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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 internal::packet_traits<Scalar>::type Packet;
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;
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::internal::traits<TensorAssignOp>::Packet Packet;
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
typedef typename LhsXprType::CoeffReturnType CoeffReturnType;
typedef typename LhsXprType::PacketReturnType PacketReturnType;
typedef typename Eigen::internal::nested<TensorAssignOp>::type Nested;
typedef typename Eigen::internal::traits<TensorAssignOp>::StorageKind StorageKind;
typedef typename Eigen::internal::traits<TensorAssignOp>::Index Index;
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;
enum {
IsAligned = TensorEvaluator<LeftArgType, Device>::IsAligned & TensorEvaluator<RightArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess,
};
EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) :
m_leftImpl(op.lhsExpression(), device),
m_rightImpl(op.rhsExpression(), device)
{ }
typedef typename XprType::Index Index;
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
typedef typename XprType::PacketReturnType PacketReturnType;
typedef typename TensorEvaluator<RightArgType, Device>::Dimensions Dimensions;
EIGEN_DEVICE_FUNC const Dimensions& dimensions() const
{
// TODO: use left impl instead if right impl dimensions are known at compile time.
return m_rightImpl.dimensions();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
eigen_assert(internal::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());
}
EIGEN_DEVICE_FUNC 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);
}
private:
TensorEvaluator<LeftArgType, Device> m_leftImpl;
TensorEvaluator<RightArgType, Device> m_rightImpl;
};
}
#endif // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
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