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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 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_EVALUATOR_H
#define EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \class TensorEvaluator
* \ingroup CXX11_Tensor_Module
*
* \brief The tensor evaluator classes.
*
* These classes are responsible for the evaluation of the tensor expression.
*
* TODO: add support for more types of expressions, in particular expressions
* leading to lvalues (slicing, reshaping, etc...)
*/
// Generic evaluator
template<typename Derived, typename Device>
struct TensorEvaluator
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Scalar CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
typedef typename Derived::Dimensions Dimensions;
typedef Derived XprType;
static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef typename internal::traits<Derived>::template MakePointer<Scalar>::Type TensorPointerType;
typedef StorageMemory<Scalar, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
// NumDimensions is -1 for variable dim tensors
static constexpr int NumCoords = internal::traits<Derived>::NumDimensions > 0 ?
internal::traits<Derived>::NumDimensions : 0;
static constexpr int Layout = Derived::Layout;
enum {
IsAligned = Derived::IsAligned,
PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
BlockAccess = internal::is_arithmetic<std::remove_const_t<Scalar>>::value,
PreferBlockAccess = false,
CoordAccess = NumCoords > 0,
RawAccess = true
};
typedef std::remove_const_t<Scalar> ScalarNoConst;
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords,
Layout, Index>
TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
: m_data(device.get((const_cast<TensorPointerType>(m.data())))),
m_dims(m.dimensions()),
m_device(device)
{ }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType dest) {
if (!NumTraits<std::remove_const_t<Scalar>>::RequireInitialization && dest) {
m_device.memcpy((void*)(m_device.get(dest)), m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
return false;
}
return true;
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType dest, EvalSubExprsCallback done) {
// TODO(ezhulenev): ThreadPoolDevice memcpy is blockign operation.
done(evalSubExprsIfNeeded(dest));
}
#endif // EIGEN_USE_THREADS
EIGEN_STRONG_INLINE void cleanup() {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
eigen_assert(m_data != NULL);
return m_data[index];
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType& coeffRef(Index index) const{
eigen_assert(m_data != NULL);
return m_data[index];
}
template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketReturnType packet(Index index) const
{
return internal::ploadt<PacketReturnType, LoadMode>(m_data + index);
}
// Return a packet starting at `index` where `umask` specifies which elements
// have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
// Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
// float element will be loaded, otherwise 0 will be loaded.
// Function has been templatized to enable Sfinae.
template <typename PacketReturnTypeT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
std::enable_if_t<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>
partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const
{
return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
}
template <int StoreMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
void writePacket(Index index, const PacketReturnType& x) const
{
return internal::pstoret<Scalar, PacketReturnType, StoreMode>(m_data + index, x);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
eigen_assert(m_data != NULL);
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return m_data[m_dims.IndexOfColMajor(coords)];
} else {
return m_data[m_dims.IndexOfRowMajor(coords)];
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType&
coeffRef(const array<DenseIndex, NumCoords>& coords) const {
eigen_assert(m_data != NULL);
if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) {
return m_data[m_dims.IndexOfColMajor(coords)];
} else {
return m_data[m_dims.IndexOfRowMajor(coords)];
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
PacketType<CoeffReturnType, Device>::size);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
return internal::TensorBlockResourceRequirements::any();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
bool /*root_of_expr_ast*/ = false) const {
eigen_assert(m_data != NULL);
return TensorBlock::materialize(m_data, m_dims, desc, scratch);
}
template<typename TensorBlock>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void writeBlock(
const TensorBlockDesc& desc, const TensorBlock& block) {
eigen_assert(m_data != NULL);
typedef typename TensorBlock::XprType TensorBlockExpr;
typedef internal::TensorBlockAssignment<Scalar, NumCoords, TensorBlockExpr,
Index>
TensorBlockAssign;
TensorBlockAssign::Run(
TensorBlockAssign::target(desc.dimensions(),
internal::strides<Layout>(m_dims), m_data,
desc.offset()),
block.expr());
}
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
m_data.bind(cgh);
}
#endif
protected:
EvaluatorPointerType m_data;
Dimensions m_dims;
const Device EIGEN_DEVICE_REF m_device;
};
namespace internal {
template <typename T> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
T loadConstant(const T* address) {
return *address;
}
// Use the texture cache on CUDA devices whenever possible
#if defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350
template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
float loadConstant(const float* address) {
return __ldg(address);
}
template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
double loadConstant(const double* address) {
return __ldg(address);
}
template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
Eigen::half loadConstant(const Eigen::half* address) {
return Eigen::half(half_impl::raw_uint16_to_half(__ldg(&address->x)));
}
#endif
#ifdef EIGEN_USE_SYCL
// overload of load constant should be implemented here based on range access
template <cl::sycl::access::mode AcMd, typename T>
T &loadConstant(const Eigen::TensorSycl::internal::RangeAccess<AcMd, T> &address) {
return *address;
}
#endif
} // namespace internal
// Default evaluator for rvalues
template<typename Derived, typename Device>
struct TensorEvaluator<const Derived, Device>
{
typedef typename Derived::Index Index;
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Scalar CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
typedef typename Derived::Dimensions Dimensions;
typedef const Derived XprType;
typedef typename internal::traits<Derived>::template MakePointer<const Scalar>::Type TensorPointerType;
typedef StorageMemory<const Scalar, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
typedef std::remove_const_t<Scalar> ScalarNoConst;
// NumDimensions is -1 for variable dim tensors
static constexpr int NumCoords = internal::traits<Derived>::NumDimensions > 0 ?
internal::traits<Derived>::NumDimensions : 0;
static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
static constexpr int Layout = Derived::Layout;
enum {
IsAligned = Derived::IsAligned,
PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
BlockAccess = internal::is_arithmetic<ScalarNoConst>::value,
PreferBlockAccess = false,
CoordAccess = NumCoords > 0,
RawAccess = true
};
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockDescriptor<NumCoords, Index> TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
typedef typename internal::TensorMaterializedBlock<ScalarNoConst, NumCoords,
Layout, Index>
TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_STRONG_INLINE TensorEvaluator(const Derived& m, const Device& device)
: m_data(device.get(m.data())), m_dims(m.dimensions()), m_device(device)
{ }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dims; }
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) {
if (!NumTraits<std::remove_const_t<Scalar>>::RequireInitialization && data) {
m_device.memcpy((void*)(m_device.get(data)),m_device.get(m_data), m_dims.TotalSize() * sizeof(Scalar));
return false;
}
return true;
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType dest, EvalSubExprsCallback done) {
// TODO(ezhulenev): ThreadPoolDevice memcpy is a blockign operation.
done(evalSubExprsIfNeeded(dest));
}
#endif // EIGEN_USE_THREADS
EIGEN_STRONG_INLINE void cleanup() { }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const {
eigen_assert(m_data != NULL);
return internal::loadConstant(m_data+index);
}
template<int LoadMode> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketReturnType packet(Index index) const
{
return internal::ploadt_ro<PacketReturnType, LoadMode>(m_data + index);
}
// Return a packet starting at `index` where `umask` specifies which elements
// have to be loaded. Type/size of mask depends on PacketReturnType, e.g. for
// Packet16f, `umask` is of type uint16_t and if a bit is 1, corresponding
// float element will be loaded, otherwise 0 will be loaded.
// Function has been templatized to enable Sfinae.
template <typename PacketReturnTypeT> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
std::enable_if_t<internal::unpacket_traits<PacketReturnTypeT>::masked_load_available, PacketReturnTypeT>
partialPacket(Index index, typename internal::unpacket_traits<PacketReturnTypeT>::mask_t umask) const
{
return internal::ploadu<PacketReturnTypeT>(m_data + index, umask);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(const array<DenseIndex, NumCoords>& coords) const {
eigen_assert(m_data != NULL);
const Index index = (static_cast<int>(Layout) == static_cast<int>(ColMajor)) ? m_dims.IndexOfColMajor(coords)
: m_dims.IndexOfRowMajor(coords);
return internal::loadConstant(m_data+index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
PacketType<CoeffReturnType, Device>::size);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
return internal::TensorBlockResourceRequirements::any();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
bool /*root_of_expr_ast*/ = false) const {
eigen_assert(m_data != NULL);
return TensorBlock::materialize(m_data, m_dims, desc, scratch);
}
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_data; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
m_data.bind(cgh);
}
#endif
protected:
EvaluatorPointerType m_data;
Dimensions m_dims;
const Device EIGEN_DEVICE_REF m_device;
};
// -------------------- CwiseNullaryOp --------------------
template<typename NullaryOp, typename ArgType, typename Device>
struct TensorEvaluator<const TensorCwiseNullaryOp<NullaryOp, ArgType>, Device>
{
typedef TensorCwiseNullaryOp<NullaryOp, ArgType> XprType;
TensorEvaluator(const XprType& op, const Device& device)
: m_functor(op.functor()), m_argImpl(op.nestedExpression(), device), m_wrapper()
{ }
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
enum {
IsAligned = true,
PacketAccess = internal::functor_traits<NullaryOp>::PacketAccess
#ifdef EIGEN_USE_SYCL
&& (PacketType<CoeffReturnType, Device>::size >1)
#endif
,
BlockAccess = false,
PreferBlockAccess = false,
CoordAccess = false, // to be implemented
RawAccess = false
};
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockNotImplemented TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) { return true; }
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType, EvalSubExprsCallback done) {
done(true);
}
#endif // EIGEN_USE_THREADS
EIGEN_STRONG_INLINE void cleanup() { }
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_wrapper(m_functor, index);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_wrapper.template packetOp<PacketReturnType, Index>(m_functor, index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized,
PacketType<CoeffReturnType, Device>::size);
}
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
m_argImpl.bind(cgh);
}
#endif
private:
const NullaryOp m_functor;
TensorEvaluator<ArgType, Device> m_argImpl;
const internal::nullary_wrapper<CoeffReturnType,NullaryOp> m_wrapper;
};
// -------------------- CwiseUnaryOp --------------------
template<typename UnaryOp, typename ArgType, typename Device>
struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device>
{
typedef TensorCwiseUnaryOp<UnaryOp, ArgType> XprType;
static constexpr int Layout = TensorEvaluator<ArgType, Device>::Layout;
enum {
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = int(TensorEvaluator<ArgType, Device>::PacketAccess) &
int(internal::functor_traits<UnaryOp>::PacketAccess),
BlockAccess = TensorEvaluator<ArgType, Device>::BlockAccess,
PreferBlockAccess = TensorEvaluator<ArgType, Device>::PreferBlockAccess,
CoordAccess = false, // to be implemented
RawAccess = false
};
TensorEvaluator(const XprType& op, const Device& device)
: m_device(device),
m_functor(op.functor()),
m_argImpl(op.nestedExpression(), device)
{ }
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef std::remove_const_t<Scalar> ScalarNoConst;
typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
static constexpr int NumDims = internal::array_size<Dimensions>::value;
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
typedef typename TensorEvaluator<const ArgType, Device>::TensorBlock
ArgTensorBlock;
typedef internal::TensorCwiseUnaryBlock<UnaryOp, ArgTensorBlock>
TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const { return m_argImpl.dimensions(); }
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
m_argImpl.evalSubExprsIfNeeded(NULL);
return true;
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType, EvalSubExprsCallback done) {
m_argImpl.evalSubExprsIfNeededAsync(nullptr, [done](bool) { done(true); });
}
#endif // EIGEN_USE_THREADS
EIGEN_STRONG_INLINE void cleanup() {
m_argImpl.cleanup();
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_functor(m_argImpl.coeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const {
const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
return m_argImpl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
static const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
return m_argImpl.getResourceRequirements().addCostPerCoeff(
{0, 0, functor_cost / PacketSize});
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
bool /*root_of_expr_ast*/ = false) const {
return TensorBlock(m_argImpl.block(desc, scratch), m_functor);
}
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const{
m_argImpl.bind(cgh);
}
#endif
private:
const Device EIGEN_DEVICE_REF m_device;
const UnaryOp m_functor;
TensorEvaluator<ArgType, Device> m_argImpl;
};
// -------------------- CwiseBinaryOp --------------------
template<typename BinaryOp, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType>, Device>
{
typedef TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArgType> XprType;
static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
enum {
IsAligned = int(TensorEvaluator<LeftArgType, Device>::IsAligned) &
int(TensorEvaluator<RightArgType, Device>::IsAligned),
PacketAccess = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) &
int(TensorEvaluator<RightArgType, Device>::PacketAccess) &
int(internal::functor_traits<BinaryOp>::PacketAccess),
BlockAccess = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) &
int(TensorEvaluator<RightArgType, Device>::BlockAccess),
PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) |
int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
CoordAccess = false, // to be implemented
RawAccess = false
};
TensorEvaluator(const XprType& op, const Device& device)
: m_device(device),
m_functor(op.functor()),
m_leftImpl(op.lhsExpression(), device),
m_rightImpl(op.rhsExpression(), device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<LeftArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<RightArgType, Device>::Layout) || internal::traits<XprType>::NumDimensions <= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef typename TensorEvaluator<LeftArgType, Device>::Dimensions Dimensions;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
static constexpr int NumDims = internal::array_size<
typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
typedef typename TensorEvaluator<const LeftArgType, Device>::TensorBlock
LeftTensorBlock;
typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock
RightTensorBlock;
typedef internal::TensorCwiseBinaryBlock<BinaryOp, LeftTensorBlock,
RightTensorBlock>
TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
{
// TODO: use right impl instead if right impl dimensions are known at compile time.
return m_leftImpl.dimensions();
}
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
m_leftImpl.evalSubExprsIfNeeded(NULL);
m_rightImpl.evalSubExprsIfNeeded(NULL);
return true;
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType, EvalSubExprsCallback done) {
// TODO(ezhulenev): Evaluate two expression in parallel?
m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done](bool) {
m_rightImpl.evalSubExprsIfNeededAsync(nullptr,
[done](bool) { done(true); });
});
}
#endif // EIGEN_USE_THREADS
EIGEN_STRONG_INLINE void cleanup() {
m_leftImpl.cleanup();
m_rightImpl.cleanup();
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_functor(m_leftImpl.coeff(index), m_rightImpl.coeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_functor.packetOp(m_leftImpl.template packet<LoadMode>(index), m_rightImpl.template packet<LoadMode>(index));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
return m_leftImpl.costPerCoeff(vectorized) +
m_rightImpl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
static const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
return internal::TensorBlockResourceRequirements::merge(
m_leftImpl.getResourceRequirements(),
m_rightImpl.getResourceRequirements())
.addCostPerCoeff({0, 0, functor_cost / PacketSize});
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
bool /*root_of_expr_ast*/ = false) const {
desc.DropDestinationBuffer();
return TensorBlock(m_leftImpl.block(desc, scratch),
m_rightImpl.block(desc, scratch), m_functor);
}
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
m_leftImpl.bind(cgh);
m_rightImpl.bind(cgh);
}
#endif
private:
const Device EIGEN_DEVICE_REF m_device;
const BinaryOp m_functor;
TensorEvaluator<LeftArgType, Device> m_leftImpl;
TensorEvaluator<RightArgType, Device> m_rightImpl;
};
// -------------------- CwiseTernaryOp --------------------
template<typename TernaryOp, typename Arg1Type, typename Arg2Type, typename Arg3Type, typename Device>
struct TensorEvaluator<const TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type>, Device>
{
typedef TensorCwiseTernaryOp<TernaryOp, Arg1Type, Arg2Type, Arg3Type> XprType;
static constexpr int Layout = TensorEvaluator<Arg1Type, Device>::Layout;
enum {
IsAligned = TensorEvaluator<Arg1Type, Device>::IsAligned & TensorEvaluator<Arg2Type, Device>::IsAligned & TensorEvaluator<Arg3Type, Device>::IsAligned,
PacketAccess = TensorEvaluator<Arg1Type, Device>::PacketAccess &&
TensorEvaluator<Arg2Type, Device>::PacketAccess &&
TensorEvaluator<Arg3Type, Device>::PacketAccess &&
internal::functor_traits<TernaryOp>::PacketAccess,
BlockAccess = false,
PreferBlockAccess = TensorEvaluator<Arg1Type, Device>::PreferBlockAccess ||
TensorEvaluator<Arg2Type, Device>::PreferBlockAccess ||
TensorEvaluator<Arg3Type, Device>::PreferBlockAccess,
CoordAccess = false, // to be implemented
RawAccess = false
};
TensorEvaluator(const XprType& op, const Device& device)
: m_functor(op.functor()),
m_arg1Impl(op.arg1Expression(), device),
m_arg2Impl(op.arg2Expression(), device),
m_arg3Impl(op.arg3Expression(), device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<Arg1Type, Device>::Layout) == static_cast<int>(TensorEvaluator<Arg3Type, Device>::Layout) || internal::traits<XprType>::NumDimensions <= 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg2Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::StorageKind,
typename internal::traits<Arg3Type>::StorageKind>::value),
STORAGE_KIND_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
typename internal::traits<Arg2Type>::Index>::value),
STORAGE_INDEX_MUST_MATCH)
EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Arg1Type>::Index,
typename internal::traits<Arg3Type>::Index>::value),
STORAGE_INDEX_MUST_MATCH)
eigen_assert(dimensions_match(m_arg1Impl.dimensions(), m_arg2Impl.dimensions()) && dimensions_match(m_arg1Impl.dimensions(), m_arg3Impl.dimensions()));
}
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef typename TensorEvaluator<Arg1Type, Device>::Dimensions Dimensions;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockNotImplemented TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
{
// TODO: use arg2 or arg3 dimensions if they are known at compile time.
return m_arg1Impl.dimensions();
}
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
m_arg1Impl.evalSubExprsIfNeeded(NULL);
m_arg2Impl.evalSubExprsIfNeeded(NULL);
m_arg3Impl.evalSubExprsIfNeeded(NULL);
return true;
}
EIGEN_STRONG_INLINE void cleanup() {
m_arg1Impl.cleanup();
m_arg2Impl.cleanup();
m_arg3Impl.cleanup();
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_functor(m_arg1Impl.coeff(index), m_arg2Impl.coeff(index), m_arg3Impl.coeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
return m_functor.packetOp(m_arg1Impl.template packet<LoadMode>(index),
m_arg2Impl.template packet<LoadMode>(index),
m_arg3Impl.template packet<LoadMode>(index));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
const double functor_cost = internal::functor_traits<TernaryOp>::Cost;
return m_arg1Impl.costPerCoeff(vectorized) +
m_arg2Impl.costPerCoeff(vectorized) +
m_arg3Impl.costPerCoeff(vectorized) +
TensorOpCost(0, 0, functor_cost, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return NULL; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
m_arg1Impl.bind(cgh);
m_arg2Impl.bind(cgh);
m_arg3Impl.bind(cgh);
}
#endif
private:
const TernaryOp m_functor;
TensorEvaluator<Arg1Type, Device> m_arg1Impl;
TensorEvaluator<Arg2Type, Device> m_arg2Impl;
TensorEvaluator<Arg3Type, Device> m_arg3Impl;
};
// -------------------- SelectOp --------------------
template<typename IfArgType, typename ThenArgType, typename ElseArgType, typename Device>
struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>, Device>
{
typedef TensorSelectOp<IfArgType, ThenArgType, ElseArgType> XprType;
typedef typename XprType::Scalar Scalar;
static constexpr int Layout = TensorEvaluator<IfArgType, Device>::Layout;
enum {
IsAligned = TensorEvaluator<ThenArgType, Device>::IsAligned &
TensorEvaluator<ElseArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<ThenArgType, Device>::PacketAccess &
TensorEvaluator<ElseArgType, Device>::PacketAccess &
PacketType<Scalar, Device>::HasBlend,
BlockAccess = TensorEvaluator<IfArgType, Device>::BlockAccess &&
TensorEvaluator<ThenArgType, Device>::BlockAccess &&
TensorEvaluator<ElseArgType, Device>::BlockAccess,
PreferBlockAccess = TensorEvaluator<IfArgType, Device>::PreferBlockAccess ||
TensorEvaluator<ThenArgType, Device>::PreferBlockAccess ||
TensorEvaluator<ElseArgType, Device>::PreferBlockAccess,
CoordAccess = false, // to be implemented
RawAccess = false
};
TensorEvaluator(const XprType& op, const Device& device)
: m_condImpl(op.ifExpression(), device),
m_thenImpl(op.thenExpression(), device),
m_elseImpl(op.elseExpression(), device)
{
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ThenArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<IfArgType, Device>::Layout) == static_cast<int>(TensorEvaluator<ElseArgType, Device>::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE);
eigen_assert(dimensions_match(m_condImpl.dimensions(), m_thenImpl.dimensions()));
eigen_assert(dimensions_match(m_thenImpl.dimensions(), m_elseImpl.dimensions()));
}
typedef typename XprType::Index Index;
typedef typename internal::traits<XprType>::Scalar CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size;
typedef typename TensorEvaluator<IfArgType, Device>::Dimensions Dimensions;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
static constexpr int NumDims = internal::array_size<Dimensions>::value;
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
typedef typename TensorEvaluator<const IfArgType, Device>::TensorBlock
IfArgTensorBlock;
typedef typename TensorEvaluator<const ThenArgType, Device>::TensorBlock
ThenArgTensorBlock;
typedef typename TensorEvaluator<const ElseArgType, Device>::TensorBlock
ElseArgTensorBlock;
struct TensorSelectOpBlockFactory {
template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType>
struct XprType {
typedef TensorSelectOp<const IfArgXprType, const ThenArgXprType, const ElseArgXprType> type;
};
template <typename IfArgXprType, typename ThenArgXprType, typename ElseArgXprType>
typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type expr(
const IfArgXprType& if_expr, const ThenArgXprType& then_expr, const ElseArgXprType& else_expr) const {
return typename XprType<IfArgXprType, ThenArgXprType, ElseArgXprType>::type(if_expr, then_expr, else_expr);
}
};
typedef internal::TensorTernaryExprBlock<TensorSelectOpBlockFactory,
IfArgTensorBlock, ThenArgTensorBlock,
ElseArgTensorBlock>
TensorBlock;
//===--------------------------------------------------------------------===//
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
{
// TODO: use then or else impl instead if they happen to be known at compile time.
return m_condImpl.dimensions();
}
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
m_condImpl.evalSubExprsIfNeeded(NULL);
m_thenImpl.evalSubExprsIfNeeded(NULL);
m_elseImpl.evalSubExprsIfNeeded(NULL);
return true;
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType, EvalSubExprsCallback done) {
m_condImpl.evalSubExprsIfNeeded(nullptr, [this, done](bool) {
m_thenImpl.evalSubExprsIfNeeded(nullptr, [this, done](bool) {
m_elseImpl.evalSubExprsIfNeeded(nullptr, [done](bool) { done(true); });
});
});
}
#endif // EIGEN_USE_THREADS
EIGEN_STRONG_INLINE void cleanup() {
m_condImpl.cleanup();
m_thenImpl.cleanup();
m_elseImpl.cleanup();
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_condImpl.coeff(index) ? m_thenImpl.coeff(index) : m_elseImpl.coeff(index);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
{
internal::Selector<PacketSize> select;
EIGEN_UNROLL_LOOP
for (Index i = 0; i < PacketSize; ++i) {
select.select[i] = m_condImpl.coeff(index+i);
}
return internal::pblend(select,
m_thenImpl.template packet<LoadMode>(index),
m_elseImpl.template packet<LoadMode>(index));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
return m_condImpl.costPerCoeff(vectorized) +
m_thenImpl.costPerCoeff(vectorized)
.cwiseMax(m_elseImpl.costPerCoeff(vectorized));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
auto then_req = m_thenImpl.getResourceRequirements();
auto else_req = m_elseImpl.getResourceRequirements();
auto merged_req =
internal::TensorBlockResourceRequirements::merge(then_req, else_req);
merged_req.cost_per_coeff =
then_req.cost_per_coeff.cwiseMax(else_req.cost_per_coeff);
return internal::TensorBlockResourceRequirements::merge(
m_condImpl.getResourceRequirements(), merged_req);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
block(TensorBlockDesc& desc, TensorBlockScratch& scratch,
bool /*root_of_expr_ast*/ = false) const {
// It's unsafe to pass destination buffer to underlying expressions, because
// output might be aliased with one of the inputs.
desc.DropDestinationBuffer();
return TensorBlock(
m_condImpl.block(desc, scratch), m_thenImpl.block(desc, scratch),
m_elseImpl.block(desc, scratch), TensorSelectOpBlockFactory());
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return NULL; }
#ifdef EIGEN_USE_SYCL
// binding placeholder accessors to a command group handler for SYCL
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler &cgh) const {
m_condImpl.bind(cgh);
m_thenImpl.bind(cgh);
m_elseImpl.bind(cgh);
}
#endif
private:
TensorEvaluator<IfArgType, Device> m_condImpl;
TensorEvaluator<ThenArgType, Device> m_thenImpl;
TensorEvaluator<ElseArgType, Device> m_elseImpl;
};
} // end namespace Eigen
#if defined(EIGEN_USE_SYCL) && defined(SYCL_COMPILER_IS_DPCPP)
template <typename Derived, typename Device>
struct cl::sycl::is_device_copyable<
Eigen::TensorEvaluator<Derived, Device>,
std::enable_if_t<!std::is_trivially_copyable<
Eigen::TensorEvaluator<Derived, Device>>::value>> : std::true_type {};
#endif
#endif // EIGEN_CXX11_TENSOR_TENSOR_EVALUATOR_H