// 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_GENERAL_MATRIX_MATRIX_H
#define EIGEN_GENERAL_MATRIX_MATRIX_H

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

namespace Eigen {

namespace internal {

template <typename LhsScalar_, typename RhsScalar_>
class level3_blocking;

/* Specialization for a row-major destination matrix => simple transposition of the product */
template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
          int RhsStorageOrder, bool ConjugateRhs, int ResInnerStride>
struct general_matrix_matrix_product<Index, LhsScalar, LhsStorageOrder, ConjugateLhs, RhsScalar, RhsStorageOrder,
                                     ConjugateRhs, RowMajor, ResInnerStride> {
  typedef gebp_traits<RhsScalar, LhsScalar> Traits;

  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
  static EIGEN_STRONG_INLINE void run(Index rows, Index cols, Index depth, const LhsScalar* lhs, Index lhsStride,
                                      const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resIncr,
                                      Index resStride, ResScalar alpha, level3_blocking<RhsScalar, LhsScalar>& blocking,
                                      GemmParallelInfo<Index>* info = 0) {
    // transpose the product such that the result is column major
    general_matrix_matrix_product<Index, RhsScalar, RhsStorageOrder == RowMajor ? ColMajor : RowMajor, ConjugateRhs,
                                  LhsScalar, LhsStorageOrder == RowMajor ? ColMajor : RowMajor, ConjugateLhs, ColMajor,
                                  ResInnerStride>::run(cols, rows, depth, rhs, rhsStride, lhs, lhsStride, res, resIncr,
                                                       resStride, alpha, blocking, info);
  }
};

/*  Specialization for a col-major destination matrix
 *    => Blocking algorithm following Goto's paper */
template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar,
          int RhsStorageOrder, bool ConjugateRhs, int ResInnerStride>
struct general_matrix_matrix_product<Index, LhsScalar, LhsStorageOrder, ConjugateLhs, RhsScalar, RhsStorageOrder,
                                     ConjugateRhs, ColMajor, ResInnerStride> {
  typedef gebp_traits<LhsScalar, RhsScalar> Traits;

  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
  static void run(Index rows, Index cols, Index depth, const LhsScalar* lhs_, Index lhsStride, const RhsScalar* rhs_,
                  Index rhsStride, ResScalar* res_, Index resIncr, Index resStride, ResScalar alpha,
                  level3_blocking<LhsScalar, RhsScalar>& blocking, GemmParallelInfo<Index>* info = 0) {
    typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
    typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
    LhsMapper lhs(lhs_, lhsStride);
    RhsMapper rhs(rhs_, rhsStride);
    ResMapper res(res_, resStride, resIncr);

    Index kc = blocking.kc();                    // cache block size along the K direction
    Index mc = (std::min)(rows, blocking.mc());  // cache block size along the M direction
    Index nc = (std::min)(cols, blocking.nc());  // cache block size along the N direction

    gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing,
                  LhsStorageOrder>
        pack_lhs;
    gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;

#if !defined(EIGEN_USE_BLAS) && (defined(EIGEN_HAS_OPENMP) || defined(EIGEN_GEMM_THREADPOOL))
    if (info) {
      // this is the parallel version!
      int tid = info->logical_thread_id;
      int threads = info->num_threads;

      LhsScalar* blockA = blocking.blockA();
      eigen_internal_assert(blockA != 0);

      std::size_t sizeB = kc * nc;
      ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, 0);

      // For each horizontal panel of the rhs, and corresponding vertical panel of the lhs...
      for (Index k = 0; k < depth; k += kc) {
        const Index actual_kc = (std::min)(k + kc, depth) - k;  // => rows of B', and cols of the A'

        // In order to reduce the chance that a thread has to wait for the other,
        // let's start by packing B'.
        pack_rhs(blockB, rhs.getSubMapper(k, 0), actual_kc, nc);

        // Pack A_k to A' in a parallel fashion:
        // each thread packs the sub block A_k,i to A'_i where i is the thread id.

        // However, before copying to A'_i, we have to make sure that no other thread is still using it,
        // i.e., we test that info->task_info[tid].users equals 0.
        // Then, we set info->task_info[tid].users to the number of threads to mark that all other threads are going to
        // use it.
        while (info->task_info[tid].users != 0) {
        }
        info->task_info[tid].users = threads;

        pack_lhs(blockA + info->task_info[tid].lhs_start * actual_kc,
                 lhs.getSubMapper(info->task_info[tid].lhs_start, k), actual_kc, info->task_info[tid].lhs_length);

        // Notify the other threads that the part A'_i is ready to go.
        info->task_info[tid].sync = k;

        // Computes C_i += A' * B' per A'_i
        for (int shift = 0; shift < threads; ++shift) {
          int i = (tid + shift) % threads;

          // At this point we have to make sure that A'_i has been updated by the thread i,
          // we use testAndSetOrdered to mimic a volatile access.
          // However, no need to wait for the B' part which has been updated by the current thread!
          if (shift > 0) {
            while (info->task_info[i].sync != k) {
            }
          }

          gebp(res.getSubMapper(info->task_info[i].lhs_start, 0), blockA + info->task_info[i].lhs_start * actual_kc,
               blockB, info->task_info[i].lhs_length, actual_kc, nc, alpha);
        }

        // Then keep going as usual with the remaining B'
        for (Index j = nc; j < cols; j += nc) {
          const Index actual_nc = (std::min)(j + nc, cols) - j;

          // pack B_k,j to B'
          pack_rhs(blockB, rhs.getSubMapper(k, j), actual_kc, actual_nc);

          // C_j += A' * B'
          gebp(res.getSubMapper(0, j), blockA, blockB, rows, actual_kc, actual_nc, alpha);
        }

        // Release all the sub blocks A'_i of A' for the current thread,
        // i.e., we simply decrement the number of users by 1
        for (Index i = 0; i < threads; ++i) info->task_info[i].users -= 1;
      }
    } else
#endif  // defined(EIGEN_HAS_OPENMP) || defined(EIGEN_GEMM_THREADPOOL)
    {
      EIGEN_UNUSED_VARIABLE(info);

      // this is the sequential version!
      std::size_t sizeA = kc * mc;
      std::size_t sizeB = kc * nc;

      ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
      ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());

      const bool pack_rhs_once = mc != rows && kc == depth && nc == cols;

      // For each horizontal panel of the rhs, and corresponding panel of the lhs...
      for (Index i2 = 0; i2 < rows; i2 += mc) {
        const Index actual_mc = (std::min)(i2 + mc, rows) - i2;

        for (Index k2 = 0; k2 < depth; k2 += kc) {
          const Index actual_kc = (std::min)(k2 + kc, depth) - k2;

          // OK, here we have selected one horizontal panel of rhs and one vertical panel of lhs.
          // => Pack lhs's panel into a sequential chunk of memory (L2/L3 caching)
          // Note that this panel will be read as many times as the number of blocks in the rhs's
          // horizontal panel which is, in practice, a very low number.
          pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);

          // For each kc x nc block of the rhs's horizontal panel...
          for (Index j2 = 0; j2 < cols; j2 += nc) {
            const Index actual_nc = (std::min)(j2 + nc, cols) - j2;

            // We pack the rhs's block into a sequential chunk of memory (L2 caching)
            // Note that this block will be read a very high number of times, which is equal to the number of
            // micro horizontal panel of the large rhs's panel (e.g., rows/12 times).
            if ((!pack_rhs_once) || i2 == 0) pack_rhs(blockB, rhs.getSubMapper(k2, j2), actual_kc, actual_nc);

            // Everything is packed, we can now call the panel * block kernel:
            gebp(res.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, alpha);
          }
        }
      }
    }
  }
};

/*********************************************************************************
 *  Specialization of generic_product_impl for "large" GEMM, i.e.,
 *  implementation of the high level wrapper to general_matrix_matrix_product
 **********************************************************************************/

template <typename Scalar, typename Index, typename Gemm, typename Lhs, typename Rhs, typename Dest,
          typename BlockingType>
struct gemm_functor {
  gemm_functor(const Lhs& lhs, const Rhs& rhs, Dest& dest, const Scalar& actualAlpha, BlockingType& blocking)
      : m_lhs(lhs), m_rhs(rhs), m_dest(dest), m_actualAlpha(actualAlpha), m_blocking(blocking) {}

  void initParallelSession(Index num_threads) const {
    m_blocking.initParallel(m_lhs.rows(), m_rhs.cols(), m_lhs.cols(), num_threads);
    m_blocking.allocateA();
  }

  void operator()(Index row, Index rows, Index col = 0, Index cols = -1, GemmParallelInfo<Index>* info = 0) const {
    if (cols == -1) cols = m_rhs.cols();

    Gemm::run(rows, cols, m_lhs.cols(), &m_lhs.coeffRef(row, 0), m_lhs.outerStride(), &m_rhs.coeffRef(0, col),
              m_rhs.outerStride(), (Scalar*)&(m_dest.coeffRef(row, col)), m_dest.innerStride(), m_dest.outerStride(),
              m_actualAlpha, m_blocking, info);
  }

  typedef typename Gemm::Traits Traits;

 protected:
  const Lhs& m_lhs;
  const Rhs& m_rhs;
  Dest& m_dest;
  Scalar m_actualAlpha;
  BlockingType& m_blocking;
};

template <int StorageOrder, typename LhsScalar, typename RhsScalar, int MaxRows, int MaxCols, int MaxDepth,
          int KcFactor = 1, bool FiniteAtCompileTime = MaxRows != Dynamic && MaxCols != Dynamic && MaxDepth != Dynamic>
class gemm_blocking_space;

template <typename LhsScalar_, typename RhsScalar_>
class level3_blocking {
  typedef LhsScalar_ LhsScalar;
  typedef RhsScalar_ RhsScalar;

 protected:
  LhsScalar* m_blockA;
  RhsScalar* m_blockB;

  Index m_mc;
  Index m_nc;
  Index m_kc;

 public:
  level3_blocking() : m_blockA(0), m_blockB(0), m_mc(0), m_nc(0), m_kc(0) {}

  inline Index mc() const { return m_mc; }
  inline Index nc() const { return m_nc; }
  inline Index kc() const { return m_kc; }

  inline LhsScalar* blockA() { return m_blockA; }
  inline RhsScalar* blockB() { return m_blockB; }
};

template <int StorageOrder, typename LhsScalar_, typename RhsScalar_, int MaxRows, int MaxCols, int MaxDepth,
          int KcFactor>
class gemm_blocking_space<StorageOrder, LhsScalar_, RhsScalar_, MaxRows, MaxCols, MaxDepth, KcFactor,
                          true /* == FiniteAtCompileTime */>
    : public level3_blocking<std::conditional_t<StorageOrder == RowMajor, RhsScalar_, LhsScalar_>,
                             std::conditional_t<StorageOrder == RowMajor, LhsScalar_, RhsScalar_>> {
  enum {
    Transpose = StorageOrder == RowMajor,
    ActualRows = Transpose ? MaxCols : MaxRows,
    ActualCols = Transpose ? MaxRows : MaxCols
  };
  typedef std::conditional_t<Transpose, RhsScalar_, LhsScalar_> LhsScalar;
  typedef std::conditional_t<Transpose, LhsScalar_, RhsScalar_> RhsScalar;
  enum { SizeA = ActualRows * MaxDepth, SizeB = ActualCols * MaxDepth };

#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
  EIGEN_ALIGN_MAX LhsScalar m_staticA[SizeA];
  EIGEN_ALIGN_MAX RhsScalar m_staticB[SizeB];
#else
  EIGEN_ALIGN_MAX char m_staticA[SizeA * sizeof(LhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES - 1];
  EIGEN_ALIGN_MAX char m_staticB[SizeB * sizeof(RhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES - 1];
#endif

 public:
  gemm_blocking_space(Index /*rows*/, Index /*cols*/, Index /*depth*/, Index /*num_threads*/,
                      bool /*full_rows = false*/) {
    this->m_mc = ActualRows;
    this->m_nc = ActualCols;
    this->m_kc = MaxDepth;
#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
    this->m_blockA = m_staticA;
    this->m_blockB = m_staticB;
#else
    this->m_blockA = reinterpret_cast<LhsScalar*>((std::uintptr_t(m_staticA) + (EIGEN_DEFAULT_ALIGN_BYTES - 1)) &
                                                  ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES - 1));
    this->m_blockB = reinterpret_cast<RhsScalar*>((std::uintptr_t(m_staticB) + (EIGEN_DEFAULT_ALIGN_BYTES - 1)) &
                                                  ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES - 1));
#endif
  }

  void initParallel(Index, Index, Index, Index) {}

  inline void allocateA() {}
  inline void allocateB() {}
  inline void allocateAll() {}
};

template <int StorageOrder, typename LhsScalar_, typename RhsScalar_, int MaxRows, int MaxCols, int MaxDepth,
          int KcFactor>
class gemm_blocking_space<StorageOrder, LhsScalar_, RhsScalar_, MaxRows, MaxCols, MaxDepth, KcFactor, false>
    : public level3_blocking<std::conditional_t<StorageOrder == RowMajor, RhsScalar_, LhsScalar_>,
                             std::conditional_t<StorageOrder == RowMajor, LhsScalar_, RhsScalar_>> {
  enum { Transpose = StorageOrder == RowMajor };
  typedef std::conditional_t<Transpose, RhsScalar_, LhsScalar_> LhsScalar;
  typedef std::conditional_t<Transpose, LhsScalar_, RhsScalar_> RhsScalar;

  Index m_sizeA;
  Index m_sizeB;

 public:
  gemm_blocking_space(Index rows, Index cols, Index depth, Index num_threads, bool l3_blocking) {
    this->m_mc = Transpose ? cols : rows;
    this->m_nc = Transpose ? rows : cols;
    this->m_kc = depth;

    if (l3_blocking) {
      computeProductBlockingSizes<LhsScalar, RhsScalar, KcFactor>(this->m_kc, this->m_mc, this->m_nc, num_threads);
    } else  // no l3 blocking
    {
      Index n = this->m_nc;
      computeProductBlockingSizes<LhsScalar, RhsScalar, KcFactor>(this->m_kc, this->m_mc, n, num_threads);
    }

    m_sizeA = this->m_mc * this->m_kc;
    m_sizeB = this->m_kc * this->m_nc;
  }

  void initParallel(Index rows, Index cols, Index depth, Index num_threads) {
    this->m_mc = Transpose ? cols : rows;
    this->m_nc = Transpose ? rows : cols;
    this->m_kc = depth;

    eigen_internal_assert(this->m_blockA == 0 && this->m_blockB == 0);
    Index m = this->m_mc;
    computeProductBlockingSizes<LhsScalar, RhsScalar, KcFactor>(this->m_kc, m, this->m_nc, num_threads);
    m_sizeA = this->m_mc * this->m_kc;
    m_sizeB = this->m_kc * this->m_nc;
  }

  void allocateA() {
    if (this->m_blockA == 0) this->m_blockA = aligned_new<LhsScalar>(m_sizeA);
  }

  void allocateB() {
    if (this->m_blockB == 0) this->m_blockB = aligned_new<RhsScalar>(m_sizeB);
  }

  void allocateAll() {
    allocateA();
    allocateB();
  }

  ~gemm_blocking_space() {
    aligned_delete(this->m_blockA, m_sizeA);
    aligned_delete(this->m_blockB, m_sizeB);
  }
};

}  // end namespace internal

namespace internal {

template <typename Lhs, typename Rhs>
struct generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, GemmProduct>
    : generic_product_impl_base<Lhs, Rhs, generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, GemmProduct>> {
  typedef typename Product<Lhs, Rhs>::Scalar Scalar;
  typedef typename Lhs::Scalar LhsScalar;
  typedef typename Rhs::Scalar RhsScalar;

  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 { MaxDepthAtCompileTime = min_size_prefer_fixed(Lhs::MaxColsAtCompileTime, Rhs::MaxRowsAtCompileTime) };

  typedef generic_product_impl<Lhs, Rhs, DenseShape, DenseShape, CoeffBasedProductMode> lazyproduct;

  template <typename Dst>
  static void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
    // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=404 for a discussion and helper program
    // to determine the following heuristic.
    // EIGEN_GEMM_TO_COEFFBASED_THRESHOLD is typically defined to 20 in GeneralProduct.h,
    // unless it has been specialized by the user or for a given architecture.
    // Note that the condition rhs.rows()>0 was required because lazy product is (was?) not happy with empty inputs.
    // I'm not sure it is still required.
    if ((rhs.rows() + dst.rows() + dst.cols()) < EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows() > 0)
      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::assign_op<typename Dst::Scalar, Scalar>());
    else {
      dst.setZero();
      scaleAndAddTo(dst, lhs, rhs, Scalar(1));
    }
  }

  template <typename Dst>
  static void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
    if ((rhs.rows() + dst.rows() + dst.cols()) < EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows() > 0)
      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::add_assign_op<typename Dst::Scalar, Scalar>());
    else
      scaleAndAddTo(dst, lhs, rhs, Scalar(1));
  }

  template <typename Dst>
  static void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs) {
    if ((rhs.rows() + dst.rows() + dst.cols()) < EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows() > 0)
      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::sub_assign_op<typename Dst::Scalar, Scalar>());
    else
      scaleAndAddTo(dst, lhs, rhs, Scalar(-1));
  }

  template <typename Dest>
  static void scaleAndAddTo(Dest& dst, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha) {
    eigen_assert(dst.rows() == a_lhs.rows() && dst.cols() == a_rhs.cols());
    if (a_lhs.cols() == 0 || a_lhs.rows() == 0 || a_rhs.cols() == 0) return;

    if (dst.cols() == 1) {
      // Fallback to GEMV if either the lhs or rhs is a runtime vector
      typename Dest::ColXpr dst_vec(dst.col(0));
      return internal::generic_product_impl<Lhs, typename Rhs::ConstColXpr, DenseShape, DenseShape,
                                            GemvProduct>::scaleAndAddTo(dst_vec, a_lhs, a_rhs.col(0), alpha);
    } else if (dst.rows() == 1) {
      // Fallback to GEMV if either the lhs or rhs is a runtime vector
      typename Dest::RowXpr dst_vec(dst.row(0));
      return internal::generic_product_impl<typename Lhs::ConstRowXpr, Rhs, DenseShape, DenseShape,
                                            GemvProduct>::scaleAndAddTo(dst_vec, a_lhs.row(0), a_rhs, alpha);
    }

    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 = combine_scalar_factors(alpha, a_lhs, a_rhs);

    typedef internal::gemm_blocking_space<(Dest::Flags & RowMajorBit) ? RowMajor : ColMajor, LhsScalar, RhsScalar,
                                          Dest::MaxRowsAtCompileTime, Dest::MaxColsAtCompileTime, MaxDepthAtCompileTime>
        BlockingType;

    typedef internal::gemm_functor<
        Scalar, Index,
        internal::general_matrix_matrix_product<
            Index, LhsScalar, (ActualLhsTypeCleaned::Flags & RowMajorBit) ? RowMajor : ColMajor,
            bool(LhsBlasTraits::NeedToConjugate), RhsScalar,
            (ActualRhsTypeCleaned::Flags & RowMajorBit) ? RowMajor : ColMajor, bool(RhsBlasTraits::NeedToConjugate),
            (Dest::Flags & RowMajorBit) ? RowMajor : ColMajor, Dest::InnerStrideAtCompileTime>,
        ActualLhsTypeCleaned, ActualRhsTypeCleaned, Dest, BlockingType>
        GemmFunctor;

    BlockingType blocking(dst.rows(), dst.cols(), lhs.cols(), 1, true);
    internal::parallelize_gemm<(Dest::MaxRowsAtCompileTime > 32 || Dest::MaxRowsAtCompileTime == Dynamic)>(
        GemmFunctor(lhs, rhs, dst, actualAlpha, blocking), a_lhs.rows(), a_rhs.cols(), a_lhs.cols(),
        Dest::Flags & RowMajorBit);
  }
};

}  // end namespace internal

}  // end namespace Eigen

#endif  // EIGEN_GENERAL_MATRIX_MATRIX_H