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 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
  
 #pragma once
  
 #include "ck_tile/host/concat.hpp"
 #include "ck_tile/core.hpp"
 #include "ck_tile/ops/common/utils.hpp"
 #include "ck_tile/ops/gemm/warp/warp_gemm_dispatcher.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
 #include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"
  
 #include <type_traits>
  
 namespace ck_tile {
  
 template <typename AsDataType_,
           typename BsDataType_,
           typename DsDataType_,
           typename AccDataType_,
           typename ODataType_,
           typename DsLayout_,
           typename ELayout_,
           typename CDElementwise_,
           index_t kM_,
           index_t kN_,
           index_t MWave_,
           index_t NWave_,
           index_t MPerXdl_,
           index_t NPerXdl_,
           index_t KPerXdl_,
           bool isCTransposed_,
           memory_operation_enum MemoryOperation_,
           index_t kNumWaveGroups_      = 1,
           bool FixedVectorSize_        = false,
           index_t VectorSizeC_         = 1,
           bool TiledMMAPermuteN_       = false,
           index_t BlockedXDLN_PerWarp_ = 1> // The number of continuous xdl_output per warp
 struct CShuffleEpilogueProblem
 {
     using AsDataType                                       = remove_cvref_t<AsDataType_>;
     using BsDataType                                       = remove_cvref_t<BsDataType_>;
     using AccDataType                                      = remove_cvref_t<AccDataType_>;
     using ODataType                                        = remove_cvref_t<ODataType_>;
     using DsDataType                                       = remove_cvref_t<DsDataType_>;
     using DsLayout                                         = remove_cvref_t<DsLayout_>;
     using ELayout                                          = remove_cvref_t<ELayout_>;
     using CDElementwise                                    = remove_cvref_t<CDElementwise_>;
     static constexpr index_t kBlockSize                    = MWave_ * NWave_ * get_warp_size();
     static constexpr index_t kMPerBlock                    = kM_;
     static constexpr index_t kNPerBlock                    = kN_;
     static constexpr index_t MWave                         = MWave_;
     static constexpr index_t NWave                         = NWave_;
     static constexpr index_t MPerXdl                       = MPerXdl_;
     static constexpr index_t NPerXdl                       = NPerXdl_;
     static constexpr index_t KPerXdl                       = KPerXdl_;
     static constexpr index_t isCTransposed                 = isCTransposed_;
     static constexpr memory_operation_enum MemoryOperation = MemoryOperation_;
     static constexpr bool FixedVectorSize                  = FixedVectorSize_;
     static constexpr index_t VectorSizeC                   = VectorSizeC_;
     static constexpr index_t BlockedXDLN_PerWarp           = BlockedXDLN_PerWarp_;
     static constexpr bool TiledMMAPermuteN                 = TiledMMAPermuteN_;
     static constexpr index_t kNumWaveGroups                = kNumWaveGroups_;
     static constexpr index_t NumDTensor                    = DsDataType::size();
  
     static_assert(NumDTensor == DsLayout::size(),
                   "The size of DsDataType and DsLayout should be the same");
 };
  
 template <typename Problem_, typename Policy_ = void>
 struct CShuffleEpilogue
 {
     using Problem     = remove_cvref_t<Problem_>;
     using AsDataType  = remove_cvref_t<typename Problem::AsDataType>;
     using BsDataType  = remove_cvref_t<typename Problem::BsDataType>;
     using AccDataType = remove_cvref_t<typename Problem::AccDataType>;
     using ODataType   = remove_cvref_t<typename Problem::ODataType>;
     using DsDataType  = remove_cvref_t<typename Problem::DsDataType>;
     using DsLayout    = remove_cvref_t<typename Problem::DsLayout>;
  
     static constexpr bool ADataTypeIsTuple = is_detected<is_tuple, AsDataType>::value;
     static constexpr bool BDataTypeIsTuple = is_detected<is_tuple, BsDataType>::value;
  
     using AsDataTypeTuple = std::conditional_t<ADataTypeIsTuple,
                                                remove_cvref_t<AsDataType>,
                                                remove_cvref_t<tuple<AsDataType>>>;
  
     using BsDataTypeTuple = std::conditional_t<BDataTypeIsTuple,
                                                remove_cvref_t<BsDataType>,
                                                remove_cvref_t<tuple<BsDataType>>>;
  
     using ADataType = remove_cvref_t<std::tuple_element_t<number<0>{}, AsDataTypeTuple>>;
     using BDataType = remove_cvref_t<std::tuple_element_t<number<0>{}, BsDataTypeTuple>>;
  
     using ATypeToUse =
         std::conditional_t<std::is_same_v<ADataType, pk_int4_t>, BDataType, ADataType>;
     // Used for weight-only quantization kernel, B would be dequantized to the same data type as A
     using BTypeToUse =
         std::conditional_t<std::is_same_v<BDataType, pk_int4_t>, ADataType, BDataType>;
     using ELayout       = remove_cvref_t<typename Problem::ELayout>;
     using CDElementwise = remove_cvref_t<typename Problem::CDElementwise>;
     static constexpr memory_operation_enum MemoryOperation = Problem::MemoryOperation;
     static constexpr index_t kBlockSize                    = Problem::kBlockSize;
     static constexpr index_t kMPerBlock                    = Problem::kMPerBlock;
     static constexpr index_t kNPerBlock                    = Problem::kNPerBlock;
     static constexpr index_t MWave                         = Problem::MWave;
     static constexpr index_t NWave                         = Problem::NWave;
     static constexpr index_t MPerXdl                       = Problem::MPerXdl;
     static constexpr index_t NPerXdl                       = Problem::NPerXdl;
     static constexpr index_t KPerXdl                       = Problem::KPerXdl;
     static constexpr index_t isCTransposed                 = Problem::isCTransposed;
     static constexpr bool FixedVectorSize                  = Problem::FixedVectorSize;
     static constexpr bool TiledMMAPermuteN                 = Problem::TiledMMAPermuteN;
     static constexpr index_t BlockedXDLN_PerWarp           = Problem::BlockedXDLN_PerWarp;
     static constexpr index_t VectorSizeC                   = Problem::VectorSizeC;
     static constexpr index_t MPerIteration                 = MPerXdl * MWave;
     static constexpr index_t NPerIteration                 = NPerXdl * NWave;
     static constexpr index_t NumDTensor                    = Problem::NumDTensor;
     static constexpr index_t MRepeat                       = kMPerBlock / (MPerXdl * MWave);
     static constexpr index_t NRepeat                       = kNPerBlock / (NPerXdl * NWave);
  
     CDElementwise elfunc_;
  
     CK_TILE_DEVICE CShuffleEpilogue(CDElementwise elfunc = CDElementwise{}) : elfunc_(elfunc) {};
  
     static_assert(NumDTensor == DsLayout::size(),
                   "The size of DsDataType and DsLayout should be the same");
  
     [[nodiscard]] CK_TILE_HOST static const std::string GetName()
     {
         // clang-format off
         return concat('_', "CShuffleEpilogue", 
                       concat('x', MWave, NWave),
                       concat('x', MPerXdl, NPerXdl, KPerXdl),
                       VectorSizeC,
                       isCTransposed ? "CTransposed" : "CNotTransposed",
                       mem_op_string<MemoryOperation>());
         // clang-format on
     }
  
     CK_TILE_HOST_DEVICE static constexpr index_t GetVectorSizeC()
     {
         if constexpr(FixedVectorSize)
         {
             return VectorSizeC;
         }
         constexpr index_t max_vector_size = 16;
         if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
         {
             return std::min(static_cast<int>(NPerIteration),
                             static_cast<int>(max_vector_size / sizeof(ODataType)));
         }
         else if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::ColumnMajor>)
         {
             return std::min(static_cast<int>(MPerIteration),
                             static_cast<int>(max_vector_size / sizeof(ODataType)));
         }
         else
         {
             static_assert(false, "Unsupported ELayout!");
         }
     }
  
     template <index_t I>
     CK_TILE_HOST_DEVICE static constexpr index_t GetVectorSizeD(number<I> index)
     {
         constexpr index_t max_vector_size = 16;
         using DiDataType = remove_cvref_t<std::tuple_element_t<index.value, DsDataType>>;
         using DiLayout   = remove_cvref_t<std::tuple_element_t<index.value, DsLayout>>;
         if constexpr(std::is_same_v<DiLayout, tensor_layout::gemm::RowMajor>)
         {
             return std::min(static_cast<int>(NPerIteration),
                             static_cast<int>(max_vector_size / sizeof(DiDataType)));
         }
         else if constexpr(std::is_same_v<DiLayout, tensor_layout::gemm::ColumnMajor>)
         {
             return std::min(static_cast<int>(MPerIteration),
                             static_cast<int>(max_vector_size / sizeof(DiDataType)));
         }
         else
         {
             static_assert(false, "Unsupported DLayout!");
         }
         return max_vector_size / sizeof(DiDataType);
     }
     static constexpr auto shuffle_tile_tuple = [] {
         constexpr index_t elem_per_thread = MPerXdl * NPerXdl / get_warp_size();
         if constexpr(elem_per_thread >= GetVectorSizeC())
         {
             return std::make_tuple(1, 1);
         }
         else
         {
             constexpr index_t num_xdl_shuffles = GetVectorSizeC() / elem_per_thread;
             if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
             {
                 static_assert((kMPerBlock % (MPerXdl * MWave) == 0) &&
                                   (kMPerBlock % num_xdl_shuffles == 0),
                               "kMPerBlock must be divisible by MPerXdl*MWave and "
                               "num_xdl_shuffles for CShuffleEpilogue");
                 return std::make_tuple(min(num_xdl_shuffles, kMPerBlock / (MPerXdl * MWave)), 1);
             }
             else
             {
                 static_assert((kNPerBlock % (NPerXdl * NWave) == 0) &&
                                   (kNPerBlock % num_xdl_shuffles == 0),
                               "kNPerBlock must be divisible by NPerXdl*NWave and "
                               "num_xdl_shuffles for CShuffleEpilogue");
                 return std::make_tuple(1, min(num_xdl_shuffles, kNPerBlock / (NPerXdl * NWave)));
             }
         }
     }();
     static constexpr index_t NumMXdlPerWavePerShuffle = std::get<0>(shuffle_tile_tuple);
     static constexpr index_t NumNXdlPerWavePerShuffle =
         max(BlockedXDLN_PerWarp, std::get<1>(shuffle_tile_tuple));
  
     static constexpr auto MNPerIterationShuffle = [] {
         constexpr index_t m_val = MPerXdl * MWave * NumMXdlPerWavePerShuffle;
         constexpr index_t n_val = NPerXdl * NWave * NumNXdlPerWavePerShuffle;
         if constexpr(kMPerBlock % m_val != 0 || kNPerBlock % n_val != 0)
             return std::make_tuple(MPerXdl * MWave, NPerXdl * NWave);
         else
             return std::make_tuple(m_val, n_val);
     }();
     static constexpr index_t MPerIterationShuffle = std::get<0>(MNPerIterationShuffle);
     static constexpr index_t NPerIterationShuffle = std::get<1>(MNPerIterationShuffle);
  
     using WG = WarpGemmDispatcher<ATypeToUse,
                                   BTypeToUse,
                                   AccDataType,
                                   MPerXdl,
                                   NPerXdl,
                                   KPerXdl,
                                   isCTransposed>;
  
     using CWarpDstr         = typename WG::CWarpDstr;
     using CWarpTensor       = typename WG::CWarpTensor;
     using CWarpDstrEncoding = typename WG::CWarpDstrEncoding;
     using SFC               = space_filling_curve<sequence<kMPerBlock, kNPerBlock>,
                                                   sequence<0, 1>,
                                                   sequence<MPerIterationShuffle, NPerIterationShuffle>>;
  
     template <typename Problem>
     CK_TILE_HOST_DEVICE static constexpr auto MakeLdsBlockDescriptor()
     {
         // N is contiguous dimension
         if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>)
         {
             return make_naive_tensor_descriptor(
                 make_tuple(number<MPerIterationShuffle>{}, number<NPerIterationShuffle>{}),
                 make_tuple(number<NPerIterationShuffle>{}, number<1>{}));
         }
         // M is contiguous dimension
         else if constexpr(std::is_same_v<ELayout, tensor_layout::gemm::ColumnMajor>)
         {
             return make_naive_tensor_descriptor(
                 make_tuple(number<MPerIterationShuffle>{}, number<NPerIterationShuffle>{}),
                 make_tuple(number<1>{}, number<MPerIterationShuffle>{}));
         }
         else
         {
             static_assert(false, "Unsupported ELayout!");
         }
     }
  
     CK_TILE_DEVICE static constexpr auto MakeLdsDistributionEncode()
     {
         constexpr auto block_outer_dstr_encoding = [] {
             if constexpr(BlockedXDLN_PerWarp == 1)
             {
                 return tile_distribution_encoding<sequence<>,
                                                   tuple<sequence<NumMXdlPerWavePerShuffle, MWave>,
                                                         sequence<NumNXdlPerWavePerShuffle, NWave>>,
                                                   tuple<sequence<1, 2>>,
                                                   tuple<sequence<1, 1>>,
                                                   sequence<1, 2>,
                                                   sequence<0, 0>>{};
             }
             else
             {
                 constexpr int RakedXDLN_PerWarp = NumNXdlPerWavePerShuffle / BlockedXDLN_PerWarp;
                 // BlockedLayout
                 return tile_distribution_encoding<
                     sequence<>,
                     tuple<sequence<NumMXdlPerWavePerShuffle, MWave>,
                           sequence<RakedXDLN_PerWarp, NWave, BlockedXDLN_PerWarp>>,
                     tuple<sequence<1, 2>>,
                     tuple<sequence<1, 1>>,
                     sequence<1, 2, 2>,
                     sequence<0, 0, 2>>{};
             }
         }();
         constexpr auto block_dstr_encoding = detail::make_embed_tile_distribution_encoding(
             block_outer_dstr_encoding, typename CWarpDstr::DstrEncode{});
  
         return block_dstr_encoding;
     }
  
     CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
     {
         return MPerIterationShuffle * NPerIterationShuffle * sizeof(ODataType);
     }
  
     template <index_t iAccess, typename LdsTile, typename ScaleM, typename ScaleN>
     CK_TILE_DEVICE void
     scale_tile(LdsTile& lds_tile, ScaleM& scale_m_window, ScaleN& scale_n_window)
     {
         // Check if scales are EmptyScale first (no scaling needed)
         if constexpr(std::is_same_v<ScaleM, EmptyScale> && std::is_same_v<ScaleN, EmptyScale>)
         {
             // No scaling needed - this is a no-op
         }
         // Check if scales are scalar AccDataType
         else if constexpr(std::is_same_v<ScaleM, AccDataType> &&
                           std::is_same_v<ScaleN, AccDataType>)
         {
             // Handle scalar scales
             const AccDataType scale_m = scale_m_window;
             const AccDataType scale_n = scale_n_window;
             tile_elementwise_inout([&](auto& element) { element = element * scale_m * scale_n; },
                                    lds_tile);
         }
         // Otherwise, assume they are tile windows that can be loaded
         else
         {
             // Load tiles
             const auto scale_m_tile = load_tile(scale_m_window);
             const auto scale_n_tile = load_tile(scale_n_window);
  
             // Compute element-wise product in-place i.e. lds_tile = lds_tile * scale_m * scale_n
             tile_elementwise_inout(
                 element_wise::MultiDMultiply{}, lds_tile, lds_tile, scale_m_tile, scale_n_tile);
  
             // Move scale windows
             constexpr index_t num_access = SFC::get_num_of_access();
             if constexpr(iAccess != num_access - 1)
             {
                 constexpr auto step = SFC::get_forward_step(number<iAccess>{});
  
                 move_tile_window(scale_m_window, {step.at(number<0>{}), step.at(number<1>{})});
                 move_tile_window(scale_n_window, {step.at(number<0>{}), step.at(number<1>{})});
             }
         }
     }
  
     template <index_t iAccess, typename OAccTile, typename LdsTile>
     CK_TILE_DEVICE void slice_acc_tile(const OAccTile& o_acc_tile, LdsTile& lds_tile)
     {
         constexpr auto idx_y_start = SFC::get_index(number<iAccess>{});
  
         constexpr auto mIter = number<idx_y_start.at(number<0>{}) / (MPerIterationShuffle)>{};
         constexpr auto nIter = number<idx_y_start.at(number<1>{}) / (NPerIterationShuffle)>{};
         constexpr auto c_warp_y_lengths =
             to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
         constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
  
         lds_tile.get_thread_buffer() = o_acc_tile.get_y_sliced_thread_data(
             merge_sequences(
                 sequence<mIter * NumMXdlPerWavePerShuffle, nIter * NumNXdlPerWavePerShuffle>{},
                 c_warp_y_index_zeros),
             merge_sequences(sequence<NumMXdlPerWavePerShuffle, NumNXdlPerWavePerShuffle>{},
                             c_warp_y_lengths));
     }
  
     template <typename LdsTile, typename InLdsWindow>
     CK_TILE_DEVICE void cast_lds_tile(LdsTile& lds_tile, InLdsWindow& in_lds_window)
     {
         const auto c_warptile_in_tensor_casted = cast_tile<ODataType>(lds_tile);
  
         store_tile(in_lds_window, c_warptile_in_tensor_casted);
     }
  
     template <typename DramWindows, typename COutTensor>
     CK_TILE_DEVICE void apply_d_tensors(DramWindows& d_dram_windows, COutTensor& c_out_tensor)
     {
         const auto ds_tensor = generate_tuple(
             [&](auto idx) { return load_tile(d_dram_windows[idx]); }, number<NumDTensor>{});
  
         const auto c_ds_tiles = concat_tuple_of_reference(
             tie(c_out_tensor, c_out_tensor),
             generate_tie([&](auto idx) -> const auto& { return ds_tensor[idx]; },
                          number<NumDTensor>{}));
  
         tile_elementwise_inout_unpack(elfunc_, c_ds_tiles);
     }
  
     template <typename OutDramWindow, typename COutTensor>
     CK_TILE_DEVICE void store_to_dram(OutDramWindow& out_dram_window,
                                       const COutTensor& c_out_tensor)
     {
         if constexpr(MemoryOperation == memory_operation_enum::set)
         {
             store_tile(out_dram_window, c_out_tensor);
         }
         else
         {
             update_tile(out_dram_window, c_out_tensor);
         }
     }
  
     template <index_t iAccess, typename OutDramWindow, typename DDramWindows>
     CK_TILE_DEVICE void move_windows(OutDramWindow& out_dram_window, DDramWindows& d_dram_windows)
     {
         constexpr index_t num_access = SFC::get_num_of_access();
         if constexpr(iAccess != num_access - 1)
         {
             constexpr auto step = SFC::get_forward_step(number<iAccess>{});
  
             // move the output dram window
             move_tile_window(out_dram_window, {step.at(number<0>{}), step.at(number<1>{})});
  
             // move windows for each of the D matrices (inputs for element-wise)
             static_for<0, NumDTensor, 1>{}([&](auto idx) {
                 move_tile_window(d_dram_windows[idx], {step.at(number<0>{}), step.at(number<1>{})});
             });
         }
     }
  
     // TODO: Check if there would be nicer ways to overload rather than with EmptyScale or nullptr_t
     struct EmptyScale
     {
     };
  
     template <typename, typename = void>
     struct ScaleDataType
     {
         using DataType = float;
     };
  
     template <typename T>
     struct ScaleDataType<T, std::void_t<typename T::DataType>>
     {
         using DataType = typename T::DataType;
     };
  
     template <typename ODramWindow,
               typename OAccTile,
               typename DsDramWindows,
               typename ScaleM                         = EmptyScale,
               typename ScaleN                         = EmptyScale,
               int EnablePermuateN_                    = TiledMMAPermuteN,
               std::enable_if_t<EnablePermuateN_, int> = 0>
     CK_TILE_DEVICE auto operator()(ODramWindow& out_dram_window,
                                    const OAccTile& o_acc_tile,
                                    const DsDramWindows& ds_dram_windows,
                                    void* /* p_smem */,
                                    const ScaleM& scale_m = {},
                                    const ScaleN& scale_n = {})
     {
         static constexpr int RowsPerLane = CWarpTensor::get_thread_buffer_size();
  
         static_assert(MPerXdl % RowsPerLane == 0,
                       "CShuffle (permuteN): MPerXdl must be divisible by per-lane row count.");
         constexpr int kM0 = MWave;
         constexpr int kM2 = RowsPerLane;
         constexpr int kM1 = MPerXdl / kM2;
  
         constexpr int kN0 = NWave;
         constexpr int kN1 = NPerXdl;
         constexpr int kN2 = NRepeat;
  
         using IntrThreadShuffleEncode =
             tile_distribution_encoding<sequence<>,
                                        tuple<sequence<kM0, kM1, kM2>, sequence<kN0, kN1, kN2>>,
                                        tuple<sequence<1, 2>, sequence<1, 2>>,
                                        tuple<sequence<0, 0>, sequence<1, 1>>,
                                        sequence<1, 2>,
                                        sequence<2, 2>>;
         constexpr auto dram_tile_distribution =
             make_static_tile_distribution(IntrThreadShuffleEncode{});
  
         auto d_dram_windows = generate_tuple(
             [&](auto idx) {
                 return make_tile_window(ds_dram_windows[idx], dram_tile_distribution);
             },
             number<NumDTensor>{});
  
         constexpr auto c_warp_y_lengths =
             to_sequence(CWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
         constexpr auto c_warp_y_index_zeros = uniform_sequence_gen_t<CWarpDstr::NDimY, 0>{};
  
         auto shuffle_acc  = make_static_distributed_tensor<AccDataType>(dram_tile_distribution);
         auto c_out_tensor = make_static_distributed_tensor<ODataType>(dram_tile_distribution);
  
         // Optional scales (must share the same distribution to match per-thread indexing)
         constexpr bool has_scales =
             !std::is_same<ScaleM, EmptyScale>::value && !std::is_same<ScaleN, EmptyScale>::value;
         constexpr bool has_scalar_scales =
             std::is_same_v<ScaleM, AccDataType> && std::is_same_v<ScaleN, AccDataType>;
  
         // Tiles to hold row/col scales when present
         using SMType = typename ScaleDataType<ScaleM>::DataType;
         using SNType = typename ScaleDataType<ScaleN>::DataType;
  
         auto sm_tile = make_static_distributed_tensor<SMType>(dram_tile_distribution);
         auto sn_tile = make_static_distributed_tensor<SNType>(dram_tile_distribution);
  
         // Build windows only if non-scalar scales are provided
         auto scale_m_window = [&]() {
             if constexpr(has_scales && !has_scalar_scales)
             {
                 return make_tile_window(scale_m, dram_tile_distribution);
             }
             else
             {
                 return EmptyScale{};
             }
         }();
         auto scale_n_window = [&]() {
             if constexpr(has_scales && !has_scalar_scales)
             {
                 return make_tile_window(scale_n, dram_tile_distribution);
             }
             else
             {
                 return EmptyScale{};
             }
         }();
  
         static_for<0, MRepeat, 1>{}([&](auto mIter) {
             // Slice accumulators for this M repeat into the permuted layout
             shuffle_acc.get_thread_buffer() = o_acc_tile.get_y_sliced_thread_data(
                 merge_sequences(sequence<mIter, 0>{}, c_warp_y_index_zeros),
                 merge_sequences(sequence<1, NRepeat>{}, c_warp_y_lengths));
  
             // If non-scalar scales provided, load them with identical distribution
             if constexpr(has_scales && !has_scalar_scales)
             {
                 sm_tile = load_tile(scale_m_window); // row scales in permuted layout
                 sn_tile = load_tile(scale_n_window); // col scales in permuted layout
             }
  
             // Pack 4 “rows per lane” as you already do
             static_for<0, NRepeat, 1>{}([&](auto n_idx) {
                 // source indices in shuffle_acc: (n_idx * product(Y) + row)
                 const index_t plane = c_warp_y_lengths.product();
  
                 // local lambda to fuse scale (if present) and convert
                 static_for<0, kM2, 1>{}([&](auto m_lane) {
                     const int src = n_idx * plane + m_lane;   // source row in this N-plane
                     const int dst = n_idx + m_lane * NRepeat; // permuted N layout in output
                     AccDataType v = shuffle_acc.get_thread_buffer()[src];
  
                     if constexpr(has_scalar_scales)
                     {
                         v = static_cast<AccDataType>(v * scale_m * scale_n);
                     }
                     else if constexpr(has_scales && !has_scalar_scales)
                     {
                         const auto sm = static_cast<float>(sm_tile.get_thread_buffer()[dst]);
                         const auto sn = static_cast<float>(sn_tile.get_thread_buffer()[dst]);
                         v             = static_cast<AccDataType>(v * sm * sn);
                     }
  
                     c_out_tensor.get_thread_buffer()[dst] = type_convert<ODataType>(v);
                 });
             });
  
             // store/update
             if constexpr(MemoryOperation == memory_operation_enum::set)
             {
                 store_tile(out_dram_window, c_out_tensor);
             }
             else
             {
                 update_tile(out_dram_window, c_out_tensor);
             }
  
             // advance output (and any D-tensors) by one MPerXdl*MWave chunk
             move_tile_window(out_dram_window, {number<MPerXdl * MWave>{}, number<0>{}});
             static_for<0, NumDTensor, 1>{}([&](auto idx) {
                 move_tile_window(d_dram_windows[idx], {number<MPerXdl * MWave>{}, number<0>{}});
             });
         });
     }
  
     template <typename ODramWindow,
               typename OAccTile,
               typename DsDramWindows,
               typename ScaleM                          = EmptyScale,
               typename ScaleN                          = EmptyScale,
               int EnablePermuateN_                     = TiledMMAPermuteN,
               std::enable_if_t<!EnablePermuateN_, int> = 0>
     CK_TILE_DEVICE auto operator()(ODramWindow& out_dram_window,
                                    const OAccTile& o_acc_tile,
                                    const DsDramWindows& ds_dram_windows,
                                    void* p_smem,
                                    const ScaleM& scale_m = {},
                                    const ScaleN& scale_n = {})
     {
         constexpr auto LdsTileDistr = make_static_tile_distribution(MakeLdsDistributionEncode());
  
         auto lds_tile = make_static_distributed_tensor<AccDataType>(LdsTileDistr);
  
         constexpr auto lds_block_desc = MakeLdsBlockDescriptor<Problem>();
         auto o_lds_block              = make_tensor_view<address_space_enum::lds>(
             static_cast<ODataType*>(p_smem), lds_block_desc);
  
         auto in_lds_window = make_tile_window(
             o_lds_block,
             make_tuple(number<MPerIterationShuffle>{}, number<NPerIterationShuffle>{}),
             {0, 0},
             LdsTileDistr);
  
         auto out_lds_window = make_tile_window(
             o_lds_block,
             make_tuple(number<MPerIterationShuffle>{}, number<NPerIterationShuffle>{}),
             {0, 0});
  
         constexpr index_t num_access = SFC::get_num_of_access();
  
         static_assert(std::is_same_v<ELayout, tensor_layout::gemm::RowMajor>,
                       "Currently, the CShuffle Epilogue only supports the Row Major Output layout");
  
         using TileEncodingPattern =
             tile_distribution_encoding_pattern_2d<kBlockSize,
                                                   MPerIterationShuffle,
                                                   NPerIterationShuffle,
                                                   GetVectorSizeC(),
                                                   tile_distribution_pattern::thread_raked,
                                                   Problem::kNumWaveGroups>;
         constexpr auto dram_tile_distribution =
             TileEncodingPattern::make_2d_static_tile_distribution();
  
         auto d_dram_windows = generate_tuple(
             [&](auto idx) {
                 return make_tile_window(ds_dram_windows[idx], dram_tile_distribution);
             },
             number<NumDTensor>{});
  
         constexpr bool has_scales =
             !std::is_same_v<ScaleM, EmptyScale> && !std::is_same_v<ScaleN, EmptyScale>;
         constexpr bool has_scalar_scales =
             std::is_same_v<ScaleM, AccDataType> && std::is_same_v<ScaleN, AccDataType>;
         auto scale_m_window = [&]() {
             if constexpr(has_scalar_scales)
             {
                 return scale_m;
             }
             else if constexpr(has_scales)
             {
                 return make_tile_window(scale_m, lds_tile.get_tile_distribution());
             }
             else
             {
                 return EmptyScale{};
             }
         }();
         auto scale_n_window = [&]() {
             if constexpr(has_scalar_scales)
             {
                 return scale_n;
             }
             else if constexpr(has_scales)
             {
                 return make_tile_window(scale_n, lds_tile.get_tile_distribution());
             }
             else
             {
                 return EmptyScale{};
             }
         }();
  
         static_for<0, num_access, 1>{}([&](auto iAccess) {
             block_sync_lds();
             slice_acc_tile<iAccess>(o_acc_tile, lds_tile);
  
             if constexpr(has_scales)
             {
                 scale_tile<iAccess>(lds_tile, scale_m_window, scale_n_window);
             }
  
             cast_lds_tile(lds_tile, in_lds_window);
             block_sync_lds();
  
             auto c_out_tensor = load_tile(make_tile_window(out_lds_window, dram_tile_distribution));
  
             apply_d_tensors(d_dram_windows, c_out_tensor);
             store_to_dram(out_dram_window, c_out_tensor);
             move_windows<iAccess>(out_dram_window, d_dram_windows);
         });
     }
 };
 } // namespace ck_tile