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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 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_CONVERSION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
namespace Eigen {
/** \class TensorConversionOp
* \ingroup CXX11_Tensor_Module
*
* \brief Tensor conversion class. This class makes it possible to vectorize
* type casting operations when the number of scalars per packet in the source
* and the destination type differ
*/
namespace internal {
template<typename TargetType, typename XprType>
struct traits<TensorConversionOp<TargetType, XprType> >
{
// Type promotion to handle the case where the types of the lhs and the rhs are different.
typedef TargetType Scalar;
typedef typename traits<XprType>::StorageKind StorageKind;
typedef typename traits<XprType>::Index Index;
typedef typename XprType::Nested Nested;
typedef typename remove_reference<Nested>::type _Nested;
static const int NumDimensions = traits<XprType>::NumDimensions;
static const int Layout = traits<XprType>::Layout;
enum { Flags = 0 };
};
template<typename TargetType, typename XprType>
struct eval<TensorConversionOp<TargetType, XprType>, Eigen::Dense>
{
typedef const TensorConversionOp<TargetType, XprType>& type;
};
template<typename TargetType, typename XprType>
struct nested<TensorConversionOp<TargetType, XprType>, 1, typename eval<TensorConversionOp<TargetType, XprType> >::type>
{
typedef TensorConversionOp<TargetType, XprType> type;
};
} // end namespace internal
template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket, int SrcCoeffRatio, int TgtCoeffRatio>
struct PacketConverter {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketConverter(const TensorEvaluator& impl)
: m_impl(impl) {}
template<int LoadMode, typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<LoadMode>(index));
}
private:
const TensorEvaluator& m_impl;
};
template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 2, 1> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketConverter(const TensorEvaluator& impl)
: m_impl(impl) {}
template<int LoadMode, typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
SrcPacket src1 = m_impl.template packet<LoadMode>(index);
SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2);
return result;
}
private:
const TensorEvaluator& m_impl;
};
template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 4, 1> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketConverter(const TensorEvaluator& impl)
: m_impl(impl) {}
template<int LoadMode, typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
SrcPacket src1 = m_impl.template packet<LoadMode>(index);
SrcPacket src2 = m_impl.template packet<LoadMode>(index + SrcPacketSize);
SrcPacket src3 = m_impl.template packet<LoadMode>(index + 2 * SrcPacketSize);
SrcPacket src4 = m_impl.template packet<LoadMode>(index + 3 * SrcPacketSize);
TgtPacket result = internal::pcast<SrcPacket, TgtPacket>(src1, src2, src3, src4);
return result;
}
private:
const TensorEvaluator& m_impl;
};
template <typename TensorEvaluator, typename SrcPacket, typename TgtPacket>
struct PacketConverter<TensorEvaluator, SrcPacket, TgtPacket, 1, 2> {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
PacketConverter(const TensorEvaluator& impl)
: m_impl(impl), m_maxIndex(impl.dimensions().TotalSize()) {}
template<int LoadMode, typename Index>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket packet(Index index) const {
const int SrcPacketSize = internal::unpacket_traits<SrcPacket>::size;
// Only call m_impl.packet() when we have direct access to the underlying data. This
// ensures that we don't compute the subexpression twice. We may however load some
// coefficients twice, but in practice this doesn't negatively impact performance.
if (m_impl.data() && (index + SrcPacketSize < m_maxIndex)) {
// Force unaligned memory loads since we can't ensure alignment anymore
return internal::pcast<SrcPacket, TgtPacket>(m_impl.template packet<Unaligned>(index));
} else {
const int TgtPacketSize = internal::unpacket_traits<TgtPacket>::size;
typedef typename internal::unpacket_traits<SrcPacket>::type SrcType;
typedef typename internal::unpacket_traits<TgtPacket>::type TgtType;
internal::scalar_cast_op<SrcType, TgtType> converter;
EIGEN_ALIGN_MAX typename internal::unpacket_traits<TgtPacket>::type values[TgtPacketSize];
for (int i = 0; i < TgtPacketSize; ++i) {
values[i] = converter(m_impl.coeff(index+i));
}
TgtPacket rslt = internal::pload<TgtPacket>(values);
return rslt;
}
}
private:
const TensorEvaluator& m_impl;
const typename TensorEvaluator::Index m_maxIndex;
};
template<typename TargetType, typename XprType>
class TensorConversionOp : public TensorBase<TensorConversionOp<TargetType, XprType>, ReadOnlyAccessors>
{
public:
typedef typename internal::traits<TensorConversionOp>::Scalar Scalar;
typedef typename internal::traits<TensorConversionOp>::StorageKind StorageKind;
typedef typename internal::traits<TensorConversionOp>::Index Index;
typedef typename internal::nested<TensorConversionOp>::type Nested;
typedef Scalar CoeffReturnType;
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConversionOp(const XprType& xpr)
: m_xpr(xpr) {}
EIGEN_DEVICE_FUNC
const typename internal::remove_all<typename XprType::Nested>::type&
expression() const { return m_xpr; }
protected:
typename XprType::Nested m_xpr;
};
template <bool SameType, typename Eval, typename Scalar> struct ConversionSubExprEval {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Eval& impl, Scalar*) {
impl.evalSubExprsIfNeeded(NULL);
return true;
}
};
template <typename Eval, typename Scalar> struct ConversionSubExprEval<true, Eval, Scalar> {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(Eval& impl, Scalar* data) {
return impl.evalSubExprsIfNeeded(data);
}
};
// Eval as rvalue
template<typename TargetType, typename ArgType, typename Device>
struct TensorEvaluator<const TensorConversionOp<TargetType, ArgType>, Device>
{
typedef TensorConversionOp<TargetType, ArgType> XprType;
typedef typename XprType::Index Index;
typedef typename TensorEvaluator<ArgType, Device>::Dimensions Dimensions;
typedef TargetType Scalar;
typedef TargetType CoeffReturnType;
typedef typename internal::remove_all<typename internal::traits<ArgType>::Scalar>::type SrcType;
typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
typedef typename PacketType<SrcType, Device>::type PacketSourceType;
static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
enum {
IsAligned = false,
PacketAccess = true,
Layout = TensorEvaluator<ArgType, Device>::Layout,
RawAccess = false
};
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
: m_impl(op.expression(), device)
{
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_impl.dimensions(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data)
{
return ConversionSubExprEval<internal::is_same<TargetType, SrcType>::value, TensorEvaluator<ArgType, Device>, Scalar>::run(m_impl, data);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
{
m_impl.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
{
internal::scalar_cast_op<SrcType, TargetType> converter;
return converter(m_impl.coeff(index));
}
template<int LoadMode>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
{
const bool Vectorizable = TensorEvaluator<ArgType, Device>::PacketAccess &
internal::type_casting_traits<SrcType, TargetType>::VectorizedCast;
return PacketConv<LoadMode, Vectorizable>::run(m_impl, index);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
costPerCoeff(bool vectorized) const {
const double cast_cost = TensorOpCost::CastCost<SrcType, TargetType>();
if (vectorized) {
const double SrcCoeffRatio =
internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
const double TgtCoeffRatio =
internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
return m_impl.costPerCoeff(vectorized) * (SrcCoeffRatio / PacketSize) +
TensorOpCost(0, 0, TgtCoeffRatio * (cast_cost / PacketSize));
} else {
return m_impl.costPerCoeff(vectorized) + TensorOpCost(0, 0, cast_cost);
}
}
EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
protected:
template <int LoadMode, bool ActuallyVectorize>
struct PacketConv {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
internal::scalar_cast_op<SrcType, TargetType> converter;
EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
for (int i = 0; i < PacketSize; ++i) {
values[i] = converter(impl.coeff(index+i));
}
PacketReturnType rslt = internal::pload<PacketReturnType>(values);
return rslt;
}
};
template <int LoadMode>
struct PacketConv<LoadMode, true> {
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType run(const TensorEvaluator<ArgType, Device>& impl, Index index) {
const int SrcCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::SrcCoeffRatio;
const int TgtCoeffRatio = internal::type_casting_traits<SrcType, TargetType>::TgtCoeffRatio;
PacketConverter<TensorEvaluator<ArgType, Device>, PacketSourceType, PacketReturnType,
SrcCoeffRatio, TgtCoeffRatio> converter(impl);
return converter.template packet<LoadMode>(index);
}
};
TensorEvaluator<ArgType, Device> m_impl;
};
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H
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