eigen/unsupported/Eigen/CXX11/src/Tensor/TensorConversion.h

// 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 packet_traits<Scalar>::type Packet;
  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 {
  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> {
  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, 1, 2> {
  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;
      EIGEN_ALIGN_DEFAULT typename internal::unpacket_traits<TgtPacket>::type values[TgtPacketSize];
      for (int i = 0; i < TgtPacketSize; ++i) {
        values[i] = 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>::Packet Packet;
    typedef typename internal::traits<TensorConversionOp>::StorageKind StorageKind;
    typedef typename internal::traits<TensorConversionOp>::Index Index;
    typedef typename internal::nested<TensorConversionOp>::type Nested;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename XprType::PacketReturnType PacketReturnType;
    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;
};




// 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 internal::traits<XprType>::Packet PacketReturnType;
  typedef typename internal::packet_traits<SrcType>::type PacketSourceType;

  enum {
    IsAligned = false,
    PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess && internal::type_casting_traits<SrcType, TargetType>::VectorizedCast,
    Layout = TensorEvaluator<ArgType, Device>::Layout,
  };

  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*/)
  {
    m_impl.evalSubExprsIfNeeded(NULL);
    return true;
  }

  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 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(m_impl);
    return converter.template packet<LoadMode>(index);
  }

  EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }

  protected:
    TensorEvaluator<ArgType, Device> m_impl;
};

} // end namespace Eigen

#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVERSION_H