// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// 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_SELFADJOINT_MATRIX_VECTOR_H
#define EIGEN_SELFADJOINT_MATRIX_VECTOR_H

// IWYU pragma: private
#include "../InternalHeaderCheck.h"

namespace Eigen {

namespace internal {

/* Optimized selfadjoint matrix * vector product:
 * This algorithm processes 2 columns at once that allows to both reduce
 * the number of load/stores of the result by a factor 2 and to reduce
 * the instruction dependency.
 */

template <typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs,
          int Version = Specialized>
struct selfadjoint_matrix_vector_product;

template <typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs,
          int Version>
struct selfadjoint_matrix_vector_product

{
  static EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC void run(Index size, const Scalar* lhs, Index lhsStride, const Scalar* rhs,
                                                      Scalar* res, Scalar alpha);
};

template <typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs,
          int Version>
EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC void
selfadjoint_matrix_vector_product<Scalar, Index, StorageOrder, UpLo, ConjugateLhs, ConjugateRhs, Version>::run(
    Index size, const Scalar* lhs, Index lhsStride, const Scalar* rhs, Scalar* res, Scalar alpha) {
  typedef typename packet_traits<Scalar>::type Packet;
  typedef typename NumTraits<Scalar>::Real RealScalar;
  const Index PacketSize = sizeof(Packet) / sizeof(Scalar);

  enum {
    IsRowMajor = StorageOrder == RowMajor ? 1 : 0,
    IsLower = UpLo == Lower ? 1 : 0,
    FirstTriangular = IsRowMajor == IsLower
  };

  conj_helper<Scalar, Scalar, NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, IsRowMajor), ConjugateRhs> cj0;
  conj_helper<Scalar, Scalar, NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1;
  conj_helper<RealScalar, Scalar, false, ConjugateRhs> cjd;

  conj_helper<Packet, Packet, NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, IsRowMajor), ConjugateRhs> pcj0;
  conj_helper<Packet, Packet, NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, !IsRowMajor), ConjugateRhs>
      pcj1;

  Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha;

  Index bound = numext::maxi(Index(0), size - 8) & 0xfffffffe;
  if (FirstTriangular) bound = size - bound;

  for (Index j = FirstTriangular ? bound : 0; j < (FirstTriangular ? size : bound); j += 2) {
    const Scalar* EIGEN_RESTRICT A0 = lhs + j * lhsStride;
    const Scalar* EIGEN_RESTRICT A1 = lhs + (j + 1) * lhsStride;

    Scalar t0 = cjAlpha * rhs[j];
    Packet ptmp0 = pset1<Packet>(t0);
    Scalar t1 = cjAlpha * rhs[j + 1];
    Packet ptmp1 = pset1<Packet>(t1);

    Scalar t2(0);
    Packet ptmp2 = pset1<Packet>(t2);
    Scalar t3(0);
    Packet ptmp3 = pset1<Packet>(t3);

    Index starti = FirstTriangular ? 0 : j + 2;
    Index endi = FirstTriangular ? j : size;
    Index alignedStart = (starti) + internal::first_default_aligned(&res[starti], endi - starti);
    Index alignedEnd = alignedStart + ((endi - alignedStart) / (PacketSize)) * (PacketSize);

    res[j] += cjd.pmul(numext::real(A0[j]), t0);
    res[j + 1] += cjd.pmul(numext::real(A1[j + 1]), t1);
    if (FirstTriangular) {
      res[j] += cj0.pmul(A1[j], t1);
      t3 += cj1.pmul(A1[j], rhs[j]);
    } else {
      res[j + 1] += cj0.pmul(A0[j + 1], t0);
      t2 += cj1.pmul(A0[j + 1], rhs[j + 1]);
    }

    for (Index i = starti; i < alignedStart; ++i) {
      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i], t1);
      t2 += cj1.pmul(A0[i], rhs[i]);
      t3 += cj1.pmul(A1[i], rhs[i]);
    }
    // Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up)
    // gcc 4.2 does this optimization automatically.
    const Scalar* EIGEN_RESTRICT a0It = A0 + alignedStart;
    const Scalar* EIGEN_RESTRICT a1It = A1 + alignedStart;
    const Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart;
    Scalar* EIGEN_RESTRICT resIt = res + alignedStart;
    for (Index i = alignedStart; i < alignedEnd; i += PacketSize) {
      Packet A0i = ploadu<Packet>(a0It);
      a0It += PacketSize;
      Packet A1i = ploadu<Packet>(a1It);
      a1It += PacketSize;
      Packet Bi = ploadu<Packet>(rhsIt);
      rhsIt += PacketSize;  // FIXME should be aligned in most cases
      Packet Xi = pload<Packet>(resIt);

      Xi = pcj0.pmadd(A0i, ptmp0, pcj0.pmadd(A1i, ptmp1, Xi));
      ptmp2 = pcj1.pmadd(A0i, Bi, ptmp2);
      ptmp3 = pcj1.pmadd(A1i, Bi, ptmp3);
      pstore(resIt, Xi);
      resIt += PacketSize;
    }
    for (Index i = alignedEnd; i < endi; i++) {
      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i], t1);
      t2 += cj1.pmul(A0[i], rhs[i]);
      t3 += cj1.pmul(A1[i], rhs[i]);
    }

    res[j] += alpha * (t2 + predux(ptmp2));
    res[j + 1] += alpha * (t3 + predux(ptmp3));
  }
  for (Index j = FirstTriangular ? 0 : bound; j < (FirstTriangular ? bound : size); j++) {
    const Scalar* EIGEN_RESTRICT A0 = lhs + j * lhsStride;

    Scalar t1 = cjAlpha * rhs[j];
    Scalar t2(0);
    res[j] += cjd.pmul(numext::real(A0[j]), t1);
    for (Index i = FirstTriangular ? 0 : j + 1; i < (FirstTriangular ? j : size); i++) {
      res[i] += cj0.pmul(A0[i], t1);
      t2 += cj1.pmul(A0[i], rhs[i]);
    }
    res[j] += alpha * t2;
  }
}

}  // end namespace internal

/***************************************************************************
 * Wrapper to product_selfadjoint_vector
 ***************************************************************************/

namespace internal {

template <typename Lhs, int LhsMode, typename Rhs>
struct selfadjoint_product_impl<Lhs, LhsMode, false, Rhs, 0, true> {
  typedef typename Product<Lhs, Rhs>::Scalar Scalar;

  typedef internal::blas_traits<Lhs> LhsBlasTraits;
  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
  typedef internal::remove_all_t<ActualLhsType> ActualLhsTypeCleaned;

  typedef internal::blas_traits<Rhs> RhsBlasTraits;
  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
  typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;

  enum { LhsUpLo = LhsMode & (Upper | Lower) };

  template <typename Dest>
  static EIGEN_DEVICE_FUNC void run(Dest& dest, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha) {
    typedef typename Dest::Scalar ResScalar;
    typedef typename Rhs::Scalar RhsScalar;
    typedef Map<Matrix<ResScalar, Dynamic, 1>, plain_enum_min(AlignedMax, internal::packet_traits<ResScalar>::size)>
        MappedDest;

    eigen_assert(dest.rows() == a_lhs.rows() && dest.cols() == a_rhs.cols());

    if (a_lhs.rows() == 1) {
      dest = (alpha * a_lhs.coeff(0, 0)) * a_rhs;
      return;
    }

    add_const_on_value_type_t<ActualLhsType> lhs = LhsBlasTraits::extract(a_lhs);
    add_const_on_value_type_t<ActualRhsType> rhs = RhsBlasTraits::extract(a_rhs);

    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs) * RhsBlasTraits::extractScalarFactor(a_rhs);

    enum {
      EvalToDest = (Dest::InnerStrideAtCompileTime == 1),
      UseRhs = (ActualRhsTypeCleaned::InnerStrideAtCompileTime == 1)
    };

    internal::gemv_static_vector_if<ResScalar, Dest::SizeAtCompileTime, Dest::MaxSizeAtCompileTime, !EvalToDest>
        static_dest;
    internal::gemv_static_vector_if<RhsScalar, ActualRhsTypeCleaned::SizeAtCompileTime,
                                    ActualRhsTypeCleaned::MaxSizeAtCompileTime, !UseRhs>
        static_rhs;

    ei_declare_aligned_stack_constructed_variable(ResScalar, actualDestPtr, dest.size(),
                                                  EvalToDest ? dest.data() : static_dest.data());

    ei_declare_aligned_stack_constructed_variable(RhsScalar, actualRhsPtr, rhs.size(),
                                                  UseRhs ? const_cast<RhsScalar*>(rhs.data()) : static_rhs.data());

    if (!EvalToDest) {
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
      constexpr int Size = Dest::SizeAtCompileTime;
      Index size = dest.size();
      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
      MappedDest(actualDestPtr, dest.size()) = dest;
    }

    if (!UseRhs) {
#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
      constexpr int Size = ActualRhsTypeCleaned::SizeAtCompileTime;
      Index size = rhs.size();
      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
#endif
      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, rhs.size()) = rhs;
    }

    internal::selfadjoint_matrix_vector_product<
        Scalar, Index, (internal::traits<ActualLhsTypeCleaned>::Flags & RowMajorBit) ? RowMajor : ColMajor,
        int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate),
        bool(RhsBlasTraits::NeedToConjugate)>::run(lhs.rows(),                              // size
                                                   &lhs.coeffRef(0, 0), lhs.outerStride(),  // lhs info
                                                   actualRhsPtr,                            // rhs info
                                                   actualDestPtr,                           // result info
                                                   actualAlpha                              // scale factor
    );

    if (!EvalToDest) dest = MappedDest(actualDestPtr, dest.size());
  }
};

template <typename Lhs, typename Rhs, int RhsMode>
struct selfadjoint_product_impl<Lhs, 0, true, Rhs, RhsMode, false> {
  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
  enum { RhsUpLo = RhsMode & (Upper | Lower) };

  template <typename Dest>
  static void run(Dest& dest, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha) {
    // let's simply transpose the product
    Transpose<Dest> destT(dest);
    selfadjoint_product_impl<Transpose<const Rhs>, int(RhsUpLo) == Upper ? Lower : Upper, false, Transpose<const Lhs>,
                             0, true>::run(destT, a_rhs.transpose(), a_lhs.transpose(), alpha);
  }
};

}  // end namespace internal

}  // end namespace Eigen

#endif  // EIGEN_SELFADJOINT_MATRIX_VECTOR_H
