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 // Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
 // SPDX-License-Identifier: MIT
  
 #pragma once
  
 #include <string>
  
 #include "ck_tile/core.hpp"
 #include "ck_tile/core/algorithm/coordinate_transform.hpp"
 #include "ck_tile/core/arch/arch.hpp"
 #include "ck_tile/core/container/tuple.hpp"
 #include "ck_tile/core/numeric/integer.hpp"
 #include "ck_tile/core/numeric/math.hpp"
 #include "ck_tile/host/concat.hpp"
 #include "ck_tile/ops/gemm_quant/pipeline/tile_gemm_quant_traits.hpp"
  
 namespace ck_tile {
  
 namespace detail {
 // Helper templates for safe type extraction
 template <typename, typename Default, typename = void>
 struct get_aq_layout_or
 {
     using type = Default;
 };
  
 template <typename T, typename Default>
 struct get_aq_layout_or<T, Default, std::void_t<typename T::AQLayout>>
 {
     using type = typename T::AQLayout;
 };
  
 template <typename, typename Default, typename = void>
 struct get_bq_layout_or
 {
     using type = Default;
 };
  
 template <typename T, typename Default>
 struct get_bq_layout_or<T, Default, std::void_t<typename T::BQLayout>>
 {
     using type = typename T::BQLayout;
 };
  
 template <typename, typename Default, typename = void>
 struct get_aq_data_type_or
 {
     using type = Default;
 };
  
 template <typename T, typename Default>
 struct get_aq_data_type_or<T, Default, std::void_t<typename T::AQDataType>>
 {
     using type = typename T::AQDataType;
 };
  
 template <typename, typename Default, typename = void>
 struct get_bq_data_type_or
 {
     using type = Default;
 };
  
 template <typename T, typename Default>
 struct get_bq_data_type_or<T, Default, std::void_t<typename T::BQDataType>>
 {
     using type = typename T::BQDataType;
 };
  
 template <typename, typename = void>
 struct is_quantpreshuffle_enabled
 {
     static constexpr bool value = false;
 };
  
 template <typename T>
 struct is_quantpreshuffle_enabled<T, std::void_t<decltype(T::PreshuffleQuant)>>
 {
     static constexpr bool value = T::PreshuffleQuant;
 };
  
 template <typename, typename = void>
 struct is_preshuffleB_enabled
 {
     static constexpr bool value = false;
 };
  
 template <typename T>
 struct is_preshuffleB_enabled<T, std::void_t<decltype(T::PreshuffleB)>>
 {
     static constexpr bool value = T::PreshuffleB;
 };
 } // namespace detail
  
 struct QuantGemmProblem
 {
     CK_TILE_HOST QuantGemmProblem() = default;
     CK_TILE_HOST QuantGemmProblem(index_t M_,
                                   index_t N_,
                                   index_t K_,
                                   index_t QK_A_,
                                   index_t QK_B_,
                                   index_t stride_A_,
                                   index_t stride_B_,
                                   index_t stride_C_,
                                   index_t stride_AQ_,
                                   index_t stride_BQ_)
         : M(M_),
           N(N_),
           K(K_),
           QK_A(QK_A_),
           QK_B(QK_B_),
           stride_A(stride_A_),
           stride_B(stride_B_),
           stride_C(stride_C_),
           stride_AQ(stride_AQ_),
           stride_BQ(stride_BQ_)
     {
     }
  
     index_t M;
     index_t N;
     index_t K;
     index_t QK_A;
     index_t QK_B;
     index_t stride_A;
     index_t stride_B;
     index_t stride_C;
     index_t stride_AQ;
     index_t stride_BQ;
 };
  
 struct QuantGemmHostArgs : public QuantGemmProblem
 {
     CK_TILE_HOST QuantGemmHostArgs() = default;
     CK_TILE_HOST QuantGemmHostArgs(const void* a_ptr_,
                                    const void* b_ptr_,
                                    void* c_ptr_,
                                    const void* aq_ptr_,
                                    const void* bq_ptr_,
                                    index_t k_batch_,
                                    index_t M_,
                                    index_t N_,
                                    index_t K_,
                                    index_t QK_A_,
                                    index_t QK_B_,
                                    index_t stride_A_,
                                    index_t stride_B_,
                                    index_t stride_C_,
                                    index_t stride_AQ_,
                                    index_t stride_BQ_)
         : QuantGemmProblem(
               M_, N_, K_, QK_A_, QK_B_, stride_A_, stride_B_, stride_C_, stride_AQ_, stride_BQ_),
           a_ptr(a_ptr_),
           b_ptr(b_ptr_),
           aq_ptr(aq_ptr_),
           bq_ptr(bq_ptr_),
           c_ptr(c_ptr_),
           k_batch(k_batch_)
     {
     }
  
     const void* a_ptr  = nullptr;
     const void* b_ptr  = nullptr;
     const void* aq_ptr = nullptr;
     const void* bq_ptr = nullptr;
     void* c_ptr        = nullptr;
     index_t k_batch    = 0;
 };
  
 struct QuantGemmKernelArgs
 {
     const void* a_ptr;
     const void* b_ptr;
     const void* aq_ptr;
     const void* bq_ptr;
     void* c_ptr;
     index_t M;
     index_t N;
     index_t K;
     index_t QK_A;
     index_t QK_B;
     index_t stride_A;
     index_t stride_B;
     index_t stride_C;
     index_t stride_AQ;
     index_t stride_BQ;
     index_t k_batch;
 };
  
 template <typename TilePartitioner_,
           typename GemmPipeline_,
           typename EpiloguePipeline_,
           QuantType QuantType_>
 struct QuantGemmKernel
 {
     using TilePartitioner  = remove_cvref_t<TilePartitioner_>;
     using GemmPipeline     = remove_cvref_t<GemmPipeline_>;
     using EpiloguePipeline = remove_cvref_t<EpiloguePipeline_>;
     using ALayout          = remove_cvref_t<typename GemmPipeline::ALayout>;
     using BLayout          = remove_cvref_t<typename GemmPipeline::BLayout>;
     using CLayout          = remove_cvref_t<typename GemmPipeline::CLayout>;
  
     using AQLayout = remove_cvref_t<
         typename detail::get_aq_layout_or<GemmPipeline, typename GemmPipeline::ALayout>::type>;
     using BQLayout = remove_cvref_t<
         typename detail::get_bq_layout_or<GemmPipeline, typename GemmPipeline::BLayout>::type>;
  
     static constexpr index_t kBlockSize = GemmPipeline::BlockSize;
     static constexpr bool PreshuffleQuant =
         detail::is_quantpreshuffle_enabled<GemmPipeline_>::value;
     static constexpr bool PreshuffleB = detail::is_preshuffleB_enabled<GemmPipeline_>::value;
  
     using ADataType   = remove_cvref_t<typename GemmPipeline::ADataType>;
     using BDataType   = remove_cvref_t<typename GemmPipeline::BDataType>;
     using CDataType   = remove_cvref_t<typename EpiloguePipeline::ODataType>;
     using AccDataType = remove_cvref_t<typename EpiloguePipeline::AccDataType>;
  
     using AQDataType =
         remove_cvref_t<typename detail::get_aq_data_type_or<GemmPipeline, AccDataType>::type>;
     using BQDataType =
         remove_cvref_t<typename detail::get_bq_data_type_or<GemmPipeline, AccDataType>::type>;
  
     static constexpr auto I0 = number<0>(); // A Tensor
     static constexpr auto I1 = number<1>(); // AQ Tensor
     static constexpr auto I2 = number<2>(); // B Tensor
     static constexpr auto I3 = number<3>(); // BQ Tensor
     static constexpr auto I4 = number<4>(); // C Tensor
  
     static constexpr auto kQuantType = QuantType_;
  
     [[nodiscard]] CK_TILE_HOST static const std::string GetName()
     {
         // clang-format off
         return concat('_', "gemm_quant", gemm_prec_str<ADataType, BDataType>, GemmPipeline::GetName());
         // clang-format on
     }
  
     CK_TILE_HOST static constexpr auto GridSize(index_t M, index_t N, index_t KBatch)
     {
         return dim3(TilePartitioner::GridSize(M, N), 1, KBatch);
     }
  
     CK_TILE_HOST static auto BlockSize()
     {
         return is_wave32() ? dim3(kBlockSize / 2) : dim3(kBlockSize);
     }
  
     CK_TILE_HOST static constexpr QuantGemmKernelArgs
     MakeKernelArgs(const QuantGemmHostArgs& hostArgs)
     {
         return QuantGemmKernelArgs{hostArgs.a_ptr,
                                    hostArgs.b_ptr,
                                    hostArgs.aq_ptr,
                                    hostArgs.bq_ptr,
                                    hostArgs.c_ptr,
                                    hostArgs.M,
                                    hostArgs.N,
                                    hostArgs.K,
                                    hostArgs.QK_A,
                                    hostArgs.QK_B,
                                    hostArgs.stride_A,
                                    hostArgs.stride_B,
                                    hostArgs.stride_C,
                                    hostArgs.stride_AQ,
                                    hostArgs.stride_BQ,
                                    hostArgs.k_batch};
     }
  
     CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
     {
         return max(GemmPipeline::GetSmemSize(), EpiloguePipeline::GetSmemSize());
     }
  
     private:
     CK_TILE_DEVICE static constexpr index_t get_padding_size(index_t length, index_t alignment)
     {
         return ck_tile::integer_least_multiple(length, alignment) - length;
     };
     // ===================================================================
     // Helper: Create Pre-shuffled Quantization Tensor Descriptor
     // ===================================================================
     template <index_t KPerBlockBQ,
               index_t NPerBlockBQ,
               index_t NPerBlock,
               index_t WarpTileN,
               index_t GetVectorSizeBQ,
               typename BQDataType_>
     CK_TILE_DEVICE static auto
     MakePreshuffledQuantTensorView(const BQDataType_* bq_ptr, index_t N, index_t QN_B, index_t QK_B)
     {
         // Step 1: Calculate base BQ tensor dimensions
         // ----------------------------------------------------------
         // bq_x: Number of quantization groups in N dimension
         //       = N * KPerBlockBQ, where KPerBlockBQ is the number of
         //       K-dimension groups per block
         // bq_y: Number of quantization groups in K dimension
         //       = Total K groups (QK_B) / groups per block
         const auto bq_x = N * KPerBlockBQ;
         const auto bq_y = QK_B / KPerBlockBQ;
  
         const auto bq_desc = make_naive_tensor_descriptor(
             make_tuple(bq_y, bq_x), make_tuple(bq_x, 1), number<GetVectorSizeBQ>{}, number<1>{});
  
         // Step 2: First padding transformation (block-level alignment)
         // ----------------------------------------------------------
         // Pad the X dimension to be a multiple of block_tile_size to ensure
         // each thread block can process complete tiles without edge cases
         const auto block_tile_size = NPerBlockBQ * KPerBlockBQ;
  
         const auto bq_pad0_desc = transform_tensor_descriptor(
             bq_desc,
             make_tuple(make_pass_through_transform(bq_y),
                        make_right_pad_transform(bq_x, get_padding_size(bq_x, block_tile_size))),
             make_tuple(sequence<0>{}, sequence<1>{}),
             make_tuple(sequence<0>{}, sequence<1>{}));
  
         // Step 3: Unmerge transformation (wave-level decomposition)
         // ----------------------------------------------------------
         // Split the X dimension into [wave_tile_count_x, wave_tile_size]
         // This separates the work into tiles that can be processed by
         // individual warps/waves
         const auto pad_bq_x          = bq_pad0_desc.get_lengths()[I1];
         const auto wave_tile_size    = ((QN_B <= WarpTileN) ? (WarpTileN / QN_B) : 1) * KPerBlockBQ;
         const auto wave_tile_count_x = ck_tile::integer_divide_ceil(pad_bq_x, wave_tile_size);
  
         const auto bq_unmerge_pad0_desc = transform_tensor_descriptor(
             bq_pad0_desc,
             make_tuple(make_pass_through_transform(bq_y),
                        make_unmerge_transform(make_tuple(wave_tile_count_x, wave_tile_size))),
             make_tuple(sequence<0>{}, sequence<1>{}),
             make_tuple(sequence<0>{}, sequence<1, 2>{}));
  
         // Step 4: Second padding transformation (warp-level alignment)
         // ----------------------------------------------------------
         // Pad wave_tile_size to be a multiple of warp_size (typically 32 or 64)
         // This ensures coalesced memory accesses within each warp
         const auto bq_pad1_desc = transform_tensor_descriptor(
             bq_unmerge_pad0_desc,
             make_tuple(make_pass_through_transform(bq_y),
                        make_pass_through_transform(wave_tile_count_x),
                        make_right_pad_transform(wave_tile_size,
                                                 get_padding_size(wave_tile_size, get_warp_size()))),
             make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
             make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}));
  
         // Step 5: Final merge transformation (prepare for indexing)
         // ----------------------------------------------------------
         // Merge [bq_y, wave_tile_count_x] into a single outer dimension
         // This creates a 2D layout: [merged_outer_dim, pad_wave_size]
         // where merged_outer_dim = bq_y * wave_tile_count_x
         // This layout facilitates efficient block-to-data mapping
         const auto pad_wave_size = ck_tile::integer_least_multiple(wave_tile_size, get_warp_size());
         const auto bq_merge_pad1_desc = transform_tensor_descriptor(
             bq_pad1_desc,
             make_tuple(make_merge_transform(make_tuple(bq_y, wave_tile_count_x)),
                        make_pass_through_transform(pad_wave_size)),
             make_tuple(sequence<0, 1>{}, sequence<2>{}),
             make_tuple(sequence<0>{}, sequence<1>{}));
  
         return make_tensor_view<address_space_enum::global>(bq_ptr, bq_merge_pad1_desc);
     }
  
     public:
     struct SplitKBatchOffset
     {
         __device__ SplitKBatchOffset(const QuantGemmKernelArgs& kargs,
                                      const std::size_t k_id = blockIdx.z)
         {
             constexpr auto K1   = GemmPipeline::BlockGemmShape::WarpTile::at(I2);
             const index_t K_t   = amd_wave_read_first_lane(kargs.k_batch * K1);
             const index_t KRead = amd_wave_read_first_lane((kargs.K + K_t - 1) / K_t * K1);
  
             if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
             {
                 a_k_split_offset = amd_wave_read_first_lane(k_id * KRead);
             }
             else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
             {
                 a_k_split_offset = amd_wave_read_first_lane(k_id * KRead * kargs.stride_A);
             }
  
             if constexpr(std::is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
             {
                 b_k_split_offset = amd_wave_read_first_lane(k_id * KRead * kargs.stride_B);
             }
             else if constexpr(std::is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
             {
                 b_k_split_offset = amd_wave_read_first_lane(k_id * KRead);
             }
  
             if(k_id < static_cast<uint32_t>(kargs.k_batch - 1))
             {
                 splitted_k = amd_wave_read_first_lane(KRead);
             }
             else
             {
                 splitted_k = amd_wave_read_first_lane(kargs.K - KRead * (kargs.k_batch - 1));
             }
         }
  
         index_t a_k_split_offset;
         index_t b_k_split_offset;
         index_t splitted_k;
     };
  
     CK_TILE_DEVICE static auto MakeABlockWindow(const ADataType* a_ptr,
                                                 const QuantGemmKernelArgs& kargs,
                                                 const index_t k_size,
                                                 const index_t i_m)
     {
         // Step 1: Create tensor view for A
         const auto& a_tensor_view = [&]() {
             if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
             {
                 return make_naive_tensor_view<address_space_enum::global>(
                     a_ptr,
                     make_tuple(kargs.M, k_size),
                     make_tuple(kargs.stride_A, 1),
                     number<GemmPipeline::GetVectorSizeA()>{},
                     number<1>{});
             }
             else
             {
                 return make_naive_tensor_view<address_space_enum::global>(
                     a_ptr,
                     make_tuple(k_size, kargs.M),
                     make_tuple(kargs.stride_A, 1),
                     number<GemmPipeline::GetVectorSizeA()>{},
                     number<1>{});
             }
         }();
  
         // Step 2: Create padded view
         const auto& a_pad_view = [&]() {
             if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
             {
                 return pad_tensor_view(a_tensor_view,
                                        make_tuple(number<TilePartitioner::MPerBlock>{},
                                                   number<TilePartitioner::KPerBlock>{}),
                                        sequence<false, GemmPipeline::kPadK>{});
             }
             else
             {
                 return pad_tensor_view(a_tensor_view,
                                        make_tuple(number<TilePartitioner::KPerBlock>{},
                                                   number<TilePartitioner::MPerBlock>{}),
                                        sequence<false, GemmPipeline::kPadM>{});
             }
         }();
  
         // Step 3: Create tile window
         const auto& a_block_window = [&]() {
             if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
             {
                 return make_tile_window(a_pad_view,
                                         make_tuple(number<TilePartitioner::MPerBlock>{},
                                                    number<TilePartitioner::KPerBlock>{}),
                                         {i_m, 0});
             }
             else
             {
                 return make_tile_window(a_pad_view,
                                         make_tuple(number<TilePartitioner::KPerBlock>{},
                                                    number<TilePartitioner::MPerBlock>{}),
                                         {0, i_m});
             }
         }();
  
         return a_block_window;
     }
  
     CK_TILE_DEVICE static auto MakeAQBlockWindow(const AQDataType* aq_ptr,
                                                  const QuantGemmKernelArgs& kargs,
                                                  const index_t i_m,
                                                  const index_t i_n)
     {
         // Step 1: Create tensor view for AQ
         const auto& aq_tensor_view = [&]() {
             if constexpr(kQuantType == QuantType::AQuantGrouped && PreshuffleQuant)
             {
                 static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
                 const auto aq_x = kargs.M * GemmPipeline::KPerBlockAQ;
                 const auto aq_y = kargs.QK_A / GemmPipeline::KPerBlockAQ;
                 const auto aq_desc =
                     make_naive_tensor_descriptor(make_tuple(aq_y, aq_x),
                                                  make_tuple(aq_x, 1),
                                                  number<GemmPipeline::GetVectorSizeAQ()>{},
                                                  number<1>{});
  
                 const auto block_tile_size = GemmPipeline::MPerBlock * GemmPipeline::KPerBlockAQ;
                 const auto aq_pad0_desc    = transform_tensor_descriptor(
                     aq_desc,
                     make_tuple(
                         make_pass_through_transform(aq_y),
                         make_right_pad_transform(aq_x, get_padding_size(aq_x, block_tile_size))),
                     make_tuple(sequence<0>{}, sequence<1>{}),
                     make_tuple(sequence<0>{}, sequence<1>{}));
  
                 const auto pad_aq_x = aq_pad0_desc.get_lengths()[I1];
                 const auto wave_tile_size =
                     GemmPipeline::BlockGemmShape::WarpTile::at(I0) * GemmPipeline::KPerBlockAQ;
                 const auto wave_tile_count_x =
                     ck_tile::integer_divide_ceil(pad_aq_x, wave_tile_size);
  
                 const auto aq_unmerge_pad0_desc = transform_tensor_descriptor(
                     aq_pad0_desc,
                     make_tuple(
                         make_pass_through_transform(aq_y),
                         make_unmerge_transform(make_tuple(wave_tile_count_x, wave_tile_size))),
                     make_tuple(sequence<0>{}, sequence<1>{}),
                     make_tuple(sequence<0>{}, sequence<1, 2>{}));
  
                 const auto aq_pad1_desc = transform_tensor_descriptor(
                     aq_unmerge_pad0_desc,
                     make_tuple(
                         make_pass_through_transform(aq_y),
                         make_pass_through_transform(wave_tile_count_x),
                         make_right_pad_transform(
                             wave_tile_size, get_padding_size(wave_tile_size, get_warp_size()))),
                     make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}),
                     make_tuple(sequence<0>{}, sequence<1>{}, sequence<2>{}));
  
                 const auto pad_wave_size =
                     ck_tile::integer_least_multiple(wave_tile_size, get_warp_size());
                 const auto aq_merge_pad1_desc = transform_tensor_descriptor(
                     aq_pad1_desc,
                     make_tuple(make_merge_transform(make_tuple(aq_y, wave_tile_count_x)),
                                make_pass_through_transform(pad_wave_size)),
                     make_tuple(sequence<0, 1>{}, sequence<2>{}),
                     make_tuple(sequence<0>{}, sequence<1>{}));
  
                 return make_tensor_view<address_space_enum::global>(aq_ptr, aq_merge_pad1_desc);
             }
             else if constexpr((kQuantType == QuantType::AQuantGrouped ||
                                kQuantType == QuantType::ABQuantGrouped) &&
                               !PreshuffleQuant)
             {
                 if constexpr(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>)
                 {
                     return make_naive_tensor_view<address_space_enum::global>(
                         aq_ptr,
                         make_tuple(kargs.M, kargs.QK_A),
                         make_tuple(kargs.stride_AQ, 1),
                         number<GemmPipeline::GetVectorSizeAQ()>{},
                         number<1>{});
                 }
                 else // Column major AQ
                 {
                     return make_naive_tensor_view<address_space_enum::global>(
                         aq_ptr,
                         make_tuple(kargs.QK_A, kargs.M),
                         make_tuple(kargs.stride_AQ, 1),
                         number<GemmPipeline::GetVectorSizeAQ()>{},
                         number<1>{});
                 }
             }
             else if constexpr(kQuantType == QuantType::RowColQuant)
             {
                 return make_naive_tensor_view<address_space_enum::global>(
                     aq_ptr,
                     make_tuple(kargs.M, kargs.N),
                     make_tuple(1, 0), // broadcasting over n
                     number<1>{},
                     number<1>{});
             }
             else
             {
                 return nullptr;
             }
         }();
  
         // Step 2: Create tile window (no padding for AQ)
         const auto& aq_block_window = [&]() {
             if constexpr(kQuantType == QuantType::AQuantGrouped && PreshuffleQuant)
             {
                 static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
                 using QuantGroupSize   = remove_cvref_t<typename GemmPipeline::QuantGroupSize>;
                 constexpr auto block_m = TilePartitioner::MPerBlock;
                 constexpr auto warp_m  = GemmPipeline::BlockGemmShape::WarpTile::at(I0);
                 constexpr auto aqk_per_block = TilePartitioner::KPerBlock / QuantGroupSize::kK;
                 constexpr auto tile_window_width =
                     ck_tile::integer_least_multiple(warp_m * aqk_per_block, get_warp_size());
                 constexpr auto tile_window_height = block_m / warp_m;
                 auto block_m_idx                  = i_m / block_m;
                 return make_tile_window(
                     aq_tensor_view,
                     make_tuple(number<tile_window_height>{}, number<tile_window_width>{}),
                     {block_m_idx * tile_window_height, 0});
             }
             else if constexpr(kQuantType == QuantType::AQuantGrouped && !PreshuffleQuant)
             {
                 using QuantGroupSize = remove_cvref_t<typename GemmPipeline::QuantGroupSize>;
                 constexpr auto aqk_per_block = TilePartitioner::KPerBlock / QuantGroupSize::kK;
                 constexpr auto block_m       = TilePartitioner::MPerBlock;
                 if constexpr(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>)
                 {
                     return make_tile_window(aq_tensor_view,
                                             make_tuple(number<block_m>{}, number<aqk_per_block>{}),
                                             {i_m, 0});
                 }
                 else // Column major AQ
                 {
                     return make_tile_window(aq_tensor_view,
                                             make_tuple(number<aqk_per_block>{}, number<block_m>{}),
                                             {0, i_m});
                 }
             }
             else if constexpr(kQuantType == QuantType::ABQuantGrouped && !PreshuffleQuant)
             {
                 static_assert(std::is_same_v<AQLayout, tensor_layout::gemm::RowMajor>);
                 using QuantGroupSize   = remove_cvref_t<typename GemmPipeline::AQuantGroupSize>;
                 constexpr auto block_m = TilePartitioner::MPerBlock;
                 constexpr auto block_k = TilePartitioner::KPerBlock;
                 return make_tile_window(
                     aq_tensor_view,
                     make_tuple(number<block_m>{}, number<block_k / QuantGroupSize::kK>{}),
                     {i_m, 0});
             }
             else if constexpr(kQuantType == QuantType::RowColQuant)
             {
                 return make_tile_window(aq_tensor_view,
                                         make_tuple(number<TilePartitioner::MPerBlock>{},
                                                    number<TilePartitioner::NPerBlock>{}),
                                         {i_m, i_n});
             }
             else
             {
                 return nullptr;
             }
         }();
  
         return aq_block_window;
     }
  
     CK_TILE_DEVICE static auto MakeBBlockWindow(const BDataType* b_ptr,
                                                 const QuantGemmKernelArgs& kargs,
                                                 const index_t k_size,
                                                 const index_t i_n)
     {
         // Step 1: Create tensor view for B
         const auto& b_tensor_view = [&]() {
             if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
             {
                 if constexpr(GemmPipeline::BlockGemmShape::PermuteB)
                 {
                     constexpr index_t K1          = GemmPipeline::GetSmemPackB();
                     const index_t K0              = k_size / K1;
                     constexpr index_t VectorSizeB = std::min(K1, GemmPipeline::GetVectorSizeB());
                     const auto b_k0_n_k1_desc =
                         make_naive_tensor_descriptor(make_tuple(K0, kargs.N, K1),
                                                      make_tuple(kargs.N * K1, K1, I1),
                                                      number<VectorSizeB>{},
                                                      number<1>{});
                     const auto b_n_k_desc = transform_tensor_descriptor(
                         b_k0_n_k1_desc,
                         make_tuple(make_merge_transform(make_tuple(K0, K1)),
                                    make_pass_through_transform(kargs.N)),
                         make_tuple(sequence<0, 2>{}, sequence<1>{}),
                         make_tuple(sequence<0>{}, sequence<1>{}));
                     return make_tensor_view<address_space_enum::global>(b_ptr, b_n_k_desc);
                 }
                 else
                 {
                     return make_naive_tensor_view<address_space_enum::global>(
                         b_ptr,
                         make_tuple(k_size, kargs.N),
                         make_tuple(kargs.stride_B, 1),
                         number<GemmPipeline::GetVectorSizeB()>{},
                         number<1>{});
                 }
             }
             else
             {
                 if constexpr(GemmPipeline::BlockGemmShape::PermuteB)
                 {
                     constexpr index_t K1          = GemmPipeline::GetSmemPackB();
                     const index_t K0              = k_size / K1;
                     constexpr index_t VectorSizeB = std::min(K1, GemmPipeline::GetVectorSizeB());
                     const auto b_k0_n_k1_desc =
                         make_naive_tensor_descriptor(make_tuple(K0, kargs.N, K1),
                                                      make_tuple(kargs.N * K1, K1, I1),
                                                      number<VectorSizeB>{},
                                                      number<1>{});
                     const auto b_n_k_desc = transform_tensor_descriptor(
                         b_k0_n_k1_desc,
                         make_tuple(make_merge_transform(make_tuple(K0, K1)),
                                    make_pass_through_transform(kargs.N)),
                         make_tuple(sequence<0, 2>{}, sequence<1>{}),
                         make_tuple(sequence<1>{}, sequence<0>{}));
                     return make_tensor_view<address_space_enum::global>(b_ptr, b_n_k_desc);
                 }
                 else
                 {
                     if constexpr(PreshuffleB)
                     {
                         index_t kFlatK =
                             GemmPipeline::flatKPerWarp *
                             (k_size / GemmPipeline::BlockGemmShape::WarpTile::at(number<2>{}));
                         index_t kFlatN = kargs.N * kargs.K / kFlatK;
                         return make_naive_tensor_view<address_space_enum::global>(
                             b_ptr,
                             make_tuple(kFlatN, kFlatK),
                             make_tuple(kFlatK, 1),
                             number<GemmPipeline::GetVectorSizeB()>{},
                             number<1>{});
                     }
                     else
                     {
                         if constexpr(std::is_same_v<BDataType, pk_fp4_raw_t>)
                             return make_naive_tensor_view<address_space_enum::global>(
                                 b_ptr,
                                 make_tuple(kargs.N, k_size / 2),
                                 make_tuple(kargs.stride_B, 1),
                                 number<GemmPipeline::GetVectorSizeB()>{},
                                 number<1>{});
                         else
                             return make_naive_tensor_view<address_space_enum::global>(
                                 b_ptr,
                                 make_tuple(kargs.N, k_size),
                                 make_tuple(kargs.stride_B, 1),
                                 number<GemmPipeline::GetVectorSizeB()>{},
                                 number<1>{});
                     }
                 }
             }
         }();
  
         // Step 2: Create padded view (or flat view for PreshuffleB)
         const auto& b_pad_view = [&]() {
             if constexpr(PreshuffleB)
             {
                 return b_tensor_view; // no padding for preshuffle
             }
             else if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
             {
                 if constexpr(std::is_same_v<BDataType, pk_fp4_raw_t>)
                     return pad_tensor_view(b_tensor_view,
                                            make_tuple(number<TilePartitioner::NPerBlock>{},
                                                       number<TilePartitioner::KPerBlock / 2>{}),
                                            sequence<false, GemmPipeline::kPadK>{});
                 else
                     return pad_tensor_view(b_tensor_view,
                                            make_tuple(number<TilePartitioner::NPerBlock>{},
                                                       number<TilePartitioner::KPerBlock>{}),
                                            sequence<false, GemmPipeline::kPadK>{});
             }
             else
             {
                 return pad_tensor_view(b_tensor_view,
                                        make_tuple(number<TilePartitioner::KPerBlock>{},
                                                   number<TilePartitioner::NPerBlock>{}),
                                        sequence<false, GemmPipeline::kPadN>{});
             }
         }();
  
         // Step 3: Create tile window
         const auto& b_block_window = [&]() {
             if constexpr(PreshuffleB)
             {
                 return make_tile_window(
                     b_pad_view,
                     make_tuple(number<GemmPipeline::flatNPerWarp>{},
                                number<GemmPipeline::flatKPerWarp>{}),
                     {static_cast<int>(i_n / GemmPipeline::BlockGemmShape::WarpTile::at(I1)), 0});
             }
             else
             {
                 if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::ColumnMajor>)
                 {
                     if constexpr(std::is_same_v<BDataType, pk_fp4_raw_t>)
                         return make_tile_window(
                             b_pad_view,
                             make_tuple(number<TilePartitioner::NPerBlock>{},
                                        number<TilePartitioner::KPerBlock / 2>{}),
                             {i_n, 0});
                     else
                         return make_tile_window(b_pad_view,
                                                 make_tuple(number<TilePartitioner::NPerBlock>{},
                                                            number<TilePartitioner::KPerBlock>{}),
                                                 {i_n, 0});
                 }
                 else
                 {
                     return make_tile_window(b_pad_view,
                                             make_tuple(number<TilePartitioner::KPerBlock>{},
                                                        number<TilePartitioner::NPerBlock>{}),
                                             {0, i_n});
                 }
             }
         }();
  
         return b_block_window;
     }
  
     CK_TILE_DEVICE static auto MakeBQBlockWindow(const BQDataType* bq_ptr,
                                                  const QuantGemmKernelArgs& kargs,
                                                  const index_t i_m,
                                                  const index_t i_n)
     {
         // Step 1: Create tensor view for BQ
         const auto& bq_tensor_view = [&]() {
             if constexpr(kQuantType == QuantType::RowColQuant)
             {
                 return make_naive_tensor_view<address_space_enum::global>(
                     bq_ptr,
                     make_tuple(kargs.M, kargs.N),
                     make_tuple(0, 1), // broadcasting over m
                     number<1>{},
                     number<1>{});
             }
             else if constexpr(kQuantType == QuantType::BQuantGrouped)
             {
                 if constexpr(PreshuffleQuant)
                 {
                     static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>,
                                   "PreshuffleQuant with BQuantGrouped currently only supports "
                                   "ColumnMajor BQ layout");
                     using QuantGroupSize = remove_cvref_t<typename GemmPipeline::QuantGroupSize>;
  
                     return MakePreshuffledQuantTensorView<
                         GemmPipeline::KPerBlockBQ,
                         GemmPipeline::NPerBlockBQ,
                         GemmPipeline::NPerBlock,
                         TilePartitioner::BlockGemmShape::WarpTile::at(I1),
                         GemmPipeline::GetVectorSizeBQ()>(
                         bq_ptr,
                         ck_tile::integer_divide_ceil(kargs.N, QuantGroupSize::kN),
                         QuantGroupSize::kN,
                         kargs.QK_B);
                 }
                 else
                 {
                     using QuantGroupSize = remove_cvref_t<typename GemmPipeline::QuantGroupSize>;
  
                     if constexpr(std::is_same_v<BQLayout, tensor_layout::gemm::RowMajor>)
                     {
                         return make_naive_tensor_view<address_space_enum::global>(
                             bq_ptr,
                             make_tuple(integer_divide_ceil(kargs.K, QuantGroupSize::kK),
                                        integer_divide_ceil(kargs.N, QuantGroupSize::kN)),
                             make_tuple(integer_divide_ceil(kargs.N, QuantGroupSize::kN), 1),
                             number<GemmPipeline::GetVectorSizeBQ()>{},
                             number<1>{});
                     }
                     else
                     {
                         static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
                         return make_naive_tensor_view<address_space_enum::global>(
                             bq_ptr,
                             make_tuple(integer_divide_ceil(kargs.N, QuantGroupSize::kN),
                                        integer_divide_ceil(kargs.K, QuantGroupSize::kK)),
                             make_tuple(integer_divide_ceil(kargs.K, QuantGroupSize::kK), 1),
                             number<GemmPipeline::GetVectorSizeBQ()>{},
                             number<1>{});
                     }
                 }
             }
             else if constexpr(kQuantType == QuantType::ABQuantGrouped)
             {
                 static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
                 using QuantGroupSize = remove_cvref_t<typename GemmPipeline::BQuantGroupSize>;
                 return make_naive_tensor_view<address_space_enum::global>(
                     bq_ptr,
                     make_tuple(integer_divide_ceil(kargs.N, QuantGroupSize::kN), kargs.QK_B),
                     make_tuple(kargs.stride_BQ, 1),
                     number<GemmPipeline::GetVectorSizeBQ()>{},
                     number<1>{});
             }
             else
             {
                 return nullptr;
             }
         }();
  
         // Step 2: Create tile window (no padding for BQ)
         const auto& bq_block_window = [&]() {
             if constexpr(kQuantType == QuantType::RowColQuant)
             {
                 return make_tile_window(bq_tensor_view,
                                         make_tuple(number<TilePartitioner::MPerBlock>{},
                                                    number<TilePartitioner::NPerBlock>{}),
                                         {i_m, i_n});
             }
             else if constexpr(kQuantType == QuantType::BQuantGrouped)
             {
                 using QuantGroupSize = remove_cvref_t<typename GemmPipeline::QuantGroupSize>;
                 if constexpr(PreshuffleQuant)
                 {
                     static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
                     constexpr auto block_n =
                         TilePartitioner::NPerBlock /
                         QuantGroupSize::kN; // Number of N-dimension quantization groups per block
                     constexpr auto warp_n = TilePartitioner::BlockGemmShape::WarpTile::at(
                         I1); // Number of N-dimension elements per warp
                     constexpr auto warp_per_group =
                         (QuantGroupSize::kN <
                          warp_n) // Determine how many warps share the same scale in N-dimension
                             ? (warp_n / QuantGroupSize::kN)
                             : (QuantGroupSize::kN / warp_n);
                     constexpr auto bqk_per_block =
                         TilePartitioner::KPerBlock /
                         QuantGroupSize::kK; // Number of K-dimension quantization groups per block
                     constexpr auto
                         tile_window_width = // The pre-shuffled layout flattens warp_n ×
                                             // bqk_per_block scales per row, Padded up to warp_size
                                             // to ensure coalesced memory access.
                         ck_tile::integer_least_multiple(warp_n * bqk_per_block, get_warp_size());
  
                     // Adapts based on fine vs coarse quantization granularity:
                     //   - Fine-grained (QuantGroupSize::kN < warp_n):
                     //       Multiple quant groups per warp → fewer rows needed per block.
                     //       height = block_n / warp_per_group
                     //
                     //   - Coarse-grained (QuantGroupSize::kN >= warp_n):
                     //       Each row represents one quant group.
                     //       height = block_n
                     constexpr auto tile_window_height =
                         (QuantGroupSize::kN < warp_n) ? block_n / warp_per_group : block_n;
                     auto block_n_idx =
                         i_n / TilePartitioner::NPerBlock; // Converts the global N-index (i_n) to a
                                                           // block index.
  
                     return make_tile_window(
                         bq_tensor_view,
                         make_tuple(number<tile_window_height>{}, number<tile_window_width>{}),
                         {block_n_idx * tile_window_height, 0});
                 }
                 else
                 {
                     if constexpr(std::is_same_v<BQLayout, tensor_layout::gemm::RowMajor>)
                     {
                         return make_tile_window(
                             bq_tensor_view,
                             make_tuple(number<TilePartitioner::KPerBlock / QuantGroupSize::kK>{},
                                        number<TilePartitioner::NPerBlock / QuantGroupSize::kN>{}),
                             {0, i_n / QuantGroupSize::kN});
                     }
                     else
                     {
                         static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
                         return make_tile_window(
                             bq_tensor_view,
                             make_tuple(number<TilePartitioner::NPerBlock / QuantGroupSize::kN>{},
                                        number<TilePartitioner::KPerBlock / QuantGroupSize::kK>{}),
                             {i_n / QuantGroupSize::kN, 0});
                     }
                 }
             }
             else if constexpr(kQuantType == QuantType::ABQuantGrouped)
             {
                 static_assert(std::is_same_v<BQLayout, tensor_layout::gemm::ColumnMajor>);
                 using QuantGroupSize = remove_cvref_t<typename GemmPipeline::BQuantGroupSize>;
                 return make_tile_window(
                     bq_tensor_view,
                     make_tuple(number<TilePartitioner::NPerBlock / QuantGroupSize::kN>{},
                                number<TilePartitioner::KPerBlock / QuantGroupSize::kK>{}),
                     {i_n / QuantGroupSize::kN, 0});
             }
             else
             {
                 return nullptr;
             }
         }();
  
         return bq_block_window;
     }
  
     template <memory_operation_enum DstInMemOp = memory_operation_enum::set>
     CK_TILE_DEVICE static auto MakeCBlockWindow(CDataType* c_ptr,
                                                 const QuantGemmKernelArgs& kargs,
                                                 const index_t i_m,
                                                 const index_t i_n)
     {
         // Step 1: Create tensor view for C
         const auto& c_tensor_view = [&]() {
             if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
             {
                 return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
                     c_ptr,
                     make_tuple(kargs.M, kargs.N),
                     make_tuple(kargs.stride_C, 1),
                     number<EpiloguePipeline::GetVectorSizeC()>{},
                     number<1>{});
             }
             else
             {
                 return make_naive_tensor_view<address_space_enum::global, DstInMemOp>(
                     c_ptr,
                     make_tuple(kargs.M, kargs.N),
                     make_tuple(1, kargs.stride_C),
                     number<1>{},
                     number<1>{});
             }
         }();
  
         // Step 2: Create padded view
         const auto& c_pad_view = [&]() {
             if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
             {
                 return pad_tensor_view(c_tensor_view,
                                        make_tuple(number<TilePartitioner::MPerBlock>{},
                                                   number<TilePartitioner::NPerBlock>{}),
                                        sequence<false, GemmPipeline::kPadN>{});
             }
             else
             {
                 return pad_tensor_view(c_tensor_view,
                                        make_tuple(number<TilePartitioner::MPerBlock>{},
                                                   number<TilePartitioner::NPerBlock>{}),
                                        sequence<GemmPipeline::kPadM, false>{});
             }
         }();
  
         // Step 3: Create tile window
         auto c_block_window = make_tile_window(
             c_pad_view,
             make_tuple(number<TilePartitioner::MPerBlock>{}, number<TilePartitioner::NPerBlock>{}),
             {i_m, i_n});
  
         return c_block_window;
     }
  
     CK_TILE_HOST static bool IsSupportedArgument(const QuantGemmKernelArgs& kargs)
     {
         if(kargs.k_batch != 1)
         {
             if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
             {
                 CK_TILE_ERROR("Conditions not met for Kbatch >1 !");
             }
             return false;
         }
  
         if constexpr(std::is_same_v<ALayout, tensor_layout::gemm::RowMajor>)
         {
             if(kargs.K % (TilePartitioner::KPerBlock * kargs.k_batch) != 0 &&
                GemmPipeline::kPadK == false)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR("Can't support K that is not a multiple of k_batch * KPerBlock "
                                   "without padding!");
                 }
                 return false;
             }
             if(kargs.K % GemmPipeline::GetVectorSizeA() != 0)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR("K is not a multiple of vector load size for A tensor!");
                 }
                 return false;
             }
         }
         else
         {
             if(kargs.M % TilePartitioner::MPerBlock != 0 && GemmPipeline::kPadM == false)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR(
                         "Can't support M that is not a multiple of MPerBlock without padding!");
                 }
                 return false;
             }
             if(kargs.M % GemmPipeline::GetVectorSizeA() != 0)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR("M is not a multiple of vector load size for A tensor!");
                 }
                 return false;
             }
         }
  
         if constexpr(std::is_same_v<BLayout, tensor_layout::gemm::RowMajor>)
         {
             if(kargs.N % TilePartitioner::NPerBlock != 0 && GemmPipeline::kPadN == false)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR(
                         "Can't support N that is not a multiple of NPerBlock without padding!");
                 }
                 return false;
             }
             if(kargs.N % GemmPipeline::GetVectorSizeB() != 0)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR("N is not a multiple of vector load size for B tensor!");
                 }
                 return false;
             }
         }
         else
         {
             if(kargs.K % (TilePartitioner::KPerBlock * kargs.k_batch) != 0 &&
                GemmPipeline::kPadK == false)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR("Can't support K that is not a multiple of k_batch * KPerBlock "
                                   "without padding!");
                 }
                 return false;
             }
             if(kargs.K % GemmPipeline::GetVectorSizeB() != 0)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR("K is not a multiple of vector load size for B tensor!");
                 }
                 return false;
             }
         }
  
         if constexpr(std::is_same_v<CLayout, tensor_layout::gemm::RowMajor>)
         {
             if(kargs.N % TilePartitioner::NPerBlock != 0 && GemmPipeline::kPadN == false)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR(
                         "Can't support N that is not a multiple of NPerBlock without padding!");
                 }
                 return false;
             }
             if(kargs.N % EpiloguePipeline::GetVectorSizeC() != 0)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR("N is not a multiple of vector load size for C tensor!");
                 }
                 return false;
             }
         }
         else
         {
             if(kargs.M % TilePartitioner::MPerBlock != 0 && GemmPipeline::kPadM == false)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR(
                         "Can't support M that is not a multiple of MPerBlock without padding!");
                 }
                 return false;
             }
             if(kargs.M % EpiloguePipeline::GetVectorSizeC() != 0)
             {
                 if(ck_tile::EnvIsEnabled(CK_TILE_ENV(CK_TILE_LOGGING)))
                 {
                     CK_TILE_ERROR("M is not a multiple of vector load size for C tensor!");
                 }
                 return false;
             }
         }
         return true;
     }
  
     CK_TILE_DEVICE static void RunGemm(const ADataType* a_ptr,
                                        const BDataType* b_ptr,
                                        const AQDataType* aq_ptr,
                                        const BQDataType* bq_ptr,
                                        CDataType* c_ptr,
                                        void* smem_ptr,
                                        const QuantGemmKernelArgs& kargs,
                                        const SplitKBatchOffset& splitk_batch_offset,
                                        const index_t block_idx_m,
                                        const index_t block_idx_n)
     {
         // Create block windows using specialized methods
         const auto& a_block_window =
             MakeABlockWindow(a_ptr, kargs, splitk_batch_offset.splitted_k, block_idx_m);
         const auto& b_block_window =
             MakeBBlockWindow(b_ptr, kargs, splitk_batch_offset.splitted_k, block_idx_n);
         const auto& aq_block_window = MakeAQBlockWindow(aq_ptr, kargs, block_idx_m, block_idx_n);
         const auto& bq_block_window = MakeBQBlockWindow(bq_ptr, kargs, block_idx_m, block_idx_n);
  
         const index_t num_loop =
             amd_wave_read_first_lane(TilePartitioner::GetLoopNum(splitk_batch_offset.splitted_k));
  
         // Run GEMM cooperatively by whole workgroup.
         const auto& c_block_tile = [&]() {
             if constexpr(kQuantType == QuantType::AQuantGrouped)
             {
                 index_t m = 0;
                 if constexpr(PreshuffleQuant)
                 {
                     m = kargs.M;
                 }
                 return GemmPipeline{}.template operator()(
                     a_block_window, b_block_window, aq_block_window, num_loop, smem_ptr, m);
             }
             else if constexpr(kQuantType == QuantType::BQuantGrouped)
             {
                 index_t n = 0;
                 if constexpr(PreshuffleQuant)
                 {
                     n = kargs.N;
                 }
                 return GemmPipeline{}.template operator()(
                     a_block_window, b_block_window, bq_block_window, num_loop, smem_ptr, n);
             }
             else if constexpr(kQuantType == QuantType::ABQuantGrouped)
             {
                 index_t m = 0;
                 index_t n = 0;
                 if constexpr(PreshuffleQuant)
                 {
                     m = kargs.M;
                     n = kargs.N;
                 }
                 return GemmPipeline{}.template operator()(a_block_window,
                                                           b_block_window,
                                                           aq_block_window,
                                                           bq_block_window,
                                                           num_loop,
                                                           smem_ptr,
                                                           m,
                                                           n);
             }
             else if constexpr(kQuantType == QuantType::RowColQuant ||
                               kQuantType == QuantType::TensorQuant)
             {
                 return GemmPipeline{}.template operator()(
                     a_block_window, b_block_window, num_loop, smem_ptr);
             }
         }();
  
         const index_t k_batch = amd_wave_read_first_lane(kargs.k_batch);
  
         // Run Epilogue Pipeline with k_batch dispatch
         if(k_batch == 1)
         {
             auto c_block_window = MakeCBlockWindow<memory_operation_enum::set>(
                 c_ptr, kargs, block_idx_m, block_idx_n);
  
             if constexpr(kQuantType == QuantType::ABQuantGrouped ||
                          kQuantType == QuantType::AQuantGrouped ||
                          kQuantType == QuantType::BQuantGrouped)
             {
                 EpiloguePipeline{}(c_block_window, c_block_tile, c_block_window, smem_ptr);
             }
             else if constexpr(kQuantType == QuantType::RowColQuant)
             {
                 EpiloguePipeline{}(c_block_window,
                                    c_block_tile,
                                    c_block_window,
                                    smem_ptr,
                                    aq_block_window,
                                    bq_block_window);
             }
             else if constexpr(kQuantType == QuantType::TensorQuant)
             {
                 const AccDataType aq_scale = type_convert<AccDataType>(*aq_ptr);
                 const AccDataType bq_scale = type_convert<AccDataType>(*bq_ptr);
                 EpiloguePipeline{}(
                     c_block_window, c_block_tile, c_block_window, smem_ptr, aq_scale, bq_scale);
             }
         }
         else
         {
             auto c_block_window = MakeCBlockWindow<memory_operation_enum::atomic_add>(
                 c_ptr, kargs, block_idx_m, block_idx_n);
  
             if constexpr(kQuantType == QuantType::ABQuantGrouped ||
                          kQuantType == QuantType::AQuantGrouped ||
                          kQuantType == QuantType::BQuantGrouped)
             {
                 EpiloguePipeline{}(c_block_window, c_block_tile, c_block_window, smem_ptr);
             }
             else if constexpr(kQuantType == QuantType::RowColQuant)
             {
                 EpiloguePipeline{}(c_block_window,
                                    c_block_tile,
                                    c_block_window,
                                    smem_ptr,
                                    aq_block_window,
                                    bq_block_window);
             }
             else if constexpr(kQuantType == QuantType::TensorQuant)
             {
                 const AccDataType aq_scale = type_convert<AccDataType>(*aq_ptr);
                 const AccDataType bq_scale = type_convert<AccDataType>(*bq_ptr);
                 EpiloguePipeline{}(
                     c_block_window, c_block_tile, c_block_window, smem_ptr, aq_scale, bq_scale);
             }
         }
     }
  
     CK_TILE_DEVICE void operator()(QuantGemmKernelArgs kargs) const
     {
         const auto blockId  = amd_wave_read_first_lane(blockIdx.x);
         const auto [iM, iN] = TilePartitioner{kargs.M, kargs.N}.GetOutputTileIndex(blockId);
         const index_t i_m   = amd_wave_read_first_lane(iM * TilePartitioner::MPerBlock);
         const index_t i_n   = amd_wave_read_first_lane(iN * TilePartitioner::NPerBlock);
         const SplitKBatchOffset splitk_batch_offset(kargs);
  
         // Apply splitk offset to input pointers
         const ADataType* a_ptr =
             static_cast<const ADataType*>(kargs.a_ptr) + splitk_batch_offset.a_k_split_offset;
         const BDataType* b_ptr =
             static_cast<const BDataType*>(kargs.b_ptr) + splitk_batch_offset.b_k_split_offset;
         const AQDataType* aq_ptr = static_cast<const AQDataType*>(kargs.aq_ptr);
         const BQDataType* bq_ptr = static_cast<const BQDataType*>(kargs.bq_ptr);
         CDataType* c_ptr         = static_cast<CDataType*>(kargs.c_ptr);
  
         // allocate LDS
         __shared__ char smem_ptr[GetSmemSize()];
  
         RunGemm(
             a_ptr, b_ptr, aq_ptr, bq_ptr, c_ptr, smem_ptr, kargs, splitk_batch_offset, i_m, i_n);
     }
 };
  
 } // namespace ck_tile