unsupported/Eigen/CXX11/src/Tensor/TensorConcatenation.h

// 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_CONCATENATION_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H

namespace Eigen {

/** \class TensorConcatenationOp
  * \ingroup CXX11_Tensor_Module
  *
  * \brief Tensor concatenation class.
  *
  *
  */
namespace internal {
template<typename Axis, typename LhsXprType, typename RhsXprType>
struct traits<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >
{
  // Type promotion to handle the case where the types of the lhs and the rhs are different.
  typedef typename promote_storage_type<typename LhsXprType::Scalar,
                                        typename RhsXprType::Scalar>::ret Scalar;
  typedef typename packet_traits<Scalar>::type Packet;
  typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind,
                                        typename traits<RhsXprType>::StorageKind>::ret 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 Axis, typename LhsXprType, typename RhsXprType>
struct eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, Eigen::Dense>
{
  typedef const TensorConcatenationOp<Axis, LhsXprType, RhsXprType>& type;
};

template<typename Axis, typename LhsXprType, typename RhsXprType>
struct nested<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, 1, typename eval<TensorConcatenationOp<Axis, LhsXprType, RhsXprType> >::type>
{
  typedef TensorConcatenationOp<Axis, LhsXprType, RhsXprType> type;
};

}  // end namespace internal


template<typename Axis, typename LhsXprType, typename RhsXprType>
class TensorConcatenationOp : public TensorBase<TensorConcatenationOp<Axis, LhsXprType, RhsXprType>, WriteAccessors>
{
  public:
    typedef typename internal::traits<TensorConcatenationOp>::Scalar Scalar;
    typedef typename internal::traits<TensorConcatenationOp>::Packet Packet;
    typedef typename internal::traits<TensorConcatenationOp>::StorageKind StorageKind;
    typedef typename internal::traits<TensorConcatenationOp>::Index Index;
    typedef typename internal::nested<TensorConcatenationOp>::type Nested;
    typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType,
                                                    typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
    typedef typename internal::promote_storage_type<typename LhsXprType::PacketReturnType,
                                                    typename RhsXprType::PacketReturnType>::ret PacketReturnType;
    typedef typename NumTraits<Scalar>::Real RealScalar;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConcatenationOp(const LhsXprType& lhs, const RhsXprType& rhs, Axis axis)
        : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_axis(axis) {}

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename LhsXprType::Nested>::type&
    lhsExpression() const { return m_lhs_xpr; }

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename RhsXprType::Nested>::type&
    rhsExpression() const { return m_rhs_xpr; }

    EIGEN_DEVICE_FUNC Axis axis() const { return m_axis; }

  protected:
    typename LhsXprType::Nested m_lhs_xpr;
    typename RhsXprType::Nested m_rhs_xpr;
    const Axis m_axis;
};


// Eval as rvalue
template<typename Axis, typename LeftArgType, typename RightArgType, typename Device>
struct TensorEvaluator<const TensorConcatenationOp<Axis, LeftArgType, RightArgType>, Device>
{
  typedef TensorConcatenationOp<Axis, LeftArgType, RightArgType> XprType;
  typedef typename XprType::Index Index;
  static const int NumDims = internal::array_size<typename TensorEvaluator<LeftArgType, Device>::Dimensions>::value;
  static const int RightNumDims = internal::array_size<typename TensorEvaluator<RightArgType, Device>::Dimensions>::value;
  typedef DSizes<Index, NumDims> Dimensions;
  typedef typename XprType::Scalar Scalar;
  typedef typename XprType::CoeffReturnType CoeffReturnType;
  typedef typename XprType::PacketReturnType PacketReturnType;
  enum {
    IsAligned = false,
    PacketAccess = TensorEvaluator<LeftArgType, Device>::PacketAccess & TensorEvaluator<RightArgType, Device>::PacketAccess,
  };

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
    : m_leftImpl(op.lhsExpression(), device), m_rightImpl(op.rhsExpression(), device), m_axis(op.axis())
  {
    EIGEN_STATIC_ASSERT(NumDims == RightNumDims, YOU_MADE_A_PROGRAMMING_MISTAKE)
    eigen_assert(0 <= m_axis && m_axis < NumDims);
    const Dimensions& lhs_dims = m_leftImpl.dimensions();
    const Dimensions& rhs_dims = m_rightImpl.dimensions();
    int i = 0;
    for (; i < m_axis; ++i) {
      eigen_assert(lhs_dims[i] > 0);
      eigen_assert(lhs_dims[i] == rhs_dims[i]);
      m_dimensions[i] = lhs_dims[i];
    }
    eigen_assert(lhs_dims[i] > 0);  // Now i == m_axis.
    eigen_assert(rhs_dims[i] > 0);
    m_dimensions[i] = lhs_dims[i] + rhs_dims[i];
    for (++i; i < NumDims; ++i) {
      eigen_assert(lhs_dims[i] > 0);
      eigen_assert(lhs_dims[i] == rhs_dims[i]);
      m_dimensions[i] = lhs_dims[i];
    }

    m_leftStrides[0] = 1;
    m_rightStrides[0] = 1;
    m_outputStrides[0] = 1;
    for (int i = 1; i < NumDims; ++i) {
      m_leftStrides[i] = m_leftStrides[i-1] * lhs_dims[i-1];
      m_rightStrides[i] = m_rightStrides[i-1] * rhs_dims[i-1];
      m_outputStrides[i] = m_outputStrides[i-1] * m_dimensions[i-1];
    }
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

  // TODO(phli): Add short-circuit memcpy evaluation if underlying data are linear?
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data)
  {
    m_leftImpl.evalSubExprsIfNeeded(NULL);
    m_rightImpl.evalSubExprsIfNeeded(NULL);
    return true;
  }

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup()
  {
    m_leftImpl.cleanup();
    m_rightImpl.cleanup();
  }

  // TODO(phli): attempt to speed this up. The integer divisions and modulo are slow.
  // See CL/76180724 comments for more ideas.
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
  {
    // Collect dimension-wise indices (subs).
    array<Index, NumDims> subs;
    for (int i = NumDims - 1; i > 0; --i) {
      subs[i] = index / m_outputStrides[i];
      index -= subs[i] * m_outputStrides[i];
    }
    subs[0] = index;

    const Dimensions& left_dims = m_leftImpl.dimensions();
    if (subs[m_axis] < left_dims[m_axis]) {
      Index left_index = subs[0];
      for (int i = 1; i < NumDims; ++i) {
        left_index += (subs[i] % left_dims[i]) * m_leftStrides[i];
      }
      return m_leftImpl.coeff(left_index);
    } else {
      subs[m_axis] -= left_dims[m_axis];
      const Dimensions& right_dims = m_rightImpl.dimensions();
      Index right_index = subs[0];
      for (int i = 1; i < NumDims; ++i) {
        right_index += (subs[i] % right_dims[i]) * m_rightStrides[i];
      }
      return m_rightImpl.coeff(right_index);
    }
  }

  // TODO(phli): Add a real vectorization.
  template<int LoadMode>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
  {
    static const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
    EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
    eigen_assert(index + packetSize - 1 < dimensions().TotalSize());

    EIGEN_ALIGN_DEFAULT CoeffReturnType values[packetSize];
    for (int i = 0; i < packetSize; ++i) {
      values[i] = coeff(index+i);
    }
    PacketReturnType rslt = internal::pload<PacketReturnType>(values);
    return rslt;
  }

  Scalar* data() const { return NULL; }

  protected:
    const Axis m_axis;
    Dimensions m_dimensions;
    array<Index, NumDims> m_outputStrides;
    array<Index, NumDims> m_leftStrides;
    array<Index, NumDims> m_rightStrides;
    TensorEvaluator<LeftArgType, Device> m_leftImpl;
    TensorEvaluator<RightArgType, Device> m_rightImpl;
};


} // end namespace Eigen

#endif // EIGEN_CXX11_TENSOR_TENSOR_CONCATENATION_H