||||
array<int, 3> tensorRange = {{dim_x, dim_y, dim_z}};
|
||||
|
||||
Tensor<float, 3> in(tensorRange);
|
||||
in.setRandom();
|
||||
Eigen::array<int, 1> red_axis;
|
||||
red_axis[0] = 2;
|
||||
Tensor<float, 2> redux = in.sum(red_axis);
|
||||
array<int, 2> reduced_tensorRange = {{dim_x, dim_y}};
|
||||
Tensor<float, 2> redux_g(reduced_tensorRange);
|
||||
TensorMap<Tensor<float, 3> > in_gpu(in.data(), tensorRange);
|
||||
float* out_data = (float*)sycl_device.allocate(dim_x*dim_y*sizeof(float));
|
||||
TensorMap<Tensor<float, 2> > redux_gpu(out_data, dim_x, dim_y );
|
||||
redux_gpu.device(sycl_device) = in_gpu.sum(red_axis);
|
||||
|
||||
sycl_device.deallocate(out_data);
|
||||
// Check that the CPU and GPU reductions return the same result.
|
||||
for(int j=0; j<dim_x; j++ )
|
||||
for(int k=0; k<dim_y; k++ )
|
||||
VERIFY_IS_APPROX(redux_gpu(j,k), redux(j,k));
|
||||
}
|
||||
|
||||
void test_cxx11_tensor_reduction_sycl() {
|
||||
CALL_SUBTEST((test_full_reductions_sycl()));
|
||||
CALL_SUBTEST((test_first_dim_reductions_sycl()));
|
||||
CALL_SUBTEST((test_last_dim_reductions_sycl()));
|
||||
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user