182 lines
7.5 KiB
C++
182 lines
7.5 KiB
C++
// 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 the mozilla.org home page
|
|
|
|
#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;
|
|
|
|
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;
|
|
|
|
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;
|
|
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
|
|
|
|
enum {
|
|
IsAligned = TensorEvaluator<LeftArgType, Device>::IsAligned & TensorEvaluator<RightArgType, Device>::IsAligned,
|
|
PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess,
|
|
Layout = TensorEvaluator<LeftArgType, Device>::Layout,
|
|
RawAccess = TensorEvaluator<LeftArgType, Device>::RawAccess
|
|
};
|
|
|
|
EIGEN_DEVICE_FUNC 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_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar*) {
|
|
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());
|
|
}
|
|
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);
|
|
}
|
|
|
|
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);
|
|
}
|
|
|
|
/// required by sycl in order to extract the accessor
|
|
const TensorEvaluator<LeftArgType, Device>& left_impl() const { return m_leftImpl; }
|
|
/// required by sycl in order to extract the accessor
|
|
const TensorEvaluator<RightArgType, Device>& right_impl() const { return m_rightImpl; }
|
|
|
|
EIGEN_DEVICE_FUNC CoeffReturnType* data() const { return m_leftImpl.data(); }
|
|
|
|
private:
|
|
TensorEvaluator<LeftArgType, Device> m_leftImpl;
|
|
TensorEvaluator<RightArgType, Device> m_rightImpl;
|
|
};
|
|
|
|
}
|
|
|
|
|
|
#endif // EIGEN_CXX11_TENSOR_TENSOR_ASSIGN_H
|