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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_ASSIGN_H
#define EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
namespace Eigen {
/** \class TensorAssign
* \ingroup CXX11_Tensor_Module
*
* \brief The tensor assignment class.
*
* This class is represents the assignment of the values resulting from the evaluation of
* the rhs expression to the memory locations denoted by the lhs expression.
*/
namespace internal {
template<typename LhsXprType, typename RhsXprType>
struct traits<TensorAssignOp<LhsXprType, RhsXprType> >
{
typedef typename LhsXprType::Scalar Scalar;
typedef typename traits<LhsXprType>::StorageKind StorageKind;
typedef typename promote_index_type<typename traits<LhsXprType>::Index,
typename traits<RhsXprType>::Index>::type Index;
typedef typename LhsXprType::Nested LhsNested;
typedef typename RhsXprType::Nested RhsNested;
typedef typename remove_reference<LhsNested>::type _LhsNested;
typedef typename remove_reference<RhsNested>::type _RhsNested;
static const std::size_t NumDimensions = internal::traits<LhsXprType>::NumDimensions;
static const int Layout = internal::traits<LhsXprType>::Layout;
typedef typename traits<LhsXprType>::PointerType PointerType;
enum {
Flags = 0
};
};
template<typename LhsXprType, typename RhsXprType>
struct eval<TensorAssignOp<LhsXprType, RhsXprType>, Eigen::Dense>
{
typedef const TensorAssignOp<LhsXprType, RhsXprType>& type;
};
template<typename LhsXprType, typename RhsXprType>
struct nested<TensorAssignOp<LhsXprType, RhsXprType>, 1, typename eval<TensorAssignOp<LhsXprType, RhsXprType> >::type>
{
typedef TensorAssignOp<LhsXprType, RhsXprType> type;
};
} // end namespace internal
template<typename LhsXprType, typename RhsXprType>
class TensorAssignOp : public TensorBase<TensorAssignOp<LhsXprType, RhsXprType> >
{
public:
typedef typename Eigen::internal::traits<TensorAssignOp>::Scalar Scalar;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename LhsXprType::CoeffReturnType CoeffReturnType;
typedef typename Eigen::internal::nested<TensorAssignOp>::type Nested;
typedef typename Eigen::internal::traits<TensorAssignOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorAssignOp>::Index Index;
static const int NumDims = Eigen::internal::traits<TensorAssignOp>::NumDimensions;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorAssignOp(LhsXprType& lhs, const RhsXprType& rhs)
: m_lhs_xpr(lhs), m_rhs_xpr(rhs) {}
/** \returns the nested expressions */
EIGEN_DEVICE_FUNC
typename internal::remove_all<typename LhsXprType::Nested>::type&
lhsExpression() const { return *((typename internal::remove_all<typename LhsXprType::Nested>::type*)&m_lhs_xpr); }
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename RhsXprType::Nested>::type&
rhsExpression() const { return m_rhs_xpr; }
protected:
typename internal::remove_all<typename LhsXprType::Nested>::type& m_lhs_xpr;
const typename internal::remove_all<typename RhsXprType::Nested>::type& m_rhs_xpr;
};
template<typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorAssignOp<LeftArgType, RightArgType>, Device>
{
typedef TensorAssignOp<LeftArgType, RightArgType> XprType;
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
typedef typename TensorEvaluator<RightArgType, Device>::Dimensions Dimensions;
typedef StorageMemory<CoeffReturnType, Device> Storage;
typedef typename Storage::Type EvaluatorPointerType;
static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
static const int NumDims = XprType::NumDims;
enum {
IsAligned = int(TensorEvaluator<LeftArgType, Device>::IsAligned) &
int(TensorEvaluator<RightArgType, Device>::IsAligned),
PacketAccess = int(TensorEvaluator<LeftArgType, Device>::PacketAccess) &
int(TensorEvaluator<RightArgType, Device>::PacketAccess),
BlockAccess = int(TensorEvaluator<LeftArgType, Device>::BlockAccess) &
int(TensorEvaluator<RightArgType, Device>::BlockAccess),
PreferBlockAccess = int(TensorEvaluator<LeftArgType, Device>::PreferBlockAccess) |
int(TensorEvaluator<RightArgType, Device>::PreferBlockAccess),
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
RawAccess = TensorEvaluator<LeftArgType, Device>::RawAccess
};
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
typedef typename TensorEvaluator<const RightArgType, Device>::TensorBlock
RightTensorBlock;
//===--------------------------------------------------------------------===//
TensorEvaluator(const XprType& op, const Device& device) :
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)),
YOU_MADE_A_PROGRAMMING_MISTAKE);
}
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
{
// The dimensions of the lhs and the rhs tensors should be equal to prevent
// overflows and ensure the result is fully initialized.
// TODO: use left impl instead if right impl dimensions are known at compile time.
return m_rightImpl.dimensions();
}
EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType) {
eigen_assert(dimensions_match(m_leftImpl.dimensions(), m_rightImpl.dimensions()));
m_leftImpl.evalSubExprsIfNeeded(NULL);
// If the lhs provides raw access to its storage area (i.e. if m_leftImpl.data() returns a non
// null value), attempt to evaluate the rhs expression in place. Returns true iff in place
// evaluation isn't supported and the caller still needs to manually assign the values generated
// by the rhs to the lhs.
return m_rightImpl.evalSubExprsIfNeeded(m_leftImpl.data());
}
#ifdef EIGEN_USE_THREADS
template <typename EvalSubExprsCallback>
EIGEN_STRONG_INLINE void evalSubExprsIfNeededAsync(
EvaluatorPointerType, EvalSubExprsCallback done) {
m_leftImpl.evalSubExprsIfNeededAsync(nullptr, [this, done](bool) {
m_rightImpl.evalSubExprsIfNeededAsync(
m_leftImpl.data(), [done](bool need_assign) { done(need_assign); });
});
}
#endif // EIGEN_USE_THREADS
EIGEN_STRONG_INLINE void cleanup() {
m_leftImpl.cleanup();
m_rightImpl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalScalar(Index i) {
m_leftImpl.coeffRef(i) = m_rightImpl.coeff(i);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalPacket(Index i) {
const int LhsStoreMode = TensorEvaluator<LeftArgType, Device>::IsAligned ? Aligned : Unaligned;
const int RhsLoadMode = TensorEvaluator<RightArgType, Device>::IsAligned ? Aligned : Unaligned;
m_leftImpl.template writePacket<LhsStoreMode>(i, m_rightImpl.template packet<RhsLoadMode>(i));
}
EIGEN_DEVICE_FUNC CoeffReturnType coeff(Index index) const
{
return m_leftImpl.coeff(index);
}
template<int LoadMode>
EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
{
return m_leftImpl.template packet<LoadMode>(index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
// We assume that evalPacket or evalScalar is called to perform the
// assignment and account for the cost of the write here, but reduce left
// cost by one load because we are using m_leftImpl.coeffRef.
TensorOpCost left = m_leftImpl.costPerCoeff(vectorized);
return m_rightImpl.costPerCoeff(vectorized) +
TensorOpCost(
numext::maxi(0.0, left.bytes_loaded() - sizeof(CoeffReturnType)),
left.bytes_stored(), left.compute_cycles()) +
TensorOpCost(0, sizeof(CoeffReturnType), 0, vectorized, PacketSize);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
return internal::TensorBlockResourceRequirements::merge(
m_leftImpl.getResourceRequirements(),
m_rightImpl.getResourceRequirements());
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlock(
TensorBlockDesc& desc, TensorBlockScratch& scratch) {
if (TensorEvaluator<LeftArgType, Device>::RawAccess &&
m_leftImpl.data() != NULL) {
// If destination has raw data access, we pass it as a potential
// destination for a block descriptor evaluation.
desc.template AddDestinationBuffer<Layout>(
/*dst_base=*/m_leftImpl.data() + desc.offset(),
/*dst_strides=*/internal::strides<Layout>(m_leftImpl.dimensions()));
}
RightTensorBlock block = m_rightImpl.block(desc, scratch, /*root_of_expr_ast=*/true);
// If block was evaluated into a destination, there is no need to do assignment.
if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
m_leftImpl.writeBlock(desc, block);
}
block.cleanup();
}
#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
EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_leftImpl.data(); }
private:
TensorEvaluator<LeftArgType, Device> m_leftImpl;
TensorEvaluator<RightArgType, Device> m_rightImpl;
};
}
#endif // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
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