TensorContractionSycl.h

// This file is part of Eigen, a lightweight C++ template library for linear algebra.
//
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.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/.

/*****************************************************************
 * TensorContractionSycl.h
 *
 * \brief:
 *  TensorContractionSycl.h, provides various tensor contraction kernel for SYCL backend
 *
 *****************************************************************/

#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H

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

namespace Eigen {

namespace TensorSycl {
namespace internal {

#ifndef EIGEN_SYCL_DISABLE_GEMV

template <typename Scalar, typename StorageIndex, StorageIndex NCWindow, StorageIndex CFactor, StorageIndex NCFactor>
struct TVPanelSize {
  // LocalThreadSizeC: determines total number of thread per workgroup for the contracting dimension
  static constexpr StorageIndex LocalThreadSizeC = EIGEN_SYCL_LOCAL_THREAD_DIM0;
  // LocalThreadSizeNC: determines total number of thread per workgroup for the non-contracting dimension
  static constxpr StorageIndex LocalThreadSizeNC = EIGEN_SYCL_LOCAL_THREAD_DIM1;
  // TileSizeDimNC: determines the tile size for the non-contracting dimension
  static constexpr StorageIndex TileSizeDimNC = NCWindow / NCFactor;
  // TileSizeDimC: determines the tile size for the contracting dimension
  static constexpr StorageIndex TileSizeDimC = CFactor * LocalThreadSizeNC * LocalThreadSizeC;
  // WorkLoadPerThreadNC : determines workload per thread for loading the non-contracting dimension
  static constexpr StorageIndex WorkLoadPerThreadNC = TileSizeDimNC / LocalThreadSizeNC;
  // WorkLoadPerThreadC: determines workload per thread for loading the non-contracting dimension
  static constexpr StorageIndex WorkLoadPerThreadC = TileSizeDimC / LocalThreadSizeC;
  // BC : determines if supporting bank conflict is required
  static constexpr bool BC = false;
};
#endif

template <typename Scalar, typename StorageIndex, StorageIndex REG_SIZE_M, StorageIndex REG_SIZE_N, StorageIndex TSDK>
struct TTPanelSize {
  // TileSizeDimK: determines Tile size for dimension K. The packet size is assumed to be considered
  static constexpr StorageIndex TileSizeDimK = TSDK;
  // WorkLoadPerThreadM : determines workload per thread for loading the M dimension This can be varied based on the
  // available register on a chosen device(can be controlled by EIGEN_SYCL_REG_M macro//
#ifndef EIGEN_SYCL_REG_M
  static constexpr StorageIndex WorkLoadPerThreadM = REG_SIZE_M;
#else
  static constexpr StorageIndex WorkLoadPerThreadM = EIGEN_SYCL_REG_M;
#endif
// WorkLoadPerThreadN : determines workload per thread for loading the N dimension This can be varied based on the
// available register on a chosen device(can be controlled by EIGEN_SYCL_REG_N macro
#ifndef EIGEN_SYCL_REG_N
  static constexpr StorageIndex WorkLoadPerThreadN = REG_SIZE_N;
#else
  static constexpr StorageIndex WorkLoadPerThreadN = EIGEN_SYCL_REG_N;
#endif
  // LocalThreadSizeM: determines total number of thread per workgroup for the m dimension
  static constexpr StorageIndex LocalThreadSizeM = EIGEN_SYCL_LOCAL_THREAD_DIM0;
  // LocalThreadSizeN: determines total number of thread per workgroup for the n dimension
  static constexpr StorageIndex LocalThreadSizeN = EIGEN_SYCL_LOCAL_THREAD_DIM1;
  // TileSizeDimM: determines the tile size for the m dimension
  static constexpr StorageIndex TileSizeDimM = LocalThreadSizeM * WorkLoadPerThreadM;
  // TileSizeDimN: determines the tile size for the n dimension
  static constexpr StorageIndex TileSizeDimN = LocalThreadSizeN * WorkLoadPerThreadN;
  // LoadPerThreadLhs: determines workload per thread for loading Lhs Tensor. This must be divisible by packetsize
  static constexpr StorageIndex LoadPerThreadLhs =
      ((TileSizeDimK * WorkLoadPerThreadM * WorkLoadPerThreadN) / (TileSizeDimN));
  // LoadPerThreadRhs: determines workload per thread for loading Rhs Tensor. This must be divisible by packetsize
  static constexpr StorageIndex LoadPerThreadRhs =
      ((TileSizeDimK * WorkLoadPerThreadM * WorkLoadPerThreadN) / (TileSizeDimM));
  // BC : determines if supporting bank conflict is required
  static constexpr bool BC = true;
  // DoubleBuffer: determines if double buffering technique should be used (This can be disabled by
  // EIGEN_SYCL_DISABLE_DOUBLE_BUFFER macro when the device does not have sufficient local memory)
  static constexpr bool DoubleBuffer =
#ifdef EIGEN_SYCL_DISABLE_DOUBLE_BUFFER
      false;
#else
      true;
#endif
};

/* !
 * \brief contraction_type: an enum class representing the Tensor Contraction implementation algorithm. This is used to
 * specialize the contraction algorithm based on device support for dedicated local memory.
 */
enum class contraction_type { local, no_local };
/* !
 * \brief data_source an enum class determining the location of the data in a memory hierarchy (global, local, private).
 */
enum class data_source { global_mem, local_mem, private_mem };

template <bool PacketLoad, bool is_coalesced_layout, bool, typename PacketType, typename TensorMapper,
          typename StorageIndex>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<PacketLoad, PacketType> read(
    const TensorMapper &tensorMapper, const StorageIndex &NCIndex, const StorageIndex &CIndex, const StorageIndex &ld) {
  const StorageIndex row = (is_coalesced_layout) ? NCIndex : CIndex;
  const StorageIndex col = (is_coalesced_layout) ? CIndex : NCIndex;
  return tensorMapper.get_tensor().template packet<Unaligned>(row + (col * ld));
}

template <bool PacketLoad, bool, bool IsRhs, typename PacketType, typename TensorMapper, typename StorageIndex>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<!PacketLoad, PacketType> read(
    const TensorMapper &tensorMapper, const StorageIndex &NCIndex, const StorageIndex &CIndex, const StorageIndex &) {
  const StorageIndex row = (IsRhs) ? CIndex : NCIndex;
  const StorageIndex col = (IsRhs) ? NCIndex : CIndex;
  return tensorMapper(row, col);
}

template <typename StorageIndex, StorageIndex ld, data_source dt, typename PacketType, typename DataScalar>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<dt != data_source::global_mem, void> write(
    PacketType &packet_data, DataScalar ptr) {
  constexpr int PacketSize = Eigen::internal::unpacket_traits<PacketType>::size;
  EIGEN_UNROLL_LOOP
  for (int i = 0; i < PacketSize; i++) {
    *ptr = PacketWrapper<PacketType, PacketSize>::scalarize(i, packet_data);
    ptr += ld;
  }
}

template <data_source dt, typename PacketType, typename DataScalar>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    typename std::enable_if_t<Eigen::internal::unpacket_traits<PacketType>::size != 1 && dt == data_source::global_mem,
                              void>
    write(PacketType &packet_data, DataScalar *ptr) {
  ::Eigen::internal::pstoreu<DataScalar, PacketType>(ptr, packet_data);
}

template <data_source dt, typename PacketType, typename DataScalar>
static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
    typename std::enable_if_t<Eigen::internal::unpacket_traits<PacketType>::size == 1 && dt == data_source::global_mem,
                              void>
    write(PacketType &packet_data, DataScalar *ptr) {
  *ptr = packet_data;
}

template <bool is_internal>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_boundary(bool) {
  return true;
}

template <>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_boundary<false>(bool cond) {
  return cond;
}

template <bool is_transposed, bool is_rhs_, bool packet_load_, typename PacketType>
struct BlockProperties {
  static constexpr bool packet_load = packet_load_;
  typedef typename Eigen::internal::unpacket_traits<PacketType>::type OutScalar;
  static constexpr bool is_rhs = is_rhs_;
  typedef std::conditional_t<packet_load, PacketType, OutScalar> OutType;
  static constexpr int elements_per_access = Eigen::internal::unpacket_traits<OutType>::size;
  static constexpr bool is_coalesced_layout = !(is_transposed ^ is_rhs);
  static constexpr int nc_stride = (is_coalesced_layout ? elements_per_access : 1);
  static constexpr int c_stride = (is_coalesced_layout ? 1 : elements_per_access);
};

template <typename StorageIndex>
struct ThreadProperties {
  const StorageIndex linearLocalThreadId;
  const StorageIndex kGroupId;
  const StorageIndex mGroupOffset;
  const StorageIndex nGroupOffset;
  const StorageIndex kGroupOffset;
  const StorageIndex mLocalOffset;
  const StorageIndex nLocalOffset;
  const StorageIndex mGlobalOffset;
  const StorageIndex nGlobalOffset;
  StorageIndex kSize;
  const bool is_internal;
  // this is used to adjust the last block
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ThreadProperties(
      const StorageIndex linearLocalThreadId_, const StorageIndex kGroupId_, const StorageIndex mGroupOffset_,
      const StorageIndex nGroupOffset_, const StorageIndex kGroupOffset_, const StorageIndex mLocalOffset_,
      const StorageIndex nLocalOffset_, const StorageIndex mGlobalOffset_, const StorageIndex nGlobalOffset_,
      StorageIndex kSize_, const bool is_internal_)
      : linearLocalThreadId(linearLocalThreadId_),
        kGroupId(kGroupId_),
        mGroupOffset(mGroupOffset_),
        nGroupOffset(nGroupOffset_),
        kGroupOffset(kGroupOffset_),
        mLocalOffset(mLocalOffset_),
        nLocalOffset(nLocalOffset_),
        mGlobalOffset(mGlobalOffset_),
        nGlobalOffset(nGlobalOffset_),
        kSize(kSize_),
        is_internal(is_internal_) {}
};

template <typename OutScalar, typename LhsScalar, typename RhsScalar, typename OutAccessor, typename LhsMapper,
          typename RhsMapper, typename StorageIndex, typename Properties, typename TripleDim, bool Vectorizable,
          typename input_mapper_properties, bool IsFinal, contraction_type contraction_tp>
class TensorContractionKernel {
 public:
  typedef typename Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketReturnType
      PacketReturnType;
  static constexpr int PacketSize =
      Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketSize;
  static constexpr bool is_lhs_transposed =
      !::Eigen::internal::TensorContractionInputMapperTrait<LhsMapper>::inner_dim_contiguous;
  static constexpr bool is_rhs_transposed =
      !::Eigen::internal::TensorContractionInputMapperTrait<RhsMapper>::inner_dim_contiguous;

  typedef BlockProperties<is_lhs_transposed, false, input_mapper_properties::is_lhs_matrix && Vectorizable,
                          PacketReturnType>
      LHSBlockProperties;

  typedef BlockProperties<is_rhs_transposed, true, input_mapper_properties::is_rhs_matrix && Vectorizable,
                          PacketReturnType>
      RHSBlockProperties;

  static constexpr StorageIndex NStride =
      contraction_tp == contraction_type::local ? Properties::WorkLoadPerThreadN : RHSBlockProperties::nc_stride;

  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
  typedef cl::sycl::multi_ptr<OutScalar, cl::sycl::access::address_space::local_space> local_ptr;
  typedef OutScalar * /*cl::sycl::multi_ptr<OutScalar, cl::sycl::access::address_space::private_space>*/ private_ptr;
  typedef std::conditional_t<contraction_tp == contraction_type::local, local_ptr, private_ptr> tile_ptr;
  static constexpr StorageIndex LSDL = contraction_tp == contraction_type::local
                                           ? Properties::TileSizeDimM + Properties::BC
                                           : Properties::WorkLoadPerThreadM;
  static constexpr StorageIndex LSDR = contraction_tp == contraction_type::local
                                           ? Properties::TileSizeDimN + Properties::BC
                                           : Properties::WorkLoadPerThreadN;
  static constexpr StorageIndex LocalOffset = Properties::LocalThreadSizeM * Properties::LocalThreadSizeN;

  template <contraction_type, StorageIndex>
  struct MemHolder {
    tile_ptr ptr;
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MemHolder(local_ptr block_start_ptr) : ptr(block_start_ptr) {}
  };
  template <StorageIndex MemSize>
  struct MemHolder<contraction_type::no_local, MemSize> {
    OutScalar ptr[MemSize] = {OutScalar{0}};
  };
  struct TiledMemory {
    MemHolder<contraction_tp, Properties::WorkLoadPerThreadM * Properties::TileSizeDimK> lhs_scratch_extract;
    MemHolder<contraction_tp, Properties::WorkLoadPerThreadN * Properties::TileSizeDimK> rhs_scratch_extract;
    tile_ptr lhs_scratch_ptr_compute;
    tile_ptr rhs_scratch_ptr_compute;
    const std::pair<StorageIndex, StorageIndex> lhs_extract_index;
    const std::pair<StorageIndex, StorageIndex> rhs_extract_index;
    template <contraction_type tp = contraction_tp>
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TiledMemory(const ThreadProperties<StorageIndex> &, local_ptr,
                                                      std::enable_if_t<tp == contraction_type::no_local> * = 0)
        : lhs_scratch_extract{},
          rhs_scratch_extract{},
          lhs_scratch_ptr_compute(lhs_scratch_extract.ptr),
          rhs_scratch_ptr_compute(rhs_scratch_extract.ptr),
          lhs_extract_index(std::pair<StorageIndex, StorageIndex>(StorageIndex{0}, StorageIndex{0})),
          rhs_extract_index(std::pair<StorageIndex, StorageIndex>(StorageIndex{0}, StorageIndex{0})) {}

    template <contraction_type tp = contraction_tp>
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TiledMemory(const ThreadProperties<StorageIndex> &thread_properties,
                                                      local_ptr block_start_ptr,
                                                      std::enable_if_t<tp == contraction_type::local> * = 0)
        : lhs_scratch_extract{block_start_ptr},
          rhs_scratch_extract{lhs_scratch_extract.ptr +
                              ((Properties::DoubleBuffer + 1) * LSDL * Properties::TileSizeDimK)},
          lhs_scratch_ptr_compute(lhs_scratch_extract.ptr + thread_properties.mLocalOffset),
          rhs_scratch_ptr_compute(rhs_scratch_extract.ptr + thread_properties.nLocalOffset),
          lhs_extract_index(
              local_id_extract<LHSBlockProperties, Properties::TileSizeDimM>(thread_properties.linearLocalThreadId)),
          rhs_extract_index(
              local_id_extract<RHSBlockProperties, Properties::TileSizeDimN>(thread_properties.linearLocalThreadId)) {}
  };

  Scratch scratch;
  const LhsMapper lhs;
  const RhsMapper rhs;
  OutAccessor out_res;
  const StorageIndex groupSizeM;
  const StorageIndex groupSizeN;
  const StorageIndex numTiles;
  const TripleDim triple_dim;

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionKernel(Scratch scratch_, const LhsMapper lhs_,
                                                                const RhsMapper rhs_, OutAccessor out_res_,
                                                                const StorageIndex groupSizeM_,
                                                                const StorageIndex groupSizeN_,
                                                                const StorageIndex numTiles_,
                                                                const TripleDim triple_dim_)
      : scratch(scratch_),
        lhs(lhs_),
        rhs(rhs_),
        out_res(out_res_),
        groupSizeM(groupSizeM_),
        groupSizeN(groupSizeN_),
        numTiles(numTiles_),
        triple_dim(triple_dim_) {}

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionKernel(Scratch scratch_, const LhsMapper lhs_,
                                                                const RhsMapper rhs_, OutAccessor out_res_,
                                                                const StorageIndex groupSizeM_,
                                                                const StorageIndex numTiles_,
                                                                const TripleDim triple_dim_)
      : TensorContractionKernel(scratch_, lhs_, rhs_, out_res_, groupSizeM_, 1, numTiles_, triple_dim_) {}

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) const {
    const StorageIndex linearLocalThreadId = itemID.get_local_id(0);
    const StorageIndex nLocalThreadId = linearLocalThreadId / Properties::LocalThreadSizeM;
    const StorageIndex mLocalThreadId = linearLocalThreadId % Properties::LocalThreadSizeM;
    const StorageIndex mGroupId = itemID.get_group(0) % groupSizeM;
    const StorageIndex tmp = itemID.get_group(0) / groupSizeM;
    const StorageIndex nGroupId = IsFinal ? tmp : tmp % groupSizeN;
    const StorageIndex kGroupId = IsFinal ? 0 : tmp / groupSizeN;
    const StorageIndex mGroupOffset = mGroupId * Properties::TileSizeDimM;
    const StorageIndex nGroupOffset = nGroupId * Properties::TileSizeDimN;
    const StorageIndex mLocalOffset = PacketSize * mLocalThreadId;
    const StorageIndex nLocalOffset = NStride * nLocalThreadId;
    const StorageIndex mGlobalOffset = mGroupOffset + mLocalOffset;
    const StorageIndex nGlobalOffset = nGroupOffset + nLocalOffset;

    const StorageIndex kSizePerWG = IsFinal ? triple_dim.K : numTiles * Properties::TileSizeDimK;
    StorageIndex kGroupOffset = kGroupId * kSizePerWG;
    const bool is_internal = triple_dim.M - mGroupOffset >= Properties::TileSizeDimM &&
                             triple_dim.N - nGroupOffset >= Properties::TileSizeDimN &&
                             triple_dim.K - kGroupOffset >= kSizePerWG;
    // this is used to adjust the last block
    StorageIndex kSize = IsFinal ? triple_dim.K : std::min(kSizePerWG, triple_dim.K - kGroupOffset);
    // This is used to find out the lats K offset so that kGroupOffset -kSize can compute the coffset for loading to
    // tile
    kGroupOffset += kSize;

    auto thread_properties =
        ThreadProperties<StorageIndex>(linearLocalThreadId, kGroupId, mGroupOffset, nGroupOffset, kGroupOffset,
                                       mLocalOffset, nLocalOffset, mGlobalOffset, nGlobalOffset, kSize, is_internal);

    auto out_ptr = out_res + (IsFinal ? 0 : thread_properties.kGroupId * triple_dim.M * triple_dim.N);

    (thread_properties.is_internal) ? compute_panel<true>(itemID, thread_properties, out_ptr)
                                    : compute_panel<false>(itemID, thread_properties, out_ptr);
  }
  // The compute block computes the contraction operation private block for each thread and store the resutl in the
  // privateRes memory of Each computation the compute block function is independent of local and no local concepts as
  // it only compute the block on each thread's private memory space
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_block_per_tile(OutScalar *lhs_block_ptr, OutScalar *rhs_block_ptr,
                                                                    PacketReturnType *privateRes) const {
    StorageIndex idx = 0;
    constexpr StorageIndex lhs_stride =
        contraction_tp == contraction_type::local ? (PacketSize * Properties::LocalThreadSizeM) : 1;
    EIGEN_UNROLL_LOOP
    for (StorageIndex wLPTN = 0; wLPTN < Properties::WorkLoadPerThreadN; wLPTN++) {
      auto rhsPacket = PacketReturnType{*(rhs_block_ptr + wLPTN)};
      StorageIndex lhs_index = 0;
      EIGEN_UNROLL_LOOP
      for (StorageIndex wLPTM = 0; wLPTM < Properties::WorkLoadPerThreadM / PacketSize; wLPTM++) {
        PacketReturnType lhsPack{};
        Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, PacketSize>::set_packet(lhsPack,
                                                                                             lhs_block_ptr + lhs_index);
        privateRes[idx] = ::Eigen::internal::pmadd(lhsPack, rhsPacket, privateRes[idx]);

        lhs_index += lhs_stride;
        idx++;
      }
    }
  }
  // The store function write the computed contraction operation in the private memory of each thread to the global
  // memory. The store function is independent of local and no local concepts s that it can be abstract out in the base
  // class.
  template <bool is_internal_block, StorageIndex PrivateNStride, typename OutPtr>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void store(OutPtr *out_ptr, PacketReturnType *privateRes,
                                                   StorageIndex mGlobalOffset, StorageIndex nGlobalOffset) const {
    auto chk_bound = [&](const StorageIndex &mIndex, const StorageIndex &nIndex) EIGEN_DEVICE_FUNC {
      return (mIndex + PacketSize - 1 < triple_dim.M && nGlobalOffset + nIndex < triple_dim.N);
    };
    // when local memory is not used M and N are both accessed in a coalesced way. However, when local memory is
    // available the k*N is transposed in the local to N*K therefore, each blocks operates on blockId*
    // WorkLoadPerThreadN slice of N
    constexpr StorageIndex GlobalNStride = contraction_tp == contraction_type::local ? 1 : Properties::LocalThreadSizeN;
    EIGEN_UNROLL_LOOP
    for (StorageIndex wLPTN = 0; wLPTN < Properties::WorkLoadPerThreadN / PrivateNStride; wLPTN++) {
      // output leading dimension
      StorageIndex outputLD = 0;
      // When local memory is used the PrivateNstride is always 1 because the coalesced access on N is loaded into Local
      // memory and extracting from local to global is the same as no transposed version. However, when local memory is
      // not used and RHS is transposed we packetize the load for RHS.
      EIGEN_UNROLL_LOOP
      for (StorageIndex nId = 0; nId < PrivateNStride; nId++) {
        StorageIndex globalRow = mGlobalOffset;
        EIGEN_UNROLL_LOOP
        for (StorageIndex wLPTM = 0; wLPTM < Properties::WorkLoadPerThreadM / PacketSize; wLPTM++) {
          PacketReturnType privetOut = privateRes[wLPTM];
          if (check_boundary<is_internal_block>(chk_bound(globalRow, nId))) {
            // Store the final results in C. The C matrix has always M as a first StorageIndex and N as a second
            // StorageIndex Therefore it is always coalesced layout
            write<data_source::global_mem>(privetOut, out_ptr + outputLD + globalRow);
          } else {
            EIGEN_UNROLL_LOOP
            for (StorageIndex mId = 0; mId < PacketSize; mId++) {
              StorageIndex mOffset = globalRow + mId;
              if (mOffset < triple_dim.M && (nGlobalOffset + nId < triple_dim.N)) {
                out_ptr[mOffset + outputLD] =
                    Eigen::TensorSycl::internal::PacketWrapper<PacketReturnType, PacketSize>::scalarize(mId, privetOut);
              }
            }
          }
          globalRow += (PacketSize * Properties::LocalThreadSizeM);
        }
        outputLD += triple_dim.M;
        privateRes += Properties::WorkLoadPerThreadM / PacketSize;
      }
      out_ptr += (GlobalNStride * outputLD);

      nGlobalOffset += (PrivateNStride * GlobalNStride);
    }
  }
  // when no local memory is used the following extract_block will be enabled
  template <typename InputBlockProperties, bool is_internal_block, typename Input, typename PrivateReg,
            contraction_type contract_tp = contraction_tp>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<contract_tp == contraction_type::no_local> extract_block(
      const Input &inpt, PrivateReg private_ptr, const std::pair<StorageIndex, StorageIndex> &,
      const StorageIndex &ncOffset, const StorageIndex cOffset) const {
    constexpr StorageIndex LocalThreadSizeNC =
        InputBlockProperties::is_rhs ? Properties::LocalThreadSizeN : Properties::LocalThreadSizeM;
    constexpr StorageIndex WorkLoadPerThreadNC =
        InputBlockProperties::is_rhs ? Properties::WorkLoadPerThreadN : Properties::WorkLoadPerThreadM;
    const StorageIndex &NC = InputBlockProperties::is_rhs ? triple_dim.N : triple_dim.M;

    auto chk_bound = [&](const StorageIndex &CIndex, const StorageIndex &NCIndex) EIGEN_DEVICE_FUNC {
      return ((CIndex + InputBlockProperties::c_stride - 1 < triple_dim.K) &&
              (NCIndex + InputBlockProperties::nc_stride - 1 < NC));
    };
    const StorageIndex ld = InputBlockProperties::is_coalesced_layout ? NC : triple_dim.K;
    StorageIndex cIndex = cOffset;

    EIGEN_UNROLL_LOOP
    for (StorageIndex cId = 0; cId < Properties::TileSizeDimK / InputBlockProperties::c_stride; cId++) {
      StorageIndex ncIndex = ncOffset;
      EIGEN_UNROLL_LOOP
      for (StorageIndex ncId = 0; ncId < WorkLoadPerThreadNC / InputBlockProperties::nc_stride; ncId++) {
        if (check_boundary<is_internal_block>(chk_bound(cIndex, ncIndex))) {
          auto val =
              read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
                   InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, ncIndex, cIndex, ld);

          write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : WorkLoadPerThreadNC),
                data_source::private_mem>(val, private_ptr);
        } else {
          EIGEN_UNROLL_LOOP
          for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
            const StorageIndex ncInd = ncIndex + (InputBlockProperties::is_coalesced_layout ? i : 0);
            const StorageIndex cInd = cIndex + (InputBlockProperties::is_coalesced_layout ? 0 : i);
            OutScalar val =
                (ncInd < NC && cInd < triple_dim.K)
                    ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
                          inpt, ncInd, cInd, ld)
                    : OutScalar(0);
            write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : WorkLoadPerThreadNC),
                  data_source::private_mem>(
                val, private_ptr + (InputBlockProperties::is_coalesced_layout ? i : 0) +
                         ((InputBlockProperties::is_coalesced_layout ? 0 : i) * WorkLoadPerThreadNC));
          }
        }

        // if it is lhs we have to load it packetised when the packet size is > 1, because the output is coalesced. So
        // even if M is not accessed in a coalesced mode, we have to load packet_size number of m per thread.
        ncIndex = (!InputBlockProperties::is_rhs && InputBlockProperties::nc_stride == 1 && PacketSize != 1)
                      ? ncOffset + (ncId + 1) % PacketSize + ((ncId + 1) / PacketSize) * LocalThreadSizeNC
                      : (ncIndex + InputBlockProperties::nc_stride * LocalThreadSizeNC);
        private_ptr += InputBlockProperties::nc_stride;
      }
      // the previous for loop ( private_ptr += (ncId * nc_stride)) has already moved ptr with one WorkLoadPerThreadNC
      private_ptr += (InputBlockProperties::c_stride - 1) * WorkLoadPerThreadNC;
      cIndex += InputBlockProperties::c_stride;
    }
  }
  template <typename InputBlockProperties, StorageIndex TileSizeDimNC>
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::pair<StorageIndex, StorageIndex> local_id_extract(
      const StorageIndex &linearLocalThreadId) {
    const StorageIndex localThreadNC =
        (InputBlockProperties::is_coalesced_layout)
            ? linearLocalThreadId % (TileSizeDimNC / InputBlockProperties::nc_stride)
            : linearLocalThreadId / (Properties::TileSizeDimK / InputBlockProperties::c_stride);
    const StorageIndex localThreadC =
        (InputBlockProperties::is_coalesced_layout)
            ? linearLocalThreadId / (TileSizeDimNC / InputBlockProperties::nc_stride)
            : linearLocalThreadId % (Properties::TileSizeDimK / InputBlockProperties::c_stride);
    return std::pair<StorageIndex, StorageIndex>(localThreadNC, localThreadC);
  }

  template <bool db = Properties::DoubleBuffer, contraction_type ctp = contraction_tp>
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<db && ctp == contraction_type::local> sync_mem(
      const cl::sycl::nd_item<1> &, bool &db_offset) noexcept {
    db_offset = !db_offset;
  }

  template <bool db = Properties::DoubleBuffer, contraction_type ctp = contraction_tp>
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<!db && ctp == contraction_type::local> sync_mem(
      const cl::sycl::nd_item<1> &itemID, bool &) noexcept {
    itemID.barrier(cl::sycl::access::fence_space::local_space);
  }

  template <contraction_type ctp = contraction_tp>
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<ctp == contraction_type::no_local> sync_mem(
      const cl::sycl::nd_item<1> &, bool &) noexcept {
    return;
  }

  template <bool need_sync, contraction_type ctp = contraction_tp>
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<need_sync && ctp == contraction_type::no_local>
  sync_thread(const cl::sycl::nd_item<1> &
#ifdef EIGEN_SYCL_ARM_GPU_CACHE_OPTIMISATION
                  itemID
#endif
              ) noexcept {
#ifdef EIGEN_SYCL_ARM_GPU_CACHE_OPTIMISATION
    itemID.barrier(cl::sycl::access::fence_spacce::local_space);
#else
    return;
#endif
  }
  template <bool need_sync, contraction_type ctp = contraction_tp>
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<need_sync && ctp == contraction_type::local>
  sync_thread(const cl::sycl::nd_item<1> &itemID) {
    itemID.barrier(cl::sycl::access::fence_space::local_space);
  }
  template <bool need_sync>
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<!need_sync> sync_thread(const cl::sycl::nd_item<1> &) {
    return;
  }

  template <bool is_internal_block>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_tile_per_panel(const cl::sycl::nd_item<1> &itemID,
                                                                    ThreadProperties<StorageIndex> &thread_properties,
                                                                    TiledMemory &tiled_input_block,
                                                                    PacketReturnType *privateRes,
                                                                    bool &db_offset) const {
    // Tiling the Rhs block from global to local memory
    extract_block<RHSBlockProperties, is_internal_block>(
        rhs, tiled_input_block.rhs_scratch_extract.ptr + (db_offset * Properties::TileSizeDimK * LSDR),
        tiled_input_block.rhs_extract_index,
        contraction_tp == contraction_type::local ? thread_properties.nGroupOffset : thread_properties.nGlobalOffset,
        thread_properties.kGroupOffset - thread_properties.kSize);

    sync_thread<contraction_tp == contraction_type::no_local>(itemID);

    // Tiling the Lhs block from global to local memory
    extract_block<LHSBlockProperties, is_internal_block>(
        lhs, tiled_input_block.lhs_scratch_extract.ptr + (db_offset * LSDL * Properties::TileSizeDimK),
        tiled_input_block.lhs_extract_index,
        contraction_tp == contraction_type::local ? thread_properties.mGroupOffset : thread_properties.mGlobalOffset,
        thread_properties.kGroupOffset - thread_properties.kSize);

    // itemID.barrier(cl::sycl::access::fence_space::local_space);
    sync_thread<contraction_tp == contraction_type::local>(itemID);
    // switch to compute mede
    StorageIndex lhs_offset = (db_offset * LSDL * Properties::TileSizeDimK);
    StorageIndex rhs_offset = (db_offset * Properties::TileSizeDimK * LSDR);
    // Loop over the values of a single tile
    for (StorageIndex k = 0; k < Properties::TileSizeDimK; k++) {
      compute_block_per_tile(tiled_input_block.lhs_scratch_ptr_compute + lhs_offset,
                             tiled_input_block.rhs_scratch_ptr_compute + rhs_offset, privateRes);
      lhs_offset += LSDL;
      rhs_offset += LSDR;
    }
    // computing the K index for the next tile
    thread_properties.kSize -= Properties::TileSizeDimK;
    sync_mem(itemID, db_offset);
  }

  // when local memory is available the following compute_panel will be enabled
  template <bool is_internal_block, typename OutPtr>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_panel(const cl::sycl::nd_item<1> &itemID,
                                                           ThreadProperties<StorageIndex> &thread_properties,
                                                           OutPtr out_ptr) const {
    auto tiled_input_block = TiledMemory{thread_properties, scratch.get_pointer()};
    // Allocate register space
    PacketReturnType privateRes[Properties::WorkLoadPerThreadM * Properties::WorkLoadPerThreadN / PacketSize] = {
        PacketReturnType{0}};
    bool db_offset = 0;

    while (thread_properties.kSize >= Properties::TileSizeDimK) {
      compute_tile_per_panel<is_internal_block>(itemID, thread_properties, tiled_input_block, privateRes, db_offset);
    }
    if (thread_properties.kSize > 0) {
      compute_tile_per_panel<false>(itemID, thread_properties, tiled_input_block, privateRes, db_offset);
    }

    // Storing the final results in the output
    store<is_internal_block,
          contraction_tp == contraction_type::local ? static_cast<StorageIndex>(1) : RHSBlockProperties::nc_stride>(
        out_ptr + thread_properties.nGlobalOffset * triple_dim.M, privateRes, thread_properties.mGlobalOffset,
        thread_properties.nGlobalOffset);
  }
  // When local memory is available the following extract_block will be enabled
  template <typename InputBlockProperties, bool is_internal_block, typename Input, typename Local,
            contraction_type contract_tp = contraction_tp>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::enable_if_t<contract_tp == contraction_type::local> extract_block(
      const Input &inpt, Local local_ptr, const std::pair<StorageIndex, StorageIndex> &local_index,
      const StorageIndex &ncOffset, const StorageIndex cOffset) const {
    constexpr StorageIndex TileSizeDimNC =
        InputBlockProperties::is_rhs ? Properties::TileSizeDimN : Properties::TileSizeDimM;
    constexpr StorageIndex LoadPerThread =
        InputBlockProperties::is_rhs ? Properties::LoadPerThreadRhs : Properties::LoadPerThreadLhs;
    constexpr StorageIndex LSD = InputBlockProperties::is_rhs ? LSDR : LSDL;
    static_assert(((LocalOffset % (TileSizeDimNC / InputBlockProperties::nc_stride) == 0) &&
                   (LocalOffset % (Properties::TileSizeDimK / InputBlockProperties::c_stride) == 0)),
                  " LocalOffset must be divisible by stride");
    const StorageIndex &NC = InputBlockProperties::is_rhs ? triple_dim.N : triple_dim.M;
    StorageIndex localThreadNC = local_index.first;
    StorageIndex localThreadC = local_index.second;
    auto chk_bound = [&](const StorageIndex &CIndex, const StorageIndex &NCIndex) EIGEN_DEVICE_FUNC {
      return ((CIndex + InputBlockProperties::c_stride - 1 < triple_dim.K) &&
              (NCIndex + InputBlockProperties::nc_stride - 1 < NC));
    };
    EIGEN_UNROLL_LOOP
    for (StorageIndex lPT = 0; lPT < LoadPerThread / InputBlockProperties::elements_per_access; lPT++) {
      const StorageIndex CIndex = cOffset + (InputBlockProperties::c_stride * localThreadC);
      const StorageIndex NCIndex = ncOffset + (InputBlockProperties::nc_stride * localThreadNC);
      const StorageIndex ld = InputBlockProperties::is_coalesced_layout ? NC : triple_dim.K;
      if (check_boundary<is_internal_block>(chk_bound(CIndex, NCIndex))) {
        auto val =
            read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
                 InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, NCIndex, CIndex, ld);
        write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : LSD), data_source::local_mem>(
            val, local_ptr + (InputBlockProperties::nc_stride * localThreadNC) +
                     (InputBlockProperties::c_stride * localThreadC * LSD));
      } else {
        EIGEN_UNROLL_LOOP
        for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
          const StorageIndex nCInd = NCIndex + (InputBlockProperties::is_coalesced_layout ? i : 0);
          const StorageIndex cInd = CIndex + (InputBlockProperties::is_coalesced_layout ? 0 : i);
          OutScalar val =
              (nCInd < NC && cInd < triple_dim.K)
                  ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
                        inpt, nCInd, cInd, ld)
                  : OutScalar(0);

          write<StorageIndex, (InputBlockProperties::is_coalesced_layout ? 1 : LSD), data_source::local_mem>(
              val, local_ptr + (InputBlockProperties::nc_stride * localThreadNC) +
                       (InputBlockProperties::is_coalesced_layout ? i : 0) +
                       ((InputBlockProperties::c_stride * localThreadC +
                         (InputBlockProperties::is_coalesced_layout ? 0 : i)) *
                        LSD));
        }
      }
      localThreadNC += (InputBlockProperties::is_coalesced_layout)
                           ? LocalOffset % (TileSizeDimNC / InputBlockProperties::nc_stride)
                           : LocalOffset / (Properties::TileSizeDimK / InputBlockProperties::c_stride);
      localThreadC += (InputBlockProperties::is_coalesced_layout)
                          ? LocalOffset / (TileSizeDimNC / InputBlockProperties::nc_stride)
                          : LocalOffset % (Properties::TileSizeDimK / InputBlockProperties::c_stride);
    }
  }
};

#ifndef EIGEN_SYCL_DISABLE_GEMV

template <typename OutScalar, typename OutAccessor, typename VectorMapper, typename TensorMapper, typename StorageIndex,
          typename Properties, StorageIndex KFactor, bool Vectorizable, bool is_lhs_vec, bool IsFinal>
struct GeneralVectorTensor {
  typedef typename Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketReturnType
      PacketReturnType;
  static constexpr int PacketSize =
      Eigen::TensorSycl::internal::Vectorise<OutScalar, Eigen::SyclDevice, Vectorizable>::PacketSize;
  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;

  static constexpr StorageIndex OutScratchOffset =
      KFactor * Properties::LocalThreadSizeC * Properties::LocalThreadSizeNC;

  // Since the access layout for a vector can always be coalesced, when LHS is a vector, we pass false and false to make
  // sure that the !^ is true When RHS is a vector, we pass true and true to make sure that the !^ is true.
  typedef BlockProperties<is_lhs_vec ? false : true, is_lhs_vec ? false : true, Vectorizable, PacketReturnType>
      VecBlockProperties;

  Scratch scratch;
  const VectorMapper vec;
  const TensorMapper mat;
  OutAccessor out_res;
  const StorageIndex nonContractGroupSize;
  const StorageIndex nonContractDim;
  const StorageIndex contractDim;

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE GeneralVectorTensor(Scratch scratch_, const VectorMapper vec_,
                                                            const TensorMapper mat_, OutAccessor out_res_,
                                                            const StorageIndex nonContractGroupSize_,
                                                            const StorageIndex nonContractDim_,
                                                            const StorageIndex contractDim_)
      : scratch(scratch_),
        vec(vec_),
        mat(mat_),
        out_res(out_res_),
        nonContractGroupSize(nonContractGroupSize_),
        nonContractDim(nonContractDim_),
        contractDim(contractDim_) {}

  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void operator()(cl::sycl::nd_item<1> itemID) const {
    auto scratch_ptr = scratch.get_pointer();
    const StorageIndex linearLocalThreadId = itemID.get_local_id(0);
    StorageIndex nonContractId = is_lhs_vec ? linearLocalThreadId / Properties::LocalThreadSizeC
                                            : linearLocalThreadId % Properties::LocalThreadSizeNC;
    StorageIndex contractId = is_lhs_vec ? linearLocalThreadId % Properties::LocalThreadSizeC
                                         : linearLocalThreadId / Properties::LocalThreadSizeNC;
    const StorageIndex cGroupSize = itemID.get_group_range(0) / nonContractGroupSize;
    const StorageIndex nonContractGroupId =
        is_lhs_vec ? itemID.get_group(0) / cGroupSize : itemID.get_group(0) % nonContractGroupSize;
    const StorageIndex contractGroupId =
        is_lhs_vec ? itemID.get_group(0) % cGroupSize : itemID.get_group(0) / nonContractGroupSize;
    auto out_ptr = out_res + (IsFinal ? 0 : contractGroupId * nonContractDim);

    const StorageIndex nonContractGroupOffset = nonContractGroupId * Properties::TileSizeDimNC;
    const StorageIndex contractGroupOffset = contractGroupId * Properties::TileSizeDimC;
    auto outScratchIndex = nonContractId + contractId * Properties::LocalThreadSizeNC;
    const StorageIndex globalNonContractDimOffset = nonContractGroupOffset + nonContractId;
    const StorageIndex globalContractDimOffset = contractGroupOffset + contractId;
    auto local_output = scratch_ptr + OutScratchOffset;
    const bool is_internal = nonContractDim - nonContractGroupOffset >= Properties::TileSizeDimNC &&
                             contractDim - contractGroupOffset >= Properties::TileSizeDimC;
    is_internal
        ? compute_panel<true>(itemID, vec, mat, local_output, out_ptr,
#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
                              scratch_ptr, contractGroupOffset,
#endif
                              nonContractGroupOffset, linearLocalThreadId, contractDim, nonContractDim, contractId,
                              nonContractId, globalContractDimOffset, globalNonContractDimOffset, outScratchIndex)
        : compute_panel<false>(itemID, vec, mat, local_output, out_ptr,
#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
                               scratch_ptr, contractGroupOffset,
#endif
                               nonContractGroupOffset, linearLocalThreadId, contractDim, nonContractDim, contractId,
                               nonContractId, globalContractDimOffset, globalNonContractDimOffset, outScratchIndex);
  }
  template <bool is_internal_block, typename OutPtr>
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void compute_panel(
      const cl::sycl::nd_item<1> &itemID, const VectorMapper &vec, const TensorMapper &mat, OutScalar *local_output,
      OutPtr out_ptr,
#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
      OutScalar *scratch_ptr, const StorageIndex contractGroupOffset,
#endif
      const StorageIndex nonContractGroupOffset, const StorageIndex linearLocalThreadId, StorageIndex contractDim,
      StorageIndex nonContractDim, StorageIndex contractId, StorageIndex nonContractId,
      StorageIndex globalContractDimOffset, StorageIndex globalNonContractDimOffset, StorageIndex outScratchIndex) {
    OutScalar outScalar[Properties::WorkLoadPerThreadNC] = {OutScalar(0)};
    // Reading the vector
#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
    const StorageIndex vectorOffset = contractGroupOffset + linearLocalThreadId;
    extract_block<VecBlockProperties, is_internal_block, KFactor,
                  Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC>(vec, scratch_ptr, linearLocalThreadId,
                                                                                vectorOffset, contractDim);

    itemID.barrier(cl::sycl::access::fence_space::local_space);
    auto in_scratch_ptr = scratch_ptr + contractId;
#endif

    StorageIndex privateOffsetC = 0;
    EIGEN_UNROLL_LOOP
    for (StorageIndex i = 0; i < Properties::WorkLoadPerThreadC; i++) {
      StorageIndex privateOffsetNC = 0;
      bool contract_conds = ((globalContractDimOffset + privateOffsetC) < contractDim);
#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
      auto vecScalar = *in_scratch_ptr;
#else
      auto vecScalar = (check_boundary<is_internal_block>(contract_conds))
                           ? vec(is_lhs_vec ? StorageIndex(0) : globalContractDimOffset + privateOffsetC,
                                 is_lhs_vec ? globalContractDimOffset + privateOffsetC : StorageIndex(0))
                           : OutScalar(0);
#endif
      EIGEN_UNROLL_LOOP
      for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
        auto matScalar = (check_boundary<is_internal_block>(
                             contract_conds && ((globalNonContractDimOffset + privateOffsetNC) < nonContractDim)))
                             ? mat(is_lhs_vec ? globalContractDimOffset + privateOffsetC
                                              : globalNonContractDimOffset + privateOffsetNC,
                                   is_lhs_vec ? globalNonContractDimOffset + privateOffsetNC
                                              : globalContractDimOffset + privateOffsetC)
                             : OutScalar(0);

        outScalar[j] = ::Eigen::internal::pmadd(matScalar, vecScalar, outScalar[j]);
        privateOffsetNC += Properties::LocalThreadSizeNC;
      }
      privateOffsetC += Properties::LocalThreadSizeC;
#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
      in_scratch_ptr += Properties::LocalThreadSizeC;
#endif
    }

    auto out_scratch_ptr = local_output + outScratchIndex;
    // Each block of 16*16 element in shared memory should reduce to 16*1
    EIGEN_UNROLL_LOOP
    for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
      *out_scratch_ptr = outScalar[j];

      out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
    }
    if (is_lhs_vec) {
      nonContractId = linearLocalThreadId % Properties::LocalThreadSizeNC;
      contractId = linearLocalThreadId / Properties::LocalThreadSizeNC;
      outScratchIndex = nonContractId + contractId * Properties::LocalThreadSizeNC;
    }

    out_scratch_ptr = local_output + outScratchIndex;
    EIGEN_UNROLL_LOOP
    for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
      EIGEN_UNROLL_LOOP
      for (StorageIndex offset = Properties::LocalThreadSizeC >> 1; offset > 0; offset >>= 1) {
        itemID.barrier(cl::sycl::access::fence_space::local_space);
        if (contractId < offset) {
          StorageIndex myNeigbourId = (Properties::LocalThreadSizeNC * offset);
          *out_scratch_ptr += out_scratch_ptr[myNeigbourId];
        }
      }
      // moving to next 16 by 16 block
      out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
    }

    if (contractId == 0) {
      out_scratch_ptr = local_output + nonContractId;
      StorageIndex global_final_offset = nonContractGroupOffset + nonContractId;
      out_ptr += global_final_offset;
      EIGEN_UNROLL_LOOP
      for (StorageIndex j = 0; j < Properties::WorkLoadPerThreadNC; j++) {
        if (check_boundary<is_internal_block>(global_final_offset < nonContractDim)) {
          auto res = *out_scratch_ptr;

          *out_ptr = res;
          out_ptr += Properties::LocalThreadSizeNC;
        }
        // moving to next 16 by 16 block to ge the next 16 reduced elements
        out_scratch_ptr += (Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC);
        if (!(is_internal_block)) global_final_offset += Properties::LocalThreadSizeNC;
      }
    }
  }

  template <typename InputBlockProperties, bool is_internal_block, int CFactor, int GroupSize, typename Input,
            typename Local>
  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_block(const Input &inpt, Local *local_ptr,
                                                                  const StorageIndex &linearLocalThreadId,
                                                                  const StorageIndex &cOffset, const StorageIndex &C) {
    local_ptr += InputBlockProperties::c_stride * linearLocalThreadId;
    StorageIndex cIndex = cOffset;
    for (StorageIndex cId = 0; cId < CFactor / InputBlockProperties::c_stride; cId++) {
      if (check_boundary<is_internal_block>(cIndex + InputBlockProperties::c_stride - 1 < C)) {
        auto val = read<InputBlockProperties::packet_load, InputBlockProperties::is_coalesced_layout,
                        InputBlockProperties::is_rhs, typename InputBlockProperties::OutType>(inpt, StorageIndex(0),
                                                                                              cIndex, StorageIndex(1));
        write<StorageIndex, 1, data_source::local_mem>(val, local_ptr);
      } else {
        EIGEN_UNROLL_LOOP
        for (StorageIndex i = 0; i < InputBlockProperties::elements_per_access; i++) {
          OutScalar val =
              (cIndex + i < C)
                  ? read<false, InputBlockProperties::is_coalesced_layout, InputBlockProperties::is_rhs, OutScalar>(
                        inpt, StorageIndex(0), cIndex + i, StorageIndex(1))
                  : OutScalar(0);
          write<StorageIndex, 1, data_source::local_mem>(val, local_ptr + i);
        }
      }
      local_ptr += InputBlockProperties::c_stride * GroupSize;
      cIndex += InputBlockProperties::c_stride * GroupSize;
    }
  }
};
#endif

#ifndef EIGEN_SYCL_DISABLE_SCALAR

template <typename OutScalar, typename LhsScalar, typename RhsScalar, typename OutAccessor, typename LhsMapper,
          typename RhsMapper, typename StorageIndex, bool Vectorizable>
struct GeneralScalarContraction {
  typedef cl::sycl::accessor<OutScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> Scratch;
  Scratch scratch;
  const LhsMapper lhs;
  const RhsMapper rhs;
  OutAccessor out_res;
  const StorageIndex rng;

  EIGEN_DEVICE_FUNC GeneralScalarContraction(Scratch scratch_, const LhsMapper lhs_, const RhsMapper rhs_,
                                             OutAccessor out_res_, const StorageIndex rng_)
      : scratch(scratch_), lhs(lhs_), rhs(rhs_), out_res(out_res_), rng(rng_) {}

  EIGEN_DEVICE_FUNC void operator()(cl::sycl::nd_item<1> itemID) const {
    auto out_ptr = out_res;
    OutScalar *scratch_ptr = scratch.get_pointer();

    StorageIndex globalid = itemID.get_global_id(0);
    StorageIndex localid = itemID.get_local_id(0);
    OutScalar accumulator = OutScalar(0);
    for (StorageIndex i = globalid; i < rng; i += itemID.get_global_range(0)) {
      accumulator = Eigen::internal::pmadd(lhs(0, i), rhs(i, 0), accumulator);
    }
    auto out_scratch_ptr = scratch_ptr + localid;
    *out_scratch_ptr = accumulator;
    for (StorageIndex offset = itemID.get_local_range(0) >> 1; offset > 0; offset >>= 1) {
      itemID.barrier(cl::sycl::access::fence_space::local_space);
      if (localid < offset) {
        *out_scratch_ptr = (accumulator += out_scratch_ptr[offset]);
      }
    }
    if (localid == 0) {
      out_ptr[itemID.get_group(0)] = accumulator;
    }
  }
};
#endif

}  // namespace internal
}  // namespace TensorSycl

template <typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>,
                       Eigen::SyclDevice>
    : public TensorContractionEvaluatorBase<TensorEvaluator<
          const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Eigen::SyclDevice>> {
  static_assert(std::is_same<OutputKernelType, const NoOpOutputKernel>::value,
                "SYCL tensor contraction does not support output kernels.");

  typedef Eigen::SyclDevice Device;

  typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, Device> Self;
  typedef TensorContractionEvaluatorBase<Self> Base;
  typedef TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType> XprType;
  typedef std::remove_const_t<typename XprType::Scalar> Scalar;
  typedef typename XprType::Index StorageIndex;
  typedef typename XprType::CoeffReturnType CoeffReturnType;
  typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
  typedef typename Base::Storage Storage;
  typedef typename Base::EvaluatorPointerType EvaluatorPointerType;
  struct TripleDim {
    const StorageIndex M;
    const StorageIndex N;
    const StorageIndex K;
    TripleDim(const StorageIndex M_, const StorageIndex N_, const StorageIndex K_) : M(M_), N(N_), K(K_) {}
  };
  enum {
    PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
    BlockAccess = false,
  };

  static constexpr int Layout = TensorEvaluator<LeftArgType, Device>::Layout;
  static constexpr int LDims = Base::LDims;
  static constexpr int RDims = Base::RDims;
  static constexpr int ContractDims = Base::ContractDims;

  typedef array<StorageIndex, LDims> left_dim_mapper_t;
  typedef array<StorageIndex, RDims> right_dim_mapper_t;

  typedef array<StorageIndex, ContractDims> contract_t;
  typedef array<StorageIndex, LDims - ContractDims> left_nocontract_t;
  typedef array<StorageIndex, RDims - ContractDims> right_nocontract_t;

  static constexpr int NumDims = LDims + RDims - 2 * ContractDims;

  typedef DSizes<StorageIndex, NumDims> Dimensions;

  typedef TensorEvaluator<typename Base::EvalLeftArgType, Device> LeftEvaluator;
  typedef TensorEvaluator<typename Base::EvalRightArgType, Device> RightEvaluator;
  typedef std::remove_const_t<typename LeftEvaluator::CoeffReturnType> LhsScalar;
  typedef std::remove_const_t<typename RightEvaluator::CoeffReturnType> RhsScalar;

  typedef typename LeftEvaluator::Dimensions LeftDimensions;
  typedef typename RightEvaluator::Dimensions RightDimensions;

  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered>
  struct input_mapper_propertis {
    static constexpr bool is_lhs_matrix = (LDims == 2 && ContractDims == 1) || lhs_inner_dim_contiguous;
    static constexpr bool is_rhs_matrix =
        (RDims == 2 && ContractDims == 1) || (rhs_inner_dim_contiguous && !rhs_inner_dim_reordered);
  };

  TensorEvaluator(const XprType &op, const Device &device) : Base(op, device) {}

  // We need to redefine this method to make nvcc happy
  EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(typename Base::EvaluatorPointerType data) {
    this->m_leftImpl.evalSubExprsIfNeeded(NULL);
    this->m_rightImpl.evalSubExprsIfNeeded(NULL);
    if (!data) {
      this->m_result = this->m_device.get(
          static_cast<Scalar *>(this->m_device.allocate_temp(this->dimensions().TotalSize() * sizeof(Scalar))));
      data = this->m_result;
    }
    evalToSycl(data);
    return (this->m_result != NULL);
  }
  const Eigen::SyclDevice &device() const { return this->m_device; }
  void evalToSycl(typename Base::EvaluatorPointerType buffer) const {
    if (this->m_lhs_inner_dim_contiguous) {
      if (this->m_rhs_inner_dim_contiguous) {
        if (this->m_rhs_inner_dim_reordered) {
          evalTyped<true, true, true, Unaligned>(buffer);
        } else {
          evalTyped<true, true, false, Unaligned>(buffer);
        }
      } else {
        if (this->m_rhs_inner_dim_reordered) {
          evalTyped<true, false, true, Unaligned>(buffer);
        } else {
          evalTyped<true, false, false, Unaligned>(buffer);
        }
      }
    } else {
      if (this->m_rhs_inner_dim_contiguous) {
        if (this->m_rhs_inner_dim_reordered) {
          evalTyped<false, true, true, Unaligned>(buffer);
        } else {
          evalTyped<false, true, false, Unaligned>(buffer);
        }
      } else {
        if (this->m_rhs_inner_dim_reordered) {
          evalTyped<false, false, true, Unaligned>(buffer);
        } else {
          evalTyped<false, false, false, Unaligned>(buffer);
        }
      }
    }
  }

  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
  void evalTyped(typename Base::EvaluatorPointerType buffer) const {
    const auto triple_dim = TripleDim{this->m_i_size, this->m_j_size, this->m_k_size};
    typedef internal::TensorContractionInputMapper<
        LhsScalar, StorageIndex, internal::Lhs, LeftEvaluator, left_nocontract_t, contract_t,
        PacketType<CoeffReturnType, Device>::size, lhs_inner_dim_contiguous, false, Unaligned, MakePointer>
        LhsMapper;

    typedef internal::TensorContractionInputMapper<RhsScalar, StorageIndex, internal::Rhs, RightEvaluator,
                                                   right_nocontract_t, contract_t,
                                                   PacketType<CoeffReturnType, Device>::size, rhs_inner_dim_contiguous,
                                                   rhs_inner_dim_reordered, Unaligned, MakePointer>
        RhsMapper;

    // initialize data mappers
    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
                  this->m_left_contracting_strides, this->m_k_strides);

    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
                  this->m_right_contracting_strides, this->m_k_strides);

#ifndef EIGEN_SYCL_DISABLE_SCALAR
    if (triple_dim.M == 1 && triple_dim.N == 1) {
      launchSC(buffer, lhs, rhs, triple_dim.K);
    } else
#endif
#ifndef EIGEN_SYCL_DISABLE_GEMV
        if (triple_dim.M != 1 && triple_dim.N == 1) {
      LaunchVT<false>(buffer, rhs, lhs, triple_dim.M, triple_dim.K);
    } else if (triple_dim.M == 1 && triple_dim.N != 1) {
      LaunchVT<true>(buffer, lhs, rhs, triple_dim.N, triple_dim.K);
    } else  // This is equivalent of if (m!=1 && n!=1)
#endif
    {
      typedef input_mapper_propertis<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered>
          inpt_mapper_properties;
#ifndef EIGEN_SYCL_DISABLE_SKINNY
      bool skinny = false;
      auto platform_name = this->device().getPlatformName();
      // This is based on empirical calculation for AMD r9-nano and Fiji
      if (platform_name.find("AMD") == 0) {
        skinny = (triple_dim.M < triple_dim.K || triple_dim.N < triple_dim.K) &&
                 ((triple_dim.M < 1024 && triple_dim.N < 1024) ||
                  (uint64_t(triple_dim.M * triple_dim.N) < uint64_t(triple_dim.K)));
      } else {
        skinny = (((std::max(triple_dim.K, triple_dim.N) / std::min(triple_dim.K, triple_dim.N)) > 100) ||
                  ((std::max(triple_dim.K, triple_dim.M) / std::min(triple_dim.K, triple_dim.M)) > 100) ||
                  ((std::max(triple_dim.N, triple_dim.M) / std::min(triple_dim.N, triple_dim.M)) > 100));
      }
      if (skinny)
        adjustTT<true, inpt_mapper_properties>(buffer, lhs, rhs, triple_dim);
      else
#endif  // EIGEN_SYCL_DISABLE_SKINNY
        adjustTT<false, inpt_mapper_properties>(buffer, lhs, rhs, triple_dim);
    }
  }

  template <bool skinny, typename input_mapper_properties, typename LhsMapper, typename RhsMapper>
  void EIGEN_ALWAYS_INLINE adjustTT(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
                                    const TripleDim &triple_dim) const {
#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_ON
    if (device().has_local_memory()) {
      typedef TensorSycl::internal::TTPanelSize<CoeffReturnType, StorageIndex, 4, 4, 16> PanelParameters;
      launchTT<TensorSycl::internal::contraction_type::local, skinny, input_mapper_properties, PanelParameters>(
          buffer, lhs, rhs, triple_dim);
    }
#endif
#ifdef EIGEN_SYCL_LOCAL_MEM_UNSET_OR_OFF
    if (!(device().has_local_memory())) {
      typedef TensorSycl::internal::TTPanelSize<CoeffReturnType, StorageIndex, 4, 4, 4> PanelParameters;
      launchTT<TensorSycl::internal::contraction_type::no_local, skinny, input_mapper_properties, PanelParameters>(
          buffer, lhs, rhs, triple_dim);
    }
#endif
  }

  template <TensorSycl::internal::contraction_type ct, bool skinny, typename input_mapper_properties,
            typename Properties, typename LhsMapper, typename RhsMapper>
  void launchTT(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
                const TripleDim &triple_dim) const {
    const StorageIndex roundUpM = Eigen::TensorSycl::internal::roundUp(triple_dim.M, Properties::TileSizeDimM);
    const StorageIndex roundUpN = Eigen::TensorSycl::internal::roundUp(triple_dim.N, Properties::TileSizeDimN);
    const StorageIndex groupSizeM = roundUpM / Properties::TileSizeDimM;
    const StorageIndex groupSizeN = roundUpN / Properties::TileSizeDimN;

    const StorageIndex roundUpK = Eigen::TensorSycl::internal::roundUp(triple_dim.K, Properties::TileSizeDimK);
    StorageIndex totalTilesK = roundUpK / Properties::TileSizeDimK;
    StorageIndex groupSizeK =
        skinny
            ? std::max(std::min(totalTilesK,
                                (StorageIndex)(device().getPowerOfTwo(device().getNumSyclMultiProcessors(), true) * 4) /
                                    (groupSizeM * groupSizeN)),
                       StorageIndex(1))
            : StorageIndex(1);

    const StorageIndex numTilesPerGroup = Eigen::TensorSycl::internal::roundUp(totalTilesK, groupSizeK) / groupSizeK;

    const StorageIndex totalGroupSize = groupSizeM * groupSizeN * groupSizeK;

    const StorageIndex localRange = Properties::LocalThreadSizeM * Properties::LocalThreadSizeN;
    const StorageIndex globalRange = totalGroupSize * localRange;

    const StorageIndex scratchSize = (ct == TensorSycl::internal::contraction_type::local)
                                         ? ((Properties::DoubleBuffer + 1) *
                                            (Properties::TileSizeDimM + Properties::BC) * (Properties::TileSizeDimK)) +
                                               ((Properties::DoubleBuffer + 1) * (Properties::TileSizeDimK) *
                                                (Properties::TileSizeDimN + Properties::BC))
                                         : StorageIndex(1);

    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
    if (groupSizeK == 1) {
      typedef TensorSycl::internal::TensorContractionKernel<CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType,
                                                            LhsMapper, RhsMapper, StorageIndex, Properties, TripleDim,
                                                            PacketAccess, input_mapper_properties, true, ct>
          ContractKernelName;
      device()
          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
              lhs, rhs, buffer, thread_range, scratchSize, groupSizeM, groupSizeN, numTilesPerGroup, triple_dim)
          .wait();
    } else {
      typedef TensorSycl::internal::TensorContractionKernel<CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType,
                                                            LhsMapper, RhsMapper, StorageIndex, Properties, TripleDim,
                                                            PacketAccess, input_mapper_properties, false, ct>
          ContractKernelName;
      CoeffReturnType *temp_pointer = static_cast<CoeffReturnType *>(
          device().allocate_temp(triple_dim.M * triple_dim.N * groupSizeK * sizeof(CoeffReturnType)));
      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);

      device()
          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
              lhs, rhs, tmp_global_accessor, thread_range, scratchSize, groupSizeM, groupSizeN, numTilesPerGroup,
              triple_dim)
          .wait();

      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
      auto op = Op();
      typedef TensorSycl::internal::SecondStepPartialReduction<CoeffReturnType, StorageIndex, EvaluatorPointerType,
                                                               EvaluatorPointerType, Op>
          ReductionKernel;

      device()
          .template unary_kernel_launcher<CoeffReturnType, ReductionKernel>(
              tmp_global_accessor, buffer,
              cl::sycl::nd_range<1>(cl::sycl::range<1>(StorageIndex(
                                        Eigen::TensorSycl::internal::roundUp(triple_dim.M * triple_dim.N, localRange))),
                                    cl::sycl::range<1>(localRange)),
              StorageIndex(1), op, StorageIndex(triple_dim.M * triple_dim.N), groupSizeK)
          .wait();
      device().deallocate_temp(temp_pointer);
    }
  }

#ifndef EIGEN_SYCL_DISABLE_GEMV
  template <bool is_lhs_vec, typename VectorMapper, typename TensorMapper, typename StorageIndex>
  void EIGEN_ALWAYS_INLINE LaunchVT(EvaluatorPointerType buffer, const VectorMapper &vec, const TensorMapper &mat,
                                    StorageIndex NC, StorageIndex C) const {
    const StorageIndex nonContractDim = NC;
    constexpr StorageIndex NCFactor = 1;
    constexpr StorageIndex CFactor = 1;
    constexpr StorageIndex NCWindow = 16;
    typedef Eigen::TensorSycl::internal::TVPanelSize<CoeffReturnType, StorageIndex, NCWindow, CFactor, NCFactor>
        Properties;
    const StorageIndex roundUpC = Eigen::TensorSycl::internal::roundUp(C, Properties::TileSizeDimC);
    const StorageIndex cNumGroups = roundUpC / (Properties::LocalThreadSizeC * Properties::WorkLoadPerThreadC);
    const StorageIndex roundUpNC = Eigen::TensorSycl::internal::roundUp(nonContractDim, Properties::TileSizeDimNC);
    const StorageIndex nCNumGroups = roundUpNC / (Properties::LocalThreadSizeNC * Properties::WorkLoadPerThreadNC);
    const StorageIndex globalRange =
        (roundUpNC / (Properties::WorkLoadPerThreadNC)) * (roundUpC / (Properties::WorkLoadPerThreadC));
    const StorageIndex localRange = Properties::LocalThreadSizeNC * Properties::LocalThreadSizeC;
    const StorageIndex scratchSize =
        (Properties::WorkLoadPerThreadNC + CFactor) * Properties::LocalThreadSizeC * Properties::LocalThreadSizeNC;
    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(globalRange), cl::sycl::range<1>(localRange));
    if (cNumGroups > 1) {
      typedef Eigen::TensorSycl::internal::GeneralVectorTensor<CoeffReturnType, EvaluatorPointerType, VectorMapper,
                                                               TensorMapper, StorageIndex, Properties, CFactor, false,
                                                               is_lhs_vec, false>
          ContractKernelName;
      CoeffReturnType *temp_pointer =
          static_cast<CoeffReturnType *>(device().allocate_temp(nonContractDim * cNumGroups * sizeof(CoeffReturnType)));
      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);

      device()
          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
              vec, mat, tmp_global_accessor, thread_range, scratchSize, nCNumGroups, nonContractDim, C)
          .wait();

      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
      typedef TensorSycl::internal::SecondStepPartialReduction<CoeffReturnType, StorageIndex, EvaluatorPointerType,
                                                               EvaluatorPointerType, Op>
          ReductionKernel;

      device()
          .template unary_kernel_launcher<CoeffReturnType, ReductionKernel>(
              tmp_global_accessor, buffer,
              cl::sycl::nd_range<1>(
                  cl::sycl::range<1>(Eigen::TensorSycl::internal::roundUp(nonContractDim, localRange)),
                  cl::sycl::range<1>(localRange)),
              StorageIndex(1), Op(), nonContractDim, cNumGroups)
          .wait();
      device().deallocate_temp(temp_pointer);
    } else {
      typedef Eigen::TensorSycl::internal::GeneralVectorTensor<CoeffReturnType, EvaluatorPointerType, VectorMapper,
                                                               TensorMapper, StorageIndex, Properties, CFactor, false,
                                                               is_lhs_vec, true>
          ContractKernelName;
      device()
          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(
              vec, mat, buffer, thread_range, scratchSize, nCNumGroups, nonContractDim, C)
          .wait();
    }
  }
#endif

#ifndef EIGEN_SYCL_DISABLE_SCALAR
  template <typename LhsMapper, typename RhsMapper>
  EIGEN_ALWAYS_INLINE void launchSC(EvaluatorPointerType buffer, const LhsMapper &lhs, const RhsMapper &rhs,
                                    StorageIndex K) const {
    EIGEN_STATIC_ASSERT(!((EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1) &
                          (EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1 - 1)),
                        "The Local thread size must be a power of 2 for the reduction "
                        "operation");
    constexpr StorageIndex local_range = EIGEN_SYCL_LOCAL_THREAD_DIM0 * EIGEN_SYCL_LOCAL_THREAD_DIM1;

    // Here we force the code not to be more than 2-step reduction: Our empirical research shows that if each thread
    // reduces at least 512 elementss individually, we get better performance.
    const StorageIndex num_work_group = ((K + (512 * local_range - 1)) / (512 * local_range) > 1 ? local_range : 1);
    const StorageIndex global_range = num_work_group * local_range;

    typedef Eigen::TensorSycl::internal::GeneralScalarContraction<
        CoeffReturnType, LhsScalar, RhsScalar, EvaluatorPointerType, LhsMapper, RhsMapper, StorageIndex, false>
        ContractKernelName;
    auto thread_range = cl::sycl::nd_range<1>(cl::sycl::range<1>(global_range), cl::sycl::range<1>(local_range));
    if (num_work_group > 1) {
      CoeffReturnType *temp_pointer =
          static_cast<CoeffReturnType *>(device().allocate_temp(num_work_group * sizeof(CoeffReturnType)));
      EvaluatorPointerType tmp_global_accessor = device().get(temp_pointer);
      device()
          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(lhs, rhs, tmp_global_accessor,
                                                                                thread_range, local_range, K)
          .wait();
      typedef Eigen::internal::SumReducer<CoeffReturnType> Op;
      typedef TensorSycl::internal::SecondStepFullReducer<CoeffReturnType, Op, EvaluatorPointerType,
                                                          EvaluatorPointerType, StorageIndex, local_range>
          GenericRKernel;
      device()
          .template unary_kernel_launcher<CoeffReturnType, GenericRKernel>(
              tmp_global_accessor, buffer,
              cl::sycl::nd_range<1>(cl::sycl::range<1>(local_range), cl::sycl::range<1>(local_range)), local_range,
              Op())
          .wait();
      device().deallocate_temp(temp_pointer);
    } else {
      device()
          .template binary_kernel_launcher<CoeffReturnType, ContractKernelName>(lhs, rhs, buffer, thread_range,
                                                                                local_range, K)
          .wait();
    }
  }
#endif

  EIGEN_STRONG_INLINE void cleanup() {
    this->m_leftImpl.cleanup();
    this->m_rightImpl.cleanup();

    if (this->m_result) {
      this->m_device.deallocate_temp(this->m_result);
      this->m_result = NULL;
    }
  }
};
}  // namespace Eigen
#endif  // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H