// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT

#pragma once

#include "ck_tile/core.hpp"
#include "ck_tile/host/concat.hpp"
#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_problem.hpp"
#include "ck_tile/ops/flatmm/pipeline/flatmm_pipeline_agmem_bgmem_creg_v1.hpp"
#include "ck_tile/ops/flatmm/pipeline/mx_flatmm_pipeline_agmem_bgmem_creg_v1_policy.hpp"

namespace ck_tile {

template <typename ADataType_,
          typename BDataType_,
          typename CDataType_,
          typename BlockGemmShape_,
          typename Traits_,
          GemmPipelineScheduler Scheduler_      = GemmPipelineScheduler::Intrawave,
          bool HasHotLoop_                      = true,
          TailNumber TailNum_                   = TailNumber::Full,
          amd_buffer_coherence_enum BMemNTType_ = amd_buffer_coherence_enum::coherence_default,
          bool BPreShufflePermute_              = false,
          typename ComputeDataType_             = ADataType_>
struct MXFlatmmPipelineProblem : FlatmmPipelineProblem<ADataType_,
                                                       BDataType_,
                                                       CDataType_,
                                                       BlockGemmShape_,
                                                       Traits_,
                                                       Scheduler_,
                                                       HasHotLoop_,
                                                       TailNum_,
                                                       BMemNTType_,
                                                       BPreShufflePermute_,
                                                       ComputeDataType_>
{
    using BlockGemmShape = BlockGemmShape_;

    // using QuantType = BDataType_;

    static constexpr int ScaleGranularityK = 32;

    static constexpr int ContinuousKPerThread = 32; // it's fixed for mx
    static constexpr int MXdlPack             = 2;  // it's fixed for mx
    static constexpr int NXdlPack             = 2;  // it's fixed for mx
    static constexpr int KXdlPack             = 2;
    // static constexpr index_t flatKPerWarp = BlockGemmShape::flatKPerWarp * KXdlPack;
    static constexpr index_t flatKPerWarp = get_warp_size() * ContinuousKPerThread;
};

template <typename Problem, typename PipelinePolicy = MXFlatmmPipelineAgBgCrPolicy>
struct MXFlatmmPipelineAGmemBGmemCRegV1 : FlatmmPipelineAGmemBGmemCRegV1<Problem, PipelinePolicy>
{
    using Underlying = FlatmmPipelineAGmemBGmemCRegV1<Problem, PipelinePolicy>;

    using ADataType      = remove_cvref_t<typename Problem::ADataType>;
    using BDataType      = remove_cvref_t<typename Problem::BDataType>;
    using CDataType      = remove_cvref_t<typename Problem::CDataType>;
    using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>; // TileFlatmmShape

    using ComputeType = ADataType;
    static_assert(sizeof(ADataType) >= sizeof(BDataType));

    using ALayout = remove_cvref_t<typename Problem::ALayout>;
    using BLayout = remove_cvref_t<typename Problem::BLayout>;
    using CLayout = remove_cvref_t<typename Problem::CLayout>;

    static constexpr index_t APackedSize = numeric_traits<ADataType>::PackedSize;
    static constexpr index_t BPackedSize = numeric_traits<BDataType>::PackedSize;

    using BlockFlatmm =
        remove_cvref_t<decltype(PipelinePolicy::template GetBlockFlatmm<Problem>())>;

    static constexpr auto config =
        BlockFlatmm::BlockPolicy::template GetWarpGemmMWarpNWarp<Problem>();

    using WG = remove_cvref_t<decltype(config.template at<0>())>;

    static constexpr index_t DsWritePreIssue = 3; // default 2, ds write at MIter - 2
    static constexpr index_t DsReadPreload   = 4; // default 4 for MXFP4 (MXdlPack * KXdlPack)

    static constexpr index_t BlockSize = Problem::kBlockSize;
    static constexpr index_t WaveSize  = get_warp_size();

    static constexpr index_t kMPerBlock = BlockGemmShape::kM;
    static constexpr index_t kNPerBlock = BlockGemmShape::kN;
    static constexpr index_t kKPerBlock = BlockGemmShape::kK;

    static constexpr index_t flatKPerWarp = BlockGemmShape::flatKPerWarp;
    static constexpr index_t flatNPerWarp = BlockGemmShape::flatNPerWarp;

    static constexpr index_t GetVectorSizeA() { return 32; } /* fixed for fp4 shuffle layout*/
    static constexpr index_t GetVectorSizeB() { return 32; } /* fixed for fp4 shuffle layout*/
    static constexpr index_t GetVectorSizeC() { return Problem::VectorSizeC; }

    static constexpr bool kPadM = Problem::kPadM;
    static constexpr bool kPadN = Problem::kPadN;
    static constexpr bool kPadK = Problem::kPadK;

    // static constexpr index_t kLdsAlignmentInBytes = 16;
    static constexpr index_t NumWaveGroups    = Problem::NumWaveGroups;
    static constexpr bool UsePersistentKernel = Problem::Traits::UsePersistentKernel;

    static constexpr auto I0   = number<0>();
    static constexpr auto I1   = number<1>();
    static constexpr auto I2   = number<2>();
    static constexpr auto idxM = I0;
    static constexpr auto idxN = I1;
    static constexpr auto idxK = I2;
    using BlockTile            = remove_cvref_t<typename BlockGemmShape::BlockTile>;
    using BlockWarps           = remove_cvref_t<typename BlockGemmShape::BlockWarps>;
    using WarpTile             = remove_cvref_t<typename BlockGemmShape::WarpTile>;

    static constexpr index_t MWarp = config.template at<1>();
    static constexpr index_t NWarp = config.template at<2>();

    static constexpr index_t MIterPerWarp = kMPerBlock / (MWarp * WG::kM);
    static constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WG::kN);
    static constexpr index_t KIterPerWarp = kKPerBlock / WG::kK;

    static constexpr index_t KFlatBytesPerBlockPerIter =
        flatKPerWarp * sizeof(BDataType) / BPackedSize;
    static constexpr index_t NFlatPerBlockPerIter = flatNPerWarp;

    static constexpr index_t MPerBlockPerIter = kMPerBlock / MIterPerWarp;
    static constexpr index_t KPerBlockPerIter = kKPerBlock / KIterPerWarp;

    // static constexpr index_t WG_AKPacks = WG::kK / APackedSize;
    // static constexpr index_t WG_BKPacks = WG::kK / BPackedSize;

    static constexpr index_t MXdlPack          = Problem::MXdlPack;
    static constexpr index_t NXdlPack          = Problem::NXdlPack;
    static constexpr index_t KXdlPack          = Problem::KXdlPack;
    static constexpr index_t ScaleGranularityK = Problem::ScaleGranularityK;

    static constexpr index_t AK1 = std::is_same_v<ADataType, pk_fp6x16_t>
                                       ? 16
                                       : 16 /*dwordx4*/ * APackedSize / sizeof(ADataType);
    static constexpr index_t BK1 = std::is_same_v<BDataType, pk_fp6x16_t>
                                       ? 16
                                       : 16 /*dwordx4*/ * BPackedSize / sizeof(BDataType);

    static constexpr index_t m_preload = (MIterPerWarp * KIterPerWarp >= DsReadPreload)
                                             ? DsReadPreload
                                             : MIterPerWarp * KIterPerWarp;

    static constexpr bool HasHotLoop = Problem::HasHotLoop;
    static constexpr auto TailNum    = Problem::TailNum;

    static constexpr index_t mfma_per_wg = 1; // 950 only

    static constexpr index_t dsread_per_wg = WG::kM * WG::kK / AK1 / WaveSize;
    static_assert((WG::kM * WG::kK) % (AK1 * WaveSize) == 0);

    static constexpr index_t dsread_num_perK  = dsread_per_wg * MIterPerWarp;
    static constexpr index_t dswrite_num_perK = dsread_num_perK / NWarp;
    static constexpr index_t dswrite_rep    = (dswrite_num_perK + MIterPerWarp - 1) / MIterPerWarp;
    static constexpr index_t Aload_num_perK = dswrite_num_perK;
    static constexpr index_t Aload_rep      = dswrite_rep;

    static constexpr index_t Bload_num_perK = kNPerBlock * WG::kK / NWarp / BK1 / WaveSize;
    static constexpr index_t Bload_num      = Bload_num_perK * KIterPerWarp;
    static constexpr index_t ScaleBload_num =
        kNPerBlock * kKPerBlock / NWarp / ScaleGranularityK / NXdlPack / KXdlPack / WaveSize;
    static constexpr index_t ScaleAload_num =
        kMPerBlock * kKPerBlock / MWarp / ScaleGranularityK / MXdlPack / KXdlPack / WaveSize;

    // static constexpr index_t KPerScaleLoad = KIterPerWarp / ScaleBload_num;
    static constexpr index_t HalfMIter = (MIterPerWarp + 1) / 2;
    static constexpr index_t Bload_rep = (Bload_num_perK + HalfMIter - 1) / HalfMIter;

    static constexpr index_t mfma_perM_perK = NIterPerWarp * mfma_per_wg;
    static constexpr index_t dswrite_mIter  = (DsWritePreIssue - 1) % MIterPerWarp;
    static constexpr index_t dswrite_kIter  = (DsWritePreIssue - 1) / MIterPerWarp;

    // For the basic gemm pipelien DoubleSmemBuffer set to be false naturally.
    static constexpr bool DoubleSmemBuffer = false;

    CK_TILE_HOST_DEVICE static constexpr auto
    SchedulerPerM(index_t dsread_perM, index_t dswrite_perM, index_t load_perM)
    {
        // Init inst order
        index_t max_data_inst   = dsread_perM > load_perM
                                      ? (dsread_perM > dswrite_perM ? dsread_perM : dswrite_perM)
                                      : (load_perM > dswrite_perM ? load_perM : dswrite_perM);
        index_t sum_data_inst   = dsread_perM + load_perM + dswrite_perM;
        index_t round_data_inst = (sum_data_inst + mfma_perM_perK - 1) / mfma_perM_perK;

        index_t inst_order[NIterPerWarp * 10];
        _Pragma("unroll") for(int idx = 0; idx < NIterPerWarp * 10; idx++) { inst_order[idx] = 0; }

        index_t index = 0;
        _Pragma("unroll") for(int j = 0; j < max_data_inst; j++)
        {
            if(dswrite_perM > j)
            {
                inst_order[index] = 1;
                index++;
            }
            if(load_perM > j)
            {
                inst_order[index] = 2;
                index++;
            }
            if(dsread_perM > j)
            {
                inst_order[index] = 3;
                index++;
            }
        }

        // Schedule IGLP
        _Pragma("unroll") for(int j = 0; j < mfma_perM_perK; j++)
        {
            index_t inst_idx = 0;
            if(j == 0)
                ;
            else if(j == 1)
                inst_idx = mfma_perM_perK == 2 ? 1 : mfma_perM_perK - 2;
            else if(j == 2)
                inst_idx = mfma_perM_perK - 1;
            else
                inst_idx = mfma_perM_perK - j;

            __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA

            _Pragma("unroll") for(int r = 0; r < round_data_inst; r++)
            {
                if(r % 2 == 0)
                {
                    if(inst_order[inst_idx + r * mfma_perM_perK] == 1)
                    {
                        // __builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS write
                    }
                    if(inst_order[inst_idx + r * mfma_perM_perK] == 2)
                    {
                        __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                    }
                    if(inst_order[inst_idx + r * mfma_perM_perK] == 3)
                    {
                        __builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
                    }
                }
                else
                {
                    if(inst_order[(r + 1) * mfma_perM_perK - 1 - inst_idx] == 1)
                    {
                        // __builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS write
                    }
                    if(inst_order[(r + 1) * mfma_perM_perK - 1 - inst_idx] == 2)
                    {
                        __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
                    }
                    if(inst_order[(r + 1) * mfma_perM_perK - 1 - inst_idx] == 3)
                    {
                        __builtin_amdgcn_sched_group_barrier(0x100, 1, 0); // DS read
                    }
                }
            }
        }
    }

    CK_TILE_HOST_DEVICE static constexpr auto HotLoopScheduler()
    {
        // Keypoint of pipeline optimize is workload balance in time
        // instruction schedule example(128X256X256, 1X4, 16X16X128):
        // Iter MNK     MFMA    ds_read ds_write    A_load  b_load
        // -1   M6N0:   57      -       8           -       -
        // -1   M6N1:   58      1       -           -       -
        // -1   M6N2:   59      -       -           7       -
        // -1   M6N3:   60      2       -           -       -
        // -1   M7N0:   61      -       -           -       -
        // -1   M7N1:   62      3       -           -       -
        // -1   M7N2:   63      -       -           8       -
        // -1   M7N3:   64      4       -           -       -
        //  0   M0N0K0:  1      -       -           -       1
        //  0   M0N1:    2      5       -           -       -
        //  0   M0N2:    3      -       -           -       2
        //  0   M0N3:    4      6       -           -       -
        //  0   M1N0:    5      -       -           -       3
        //  0   M1N1:    6      7       -           -       -
        //  0   M1N2:    7      -       -           -       4
        //  0   M1N3:    8      8       -           -       -
        //  0   M2N0:    9      -       -           -       5
        //  0   M2N1:   10      9       -           -       -
        //  0   M2N2:   11      -       -           -       6
        //  0   M2N3:   12     10       -           -       -
        //  0   M3N0:   13      -       1           -       7
        //  0   M3N1:   14     11       -           -       -
        //  0   M3N2:   15      -       -           -       8
        //  0   M3N3:   16     12       -           -       -
        //  0   M4N0:   17      -       2           -       -
        //  0   M4N1:   18     13       -           -       -
        //  0   M4N2:   19      -       -           1       -
        //  0   M4N3:   20     14       -           -       -
        //  0   M5N0:   21      -       3           -       -
        //  0   M5N1:   22     15       -           -       -
        //  0   M5N2:   23      -       -           2       -
        //  0   M5N3:   24     16       -           -       -
        //  0   M6N0:   25      -       4           -       -
        //  0   M6N1:   26     17       -           -       -
        //  0   M6N2:   27      -       -           3       -
        //  0   M6N3:   28     18       -           -       -
        //  0   M7N0:   29      -       -           -       -
        //  0   M7N1:   30     19       -           -       -
        //  0   M7N2:   31      -       -           4       -
        //  0   M7N3:   32     20       -           -       -
        //  0   M0N0K1: 33      -       -           -       9
        //  0   M0N1:   34     21       -           -       -
        //  0   M0N2:   35      -       -           -       10
        //  0   M0N3:   36     22       -           -       -
        //  0   M1N0:   37      -       -           -       11
        //  0   M1N1:   38     23       -           -       -
        //  0   M1N2:   39      -       -           -       12
        //  0   M1N3:   40     24       -           -       -
        //  0   M2N0:   41      -       -           -       13
        //  0   M2N1:   42     25       -           -       -
        //  0   M2N2:   43      -       -           -       14
        //  0   M2N3:   44     26       -           -       -
        //  0   M3N0:   45      -       5           -       15
        //  0   M3N1:   46     27       -           -       -
        //  0   M3N2:   47      -       -           -       16
        //  0   M3N3:   48     28       -           -       -
        //  0   M4N0:   49      -       6           -       -
        //  0   M4N1:   50     29       -           -       -
        //  0   M4N2:   51      -       -           5       -
        //  0   M4N3:   52     30       -           -       -
        //  0   M5N0:   53      -       7           -       -
        //  0   M5N1:   54     31       -           -       -
        //  0   M5N2:   55      -       -           6       -
        //  0   M5N3:   56     32       -           -       -
        //  0   M6N0:   57      -       8           -       -
        //  0   M6N1:   58      1       -           -       -
        //  0   M6N2:   59      -       -           7       -
        //  0   M6N3:   60      2       -           -       -
        //  0   M7N0:   61      -       -           -       -
        //  0   M7N1:   62      3       -           -       -
        //  0   M7N2:   63      -       -           8       -
        //  0   M7N3:   64      4       -           -       -

        _Pragma("unroll") for(int kIter = 0; kIter < KIterPerWarp; kIter++)
        {
            _Pragma("unroll") for(int mIter = 0; mIter < MIterPerWarp; mIter++)
            {
                index_t dsread_perM  = 0;
                index_t dswrite_perM = 0;
                index_t load_perM    = 0;

                // Calculate ds_read number per M
                dsread_perM = dsread_per_wg;

                // Calculate ds_write number per M
                if(mIter == 0)
                {
                    dswrite_perM =
                        (dswrite_num_perK - (MIterPerWarp - DsWritePreIssue) * dswrite_rep) > 0
                            ? dswrite_num_perK - (MIterPerWarp - DsWritePreIssue) * dswrite_rep
                            : 0;
                }
                else if(mIter >= MIterPerWarp - DsWritePreIssue + 1)
                {
                    dswrite_perM = 0;
                }
                else
                {
                    dswrite_perM = (dswrite_num_perK -
                                    (MIterPerWarp - DsWritePreIssue - mIter) * dswrite_rep) > 0
                                       ? dswrite_rep
                                       : 0;
                }
                // Add ds write when ds write data > needed
                if(dswrite_num_perK == 0 && kIter == (KIterPerWarp - 1 - dswrite_kIter))
                {
                    if(mIter == MIterPerWarp - 1 - dswrite_mIter)
                        dswrite_perM = 1;
                }

                // Calculate buffer_load number per M
                if(mIter < HalfMIter)
                {
                    load_perM =
                        ((Aload_num_perK - (MIterPerWarp - 1 - mIter) * Aload_rep) > 0 ? Aload_rep
                                                                                       : 0) +
                        ((Bload_num_perK - (HalfMIter - 1 - mIter) * Bload_rep) > 0 ? Bload_rep
                                                                                    : 0);
                }
                else
                {
                    load_perM = (Aload_num_perK - (MIterPerWarp - 1 - mIter) * Aload_rep) > 0
                                    ? Aload_rep
                                    : 0;
                }
                SchedulerPerM(dsread_perM, dswrite_perM, load_perM);
            }
        }
        // Add Aload when Aload data > needed
        if(Aload_num_perK == 0)
            __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
        __builtin_amdgcn_sched_barrier(0);
    }

    CK_TILE_HOST_DEVICE static constexpr auto Last2ndHotLoopScheduler()
    {
        _Pragma("unroll") for(int kIter = 0; kIter < KIterPerWarp; kIter++)
        {
            _Pragma("unroll") for(int mIter = 0; mIter < MIterPerWarp; mIter++)
            {
                index_t dsread_perM  = 0;
                index_t dswrite_perM = 0;
                index_t load_perM    = 0;

                // Calculate ds_read number per M
                dsread_perM = dsread_per_wg;

                // Calculate ds_write number per M
                if(mIter == 0)
                {
                    dswrite_perM =
                        (dswrite_num_perK - (MIterPerWarp - DsWritePreIssue) * dswrite_rep) > 0
                            ? dswrite_num_perK - (MIterPerWarp - DsWritePreIssue) * dswrite_rep
                            : 0;
                }
                else if(mIter >= MIterPerWarp - DsWritePreIssue + 1)
                {
                    dswrite_perM = 0;
                }
                else
                {
                    dswrite_perM = (dswrite_num_perK -
                                    (MIterPerWarp - DsWritePreIssue - mIter) * dswrite_rep) > 0
                                       ? dswrite_rep
                                       : 0;
                }
                // Add ds write when ds write data > needed
                if(dswrite_num_perK == 0 && kIter == (KIterPerWarp - 1 - dswrite_kIter))
                {
                    if(mIter == MIterPerWarp - 1 - dswrite_mIter)
                        dswrite_perM = 1;
                }

                // Calculate buffer_load number per M
                if(mIter < HalfMIter)
                {
                    load_perM =
                        ((Bload_num_perK - (HalfMIter - 1 - mIter) * Bload_rep) > 0 ? Bload_rep
                                                                                    : 0);
                }
                SchedulerPerM(dsread_perM, dswrite_perM, load_perM);
            }
        }
        __builtin_amdgcn_sched_barrier(0);
    }

    CK_TILE_HOST_DEVICE static constexpr auto LastHotLoopScheduler()
    {
        _Pragma("unroll") for(int kIter = 0; kIter < KIterPerWarp; kIter++)
        {
            _Pragma("unroll") for(int mIter = 0; mIter < MIterPerWarp; mIter++)
            {
                index_t dsread_perM  = 0;
                index_t dswrite_perM = 0;
                index_t load_perM    = 0;

                // Calculate ds_read number per M
                if((kIter * MIterPerWarp + mIter) < (KIterPerWarp * MIterPerWarp - m_preload))
                    dsread_perM = dsread_per_wg;

                SchedulerPerM(dsread_perM, dswrite_perM, load_perM);
            }
        }
        // __builtin_amdgcn_sched_barrier(0);
    }

    template <typename... Args>
    CK_TILE_DEVICE auto operator()(Args&&... args) const
    {
        auto c_warp_tensors = Run_(std::forward<Args>(args)...);

        // Block GEMM Acc register tile
        using CWarpDstr = typename WG::CWarpDstr;
        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 c_block_tile                   = BlockFlatmm{}.MakeCBlockTile();
        static_ford<sequence<MIterPerWarp, NIterPerWarp>>{}([&](auto mn) {
            constexpr auto mIter = number<mn[number<0>{}]>{};
            constexpr auto nIter = number<mn[number<1>{}]>{};
            c_block_tile.set_y_sliced_thread_data(
                merge_sequences(sequence<mIter, nIter>{}, c_warp_y_index_zeros),
                merge_sequences(sequence<1, 1>{}, c_warp_y_lengths),
                c_warp_tensors(mIter)(nIter).get_thread_buffer());
        });
        return c_block_tile;
    }

    template <typename ADramBlockWindowTmp,
              typename BFlatBlockWindowTmp,
              typename ScaleADramBlockWindowTmp,
              typename ScaleBDramBlockWindowTmp>
    CK_TILE_DEVICE auto Run_(const ADramBlockWindowTmp& a_copy_dram_window_tmp,
                             const BFlatBlockWindowTmp& b_flat_dram_block_window_tmp,
                             const ScaleADramBlockWindowTmp& scale_a_window,
                             const ScaleBDramBlockWindowTmp& scale_b_window,
                             index_t num_loop,
                             void* __restrict__ p_smem_ping,
                             void* __restrict__ p_smem_pong) const
    {
#ifndef __gfx950__
        static_assert(false, "Only gfx950 is supported for MXFP4 flatmm pipeline now.");
#endif
        static_assert(
            std::is_same_v<ADataType, remove_cvref_t<typename ADramBlockWindowTmp::DataType>>,
            "wrong!");

        static_assert(kMPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[number<0>{}],
                      "wrong!");
        static_assert(kKPerBlock == ADramBlockWindowTmp{}.get_window_lengths()[number<1>{}],
                      "wrong!");

        // constexpr auto MIter_2nd_last = max(0, MIterPerWarp - 2);
        static_assert(MWarp == 1);

        using CWarpTensor = typename WG::CWarpTensor;

        auto a_dram_window = PipelinePolicy::template MakeMX_AAsyncLoadBytesDramWindow<Problem>(
            a_copy_dram_window_tmp);

        __builtin_amdgcn_sched_barrier(0);

        // A tile in LDS
        constexpr auto a_lds_block_desc =
            PipelinePolicy::template MakeMX_ALdsBytesBlockDescriptor<Problem>();

        auto a_lds_block_ping = make_tensor_view<address_space_enum::lds>(
            static_cast<uint8_t*>(p_smem_ping), a_lds_block_desc);
        auto a_lds_block_pong = make_tensor_view<address_space_enum::lds>(
            static_cast<uint8_t*>(p_smem_pong), a_lds_block_desc);

        auto a_store_lds_window_ping = make_tile_window( //
            a_lds_block_ping,
            make_tuple(number<kMPerBlock>{},
                       number<kKPerBlock / APackedSize * sizeof(ADataType)>{}),
            {0, 0});
        auto a_store_lds_window_pong = make_tile_window( //
            a_lds_block_pong,
            make_tuple(number<kMPerBlock>{},
                       number<kKPerBlock / APackedSize * sizeof(ADataType)>{}),
            {0, 0});

        // ping-pong window for A LDS
        auto a_warp_window_ping = make_tile_window(
            a_lds_block_ping,
            make_tuple(number<WG::kM>{}, number<WG::kK / APackedSize * sizeof(ADataType)>{}),
            {0, 0},
            PipelinePolicy::template MakeMX_ALDSBytes_TileDistribution<Problem>());
        auto a_warp_window_pong = make_tile_window(
            a_lds_block_pong,
            make_tuple(number<WG::kM>{}, number<WG::kK / APackedSize * sizeof(ADataType)>{}),
            {0, 0},
            PipelinePolicy::template MakeMX_ALDSBytes_TileDistribution<Problem>());

        // B flat DRAM window for load

        // pingpong buffer for B
        auto b_flat_dram_window = PipelinePolicy::template MakeMX_BFlatBytesDramWindow<Problem>(
            b_flat_dram_block_window_tmp);
        auto b_flat_dram_offsets = generate_tuple(
            [&](auto nIter) {
                constexpr auto packed_n_idx  = nIter / number<NXdlPack>{};
                constexpr auto packed_n_rank = nIter % number<NXdlPack>{};
                return b_flat_dram_window.get_load_offset(
                           tuple<number<packed_n_idx * NXdlPack * NFlatPerBlockPerIter>,
                                 number<0>>{}) +
                       b_flat_dram_window.get_load_offset(
                           tuple<number<packed_n_rank>, number<0>>{});
            },
            number<NIterPerWarp>{});
        statically_indexed_array<
            statically_indexed_array<decltype(load_tile(b_flat_dram_window)), KIterPerWarp>,
            NIterPerWarp>
            b_warp_tensor_ping, b_warp_tensor_pong;

        // pingpong buffer for Scale A and Scale B
        auto scale_a_dram_window = make_tile_window(
            scale_a_window.get_bottom_tensor_view(),
            make_tuple(number<MWarp * WG::kM>{}, number<64 / WG::kM>{}),
            scale_a_window.get_window_origin(),
            PipelinePolicy::template MakeMX_ScaleA_FlatDramTileDistribution<Problem>());
        const auto scale_a_dram_step_m = amd_wave_read_first_lane(
            scale_a_dram_window.get_load_offset(tuple<number<MWarp * WG::kM>, number<0>>{}));
        const auto scale_a_dram_step_k = amd_wave_read_first_lane(
            scale_a_dram_window.get_load_offset(tuple<number<0>, number<64 / WG::kM>>{}));

        auto scale_b_dram_window = make_tile_window(
            scale_b_window.get_bottom_tensor_view(),
            make_tuple(number<NWarp * WG::kN>{}, number<64 / WG::kN>{}),
            scale_b_window.get_window_origin(),
            PipelinePolicy::template MakeMX_ScaleB_DramTileDistribution<Problem>());
        const auto scale_b_dram_step_n = amd_wave_read_first_lane(
            scale_b_dram_window.get_load_offset(tuple<number<NWarp * WG::kN>, number<0>>{}));
        const auto scale_b_dram_step_k = amd_wave_read_first_lane(
            scale_b_dram_window.get_load_offset(tuple<number<0>, number<64 / WG::kN>>{}));

        constexpr index_t MPackIterPerWarp = MIterPerWarp / MXdlPack;
        constexpr index_t NPackIterPerWarp = NIterPerWarp / NXdlPack;
        constexpr index_t KPackIterPerWarp = KIterPerWarp / KXdlPack;

        // ping pong buffer for scale A
        statically_indexed_array<
            statically_indexed_array<decltype(load_tile(scale_a_dram_window)), KPackIterPerWarp>,
            MPackIterPerWarp>
            scale_a_tile_tensor_ping, scale_a_tile_tensor_pong;

        // ping pong buffer for scale B
        statically_indexed_array<
            statically_indexed_array<decltype(load_tile(scale_b_dram_window)), KPackIterPerWarp>,
            NPackIterPerWarp>
            scale_b_tile_tensor_ping, scale_b_tile_tensor_pong;

        auto async_load_tile_ = [](auto lds, auto dram) {
            async_load_tile(lds, dram, number<-1>{}, true_type{}, true_type{});
        };

        // HEAD
        // Prefetch A0
        async_load_tile_(a_store_lds_window_ping, a_dram_window);
        move_tile_window(a_dram_window, {0, kKPerBlock * sizeof(ADataType) / APackedSize});

        // prefetch B
        static_for<0, NIterPerWarp, 1>{}([&](auto nIter) {
            static_for<0, KIterPerWarp, 1>{}([&](auto kIter) {
                b_warp_tensor_ping(nIter)(kIter) = load_tile_with_offset(
                    b_flat_dram_window,
                    b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);
            });
            // move B window to next flat K
            b_flat_dram_offsets(nIter) += b_flat_dram_window.get_load_offset(
                tuple<number<0>, number<KIterPerWarp * KFlatBytesPerBlockPerIter>>{});
        });

        // prefetch Scale A
        static_ford<sequence<MPackIterPerWarp, KPackIterPerWarp>>{}([&](auto ii) {
            constexpr auto impack = number<ii[number<0>{}]>{};
            constexpr auto ikpack = number<ii[number<1>{}]>{};
            scale_a_tile_tensor_ping(impack)(ikpack) =
                load_tile_with_offset(scale_a_dram_window,

                                      impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
        });
        // move Scale A window to next K
        move_tile_window(scale_a_dram_window, {0, kKPerBlock / (32 * KXdlPack)});

        // prefetch Scale B
        static_ford<sequence<NPackIterPerWarp, KPackIterPerWarp>>{}([&](auto ii) {
            constexpr auto inpack                    = number<ii[number<0>{}]>{};
            constexpr auto ikpack                    = number<ii[number<1>{}]>{};
            scale_b_tile_tensor_ping(inpack)(ikpack) = load_tile_with_offset(
                scale_b_dram_window, inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
        });
        // move Scale B window to next K
        move_tile_window(scale_b_dram_window, {0, kKPerBlock / (32 * KXdlPack)});
        __builtin_amdgcn_sched_barrier(0);

        // Prefetch A1
        if constexpr(HasHotLoop || TailNum == TailNumber::Even)
        {
            async_load_tile_(a_store_lds_window_pong, a_dram_window);
            move_tile_window(a_dram_window, {0, sizeof(ADataType) * kKPerBlock / APackedSize});
        }
        // initialize C
        statically_indexed_array<statically_indexed_array<CWarpTensor, NIterPerWarp>, MIterPerWarp>
            c_warp_tensors;
        static_for<0, MIterPerWarp, 1>{}([&](auto mIter) {
            static_for<0, NIterPerWarp, 1>{}(
                [&](auto nIter) { clear_tile(c_warp_tensors(mIter)(nIter)); });
        });

        statically_indexed_array<decltype(load_tile(a_warp_window_pong)), m_preload> a_warp_tensor;

        // preload A00,A10... from lds
        s_waitcnt_barrier</*vmcnt*/ Bload_num + ScaleAload_num + ScaleBload_num>();
        static_for<0, m_preload, 1>{}([&](auto loadIter) {
            constexpr auto mIter = loadIter % MXdlPack;
            constexpr auto kIter = loadIter / MXdlPack;

            a_warp_tensor(loadIter) = load_tile_with_offset(
                a_warp_window_ping,
                tuple<number<mIter * WG::kM>,
                      number<kIter * WG::kK * sizeof(ADataType) / APackedSize>>{});
        });
        __builtin_amdgcn_sched_barrier(0);

        // MAIN LOOP
        auto main_body_implx2 = [&]() mutable {
            // prefetch B(2i+1)
            static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
                constexpr auto kIter             = number<kn[number<0>{}]>{};
                constexpr auto nIter             = number<kn[number<1>{}]>{};
                b_warp_tensor_pong(nIter)(kIter) = load_tile_with_offset(
                    b_flat_dram_window,
                    b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);

                // move B window to next flat K
                if constexpr(kIter == KIterPerWarp - 1)
                    b_flat_dram_offsets(nIter) += b_flat_dram_window.get_load_offset(
                        tuple<number<0>, number<KIterPerWarp * KFlatBytesPerBlockPerIter>>{});
            });

            // prefetch Scale A and Scale B (2i+1)
            static_ford<sequence<KPackIterPerWarp, MPackIterPerWarp>>{}([&](auto ii) {
                constexpr auto ikpack                    = number<ii[number<0>{}]>{};
                constexpr auto impack                    = number<ii[number<1>{}]>{};
                scale_a_tile_tensor_pong(impack)(ikpack) = load_tile_with_offset(
                    scale_a_dram_window,
                    impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
            });

            static_ford<sequence<KPackIterPerWarp, NPackIterPerWarp>>{}([&](auto ii) {
                constexpr auto ikpack                    = number<ii[number<0>{}]>{};
                constexpr auto inpack                    = number<ii[number<1>{}]>{};
                scale_b_tile_tensor_pong(inpack)(ikpack) = load_tile_with_offset(
                    scale_b_dram_window,
                    inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
            });

            // GEMM 2i
            static_for_product<number<KPackIterPerWarp>,
                               number<MPackIterPerWarp>,
                               number<NPackIterPerWarp>,
                               number<KXdlPack>,
                               number<MXdlPack>,
                               number<NXdlPack>>{}( //
                [&](auto ikpack, auto impack, auto inpack, auto ikxdl, auto imxdl, auto inxdl) {
                    constexpr auto n_iter    = inpack * NXdlPack + inxdl;
                    constexpr auto m_iter    = impack * MXdlPack + imxdl;
                    constexpr auto k_iter    = ikpack * KXdlPack + ikxdl;
                    constexpr auto APackIter = ikxdl * MXdlPack + imxdl; // idx inside a xdl pack
                    //  warp GEMM
                    WG{}.template operator()<APackIter, ikxdl * NXdlPack + inxdl>(
                        c_warp_tensors(number<m_iter>{})(number<n_iter>{}),
                        bit_cast<typename WG::AWarpTensor>(a_warp_tensor(number<APackIter>{})),
                        bit_cast<typename WG::BWarpTensor>(
                            b_warp_tensor_ping(number<n_iter>{})(number<k_iter>{})),
                        scale_a_tile_tensor_ping(impack)(ikpack).get_thread_buffer()[0],
                        scale_b_tile_tensor_ping(inpack)(ikpack).get_thread_buffer()[0]);
                    // preload next A from lds
                    constexpr auto addr = m_iter % 2 + k_iter * 2 + m_iter / 2 * 4 + m_preload;
                    if constexpr(addr < (KIterPerWarp * MIterPerWarp) &&
                                 (n_iter == NIterPerWarp - 1))
                    {
                        constexpr auto AmIter              = addr % 2 + addr / 4 * 2;
                        constexpr auto AkIter              = addr / 2 % 2;
                        a_warp_tensor(number<APackIter>{}) = load_tile_with_offset( //
                            a_warp_window_ping,
                            tuple<number<AmIter * WG::kM>,
                                  number<sizeof(ADataType) * AkIter * WG::kK / APackedSize>>{});
                    }
                });
            // barrier as ds_load A(2i) and buffer_load_lds A(2i + 1) finished
            s_waitcnt< // vmcnt
                Bload_num + ScaleAload_num + ScaleBload_num>();
            block_sync_lds();

            // Prefetch A(2i+2)
            async_load_tile_(a_store_lds_window_ping, a_dram_window);
            move_tile_window(a_dram_window, {0, kKPerBlock * sizeof(ADataType) / APackedSize});

            // move B window to next flat K
            move_tile_window(scale_a_dram_window, {0, kKPerBlock / (32 * KXdlPack)});
            move_tile_window(scale_b_dram_window, {0, kKPerBlock / (32 * KXdlPack)});

            // preload A(2i+1)
            static_for<0, m_preload, 1>{}([&](auto loadIter) {
                constexpr auto mIter    = loadIter % MXdlPack;
                constexpr auto kIter    = loadIter / MXdlPack;
                a_warp_tensor(loadIter) = load_tile_with_offset(
                    a_warp_window_pong,
                    tuple<number<mIter * WG::kM>,
                          number<kIter * WG::kK * sizeof(ADataType) / APackedSize>>{});
            });
            HotLoopScheduler();

            ////////////////////////////// Next K //////////////////////////////

            // prefetch B(2i+2)
            static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
                constexpr auto kIter             = number<kn[number<0>{}]>{};
                constexpr auto nIter             = number<kn[number<1>{}]>{};
                b_warp_tensor_ping(nIter)(kIter) = load_tile_with_offset(
                    b_flat_dram_window,
                    b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);

                // move B window to next flat K
                if constexpr(kIter == KIterPerWarp - 1)
                    b_flat_dram_offsets(nIter) += b_flat_dram_window.get_load_offset(
                        tuple<number<0>, number<KIterPerWarp * KFlatBytesPerBlockPerIter>>{});
            });

            // prefetch Scale A and Scale B (2i+2)
            static_ford<sequence<KPackIterPerWarp, MPackIterPerWarp>>{}([&](auto ii) {
                constexpr auto ikpack                    = number<ii[number<0>{}]>{};
                constexpr auto impack                    = number<ii[number<1>{}]>{};
                scale_a_tile_tensor_ping(impack)(ikpack) = load_tile_with_offset(
                    scale_a_dram_window,
                    impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
            });

            static_ford<sequence<KPackIterPerWarp, NPackIterPerWarp>>{}([&](auto ii) {
                constexpr auto ikpack                    = number<ii[number<0>{}]>{};
                constexpr auto inpack                    = number<ii[number<1>{}]>{};
                scale_b_tile_tensor_ping(inpack)(ikpack) = load_tile_with_offset(
                    scale_b_dram_window,
                    inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
            });

            // GEMM 2i+1
            static_for_product<number<KPackIterPerWarp>,
                               number<MPackIterPerWarp>,
                               number<NPackIterPerWarp>,
                               number<KXdlPack>,
                               number<MXdlPack>,
                               number<NXdlPack>>{}( //
                [&](auto ikpack, auto impack, auto inpack, auto ikxdl, auto imxdl, auto inxdl) {
                    constexpr auto m_iter    = impack * MXdlPack + imxdl;
                    constexpr auto n_iter    = inpack * NXdlPack + inxdl;
                    constexpr auto k_iter    = ikpack * KXdlPack + ikxdl;
                    constexpr auto APackIter = ikxdl * MXdlPack + imxdl; // idx inside a xdl pack
                    // warp GEMM
                    WG{}.template operator()<APackIter, ikxdl * NXdlPack + inxdl>(
                        c_warp_tensors(number<m_iter>{})(number<n_iter>{}),
                        bit_cast<typename WG::AWarpTensor>(a_warp_tensor(number<APackIter>{})),
                        bit_cast<typename WG::BWarpTensor>(
                            b_warp_tensor_pong(number<n_iter>{})(number<k_iter>{})),
                        scale_a_tile_tensor_pong(impack)(ikpack).get_thread_buffer()[0],  // scale A
                        scale_b_tile_tensor_pong(inpack)(ikpack).get_thread_buffer()[0]); // scale B
                    // preload next A from lds
                    constexpr auto addr = m_iter % 2 + k_iter * 2 + m_iter / 2 * 4 + m_preload;
                    if constexpr(addr < (KIterPerWarp * MIterPerWarp) &&
                                 (n_iter == NIterPerWarp - 1))
                    {
                        constexpr auto AmIter              = addr % 2 + addr / 4 * 2;
                        constexpr auto AkIter              = addr / 2 % 2;
                        a_warp_tensor(number<APackIter>{}) = load_tile_with_offset( //
                            a_warp_window_pong,
                            tuple<number<AmIter * WG::kM>,
                                  number<sizeof(ADataType) * AkIter * WG::kK / APackedSize>>{});
                    }
                });
            // barrier as ds_load A(2i + 1) and buffer_load_lds A(2i + 2) finished
            s_waitcnt< // vmcnt
                Bload_num + ScaleAload_num + ScaleBload_num>();
            block_sync_lds();

            // Prefetch A(2i+3)
            async_load_tile_(a_store_lds_window_pong, a_dram_window);
            move_tile_window(a_dram_window, {0, sizeof(ADataType) * kKPerBlock / APackedSize});
            // move B window to next flat K
            move_tile_window(scale_a_dram_window, {0, kKPerBlock / (32 * KXdlPack)});
            move_tile_window(scale_b_dram_window, {0, kKPerBlock / (32 * KXdlPack)});

            // preload A(2i+2)
            static_for<0, m_preload, 1>{}([&](auto loadIter) {
                constexpr auto mIter    = loadIter % MXdlPack;
                constexpr auto kIter    = loadIter / MXdlPack;
                a_warp_tensor(loadIter) = load_tile_with_offset(
                    a_warp_window_ping,
                    tuple<number<mIter * WG::kM>,
                          number<kIter * WG::kK * sizeof(ADataType) / APackedSize>>{});
            });
            HotLoopScheduler();
        };

        if constexpr(HasHotLoop)
        {
            index_t iCounter = (num_loop - 1) / 2;
            do
            {
                main_body_implx2();
                iCounter--;
            } while(iCounter > 0);
        }
        // TAIL
        if constexpr(TailNum == TailNumber::Even)
        {
            // prefetch B(loopK)
            static_ford<sequence<KIterPerWarp, NIterPerWarp>>{}([&](auto kn) {
                constexpr auto kIter             = number<kn[number<0>{}]>{};
                constexpr auto nIter             = number<kn[number<1>{}]>{};
                b_warp_tensor_pong(nIter)(kIter) = load_tile_with_offset(
                    b_flat_dram_window,
                    b_flat_dram_offsets(nIter) + kIter * KFlatBytesPerBlockPerIter);
            });

            // prefetch Scale A and Scale B (2i+1)
            static_ford<sequence<MPackIterPerWarp, KPackIterPerWarp>>{}([&](auto ii) {
                constexpr auto impack                    = number<ii[number<0>{}]>{};
                constexpr auto ikpack                    = number<ii[number<1>{}]>{};
                scale_a_tile_tensor_pong(impack)(ikpack) = load_tile_with_offset(
                    scale_a_dram_window,
                    impack * scale_a_dram_step_m + ikpack * scale_a_dram_step_k);
            });
            static_ford<sequence<NPackIterPerWarp, KPackIterPerWarp>>{}([&](auto ii) {
                constexpr auto inpack                    = number<ii[number<0>{}]>{};
                constexpr auto ikpack                    = number<ii[number<1>{}]>{};
                scale_b_tile_tensor_pong(inpack)(ikpack) = load_tile_with_offset(
                    scale_b_dram_window,
                    inpack * scale_b_dram_step_n + ikpack * scale_b_dram_step_k);
            });

            // GEMM loopK-1
            static_for_product<number<KPackIterPerWarp>,
                               number<MPackIterPerWarp>,
                               number<NPackIterPerWarp>,
                               number<KXdlPack>,
                               number<MXdlPack>,
                               number<NXdlPack>>{}( //
                [&](auto ikpack, auto impack, auto inpack, auto ikxdl, auto imxdl, auto inxdl) {
                    constexpr auto m_iter    = impack * MXdlPack + imxdl;
                    constexpr auto n_iter    = inpack * NXdlPack + inxdl;
                    constexpr auto k_iter    = ikpack * KXdlPack + ikxdl;
                    constexpr auto APackIter = ikxdl * MXdlPack + imxdl; // idx inside a xdl pack
                    // warp GEMM
                    WG{}.template operator()<APackIter, ikxdl * NXdlPack + inxdl>(
                        c_warp_tensors(number<m_iter>{})(number<n_iter>{}),
                        bit_cast<typename WG::AWarpTensor>(a_warp_tensor(number<APackIter>{})),
                        bit_cast<typename WG::BWarpTensor>(
                            b_warp_tensor_ping(number<n_iter>{})(number<k_iter>{})),
                        scale_a_tile_tensor_ping(impack)(ikpack).get_thread_buffer()[0],  // scale A
                        scale_b_tile_tensor_ping(inpack)(ikpack).get_thread_buffer()[0]); // scale B
                    // preload next A from lds
                    constexpr auto addr = m_iter % 2 + k_iter * 2 + m_iter / 2 * 4 + m_preload;
                    if constexpr(addr < (KIterPerWarp * MIterPerWarp) &&
                                 (n_iter == NIterPerWarp - 1))
                    {
                        constexpr auto AmIter              = addr % 2 + addr / 4 * 2;
                        constexpr auto AkIter              = addr / 2 % 2;
                        a_warp_tensor(number<APackIter>{}) = load_tile_with_offset( //
                            a_warp_window_ping,
                            tuple<number<AmIter * WG::kM>,
                                  number<sizeof(ADataType) * AkIter * WG::kK / APackedSize>>{});
                    }
                });
            // barrier as ds_load A(2i) and buffer_load_lds A(2i + 1) finished
            s_waitcnt< // vmcnt
                Bload_num + ScaleAload_num + ScaleBload_num>();
            block_sync_lds();

            // preload A(2i+1)
            static_for<0, m_preload, 1>{}([&](auto loadIter) {
                constexpr auto mIter    = loadIter % MXdlPack;
                constexpr auto kIter    = loadIter / MXdlPack;
                a_warp_tensor(loadIter) = load_tile_with_offset(
                    a_warp_window_pong,
                    tuple<number<mIter * WG::kM>,
                          number<kIter * WG::kK * sizeof(ADataType) / APackedSize>>{});
            });

            Last2ndHotLoopScheduler();

            // GEMM loopK
            static_for_product<number<KPackIterPerWarp>,
                               number<MPackIterPerWarp>,
                               number<NPackIterPerWarp>,
                               number<KXdlPack>,
                               number<MXdlPack>,
                               number<NXdlPack>>{}( //
                [&](auto ikpack, auto impack, auto inpack, auto ikxdl, auto imxdl, auto inxdl) {
                    constexpr auto m_iter    = impack * MXdlPack + imxdl;
                    constexpr auto n_iter    = inpack * NXdlPack + inxdl;
                    constexpr auto k_iter    = ikpack * KXdlPack + ikxdl;
                    constexpr auto APackIter = ikxdl * MXdlPack + imxdl; // idx inside a xdl pack
                    // warp GEMM
                    WG{}.template operator()<APackIter, ikxdl * NXdlPack + inxdl>(
                        c_warp_tensors(number<m_iter>{})(number<n_iter>{}),
                        bit_cast<typename WG::AWarpTensor>(a_warp_tensor(number<APackIter>{})),
                        bit_cast<typename WG::BWarpTensor>(
                            b_warp_tensor_pong(number<n_iter>{})(number<k_iter>{})),
                        scale_a_tile_tensor_pong(impack)(ikpack).get_thread_buffer()[0],  // scale A
                        scale_b_tile_tensor_pong(inpack)(ikpack).get_thread_buffer()[0]); // scale B
                    // preload next A from lds
                    constexpr auto addr = m_iter % 2 + k_iter * 2 + m_iter / 2 * 4 + m_preload;
                    if constexpr(addr < (KIterPerWarp * MIterPerWarp) &&
                                 (n_iter == NIterPerWarp - 1))
                    {
                        constexpr auto AmIter              = addr % 2 + addr / 4 * 2;
                        constexpr auto AkIter              = addr / 2 % 2;
                        a_warp_tensor(number<APackIter>{}) = load_tile_with_offset(
                            a_warp_window_pong,
                            tuple<number<AmIter * WG::kM>,
                                  number<sizeof(ADataType) * AkIter * WG::kK / APackedSize>>{});
                    }
                });
            LastHotLoopScheduler();
        }
        else if constexpr(TailNum == TailNumber::Odd)
        {
            // GEMM loopK
            static_for_product<number<KPackIterPerWarp>,
                               number<MPackIterPerWarp>,
                               number<NPackIterPerWarp>,
                               number<KXdlPack>,
                               number<MXdlPack>,
                               number<NXdlPack>>{}( //
                [&](auto ikpack, auto impack, auto inpack, auto ikxdl, auto imxdl, auto inxdl) {
                    constexpr auto m_iter    = impack * MXdlPack + imxdl;
                    constexpr auto n_iter    = inpack * NXdlPack + inxdl;
                    constexpr auto k_iter    = ikpack * KXdlPack + ikxdl;
                    constexpr auto APackIter = ikxdl * MXdlPack + imxdl; // idx inside a xdl pack
                    // warp GEMM
                    WG{}.template operator()<APackIter, ikxdl * NXdlPack + inxdl>(
                        c_warp_tensors(number<m_iter>{})(number<n_iter>{}),
                        bit_cast<typename WG::AWarpTensor>(a_warp_tensor(number<APackIter>{})),
                        bit_cast<typename WG::BWarpTensor>(
                            b_warp_tensor_ping(number<n_iter>{})(number<k_iter>{})),
                        scale_a_tile_tensor_ping(impack)(ikpack).get_thread_buffer()[0],  // scale A
                        scale_b_tile_tensor_ping(inpack)(ikpack).get_thread_buffer()[0]); // scale B
                    // preload next A from lds
                    constexpr auto addr = m_iter % 2 + k_iter * 2 + m_iter / 2 * 4 + m_preload;
                    if constexpr(addr < (KIterPerWarp * MIterPerWarp) &&
                                 (n_iter == NIterPerWarp - 1))
                    {
                        constexpr auto AmIter              = addr % 2 + addr / 4 * 2;
                        constexpr auto AkIter              = addr / 2 % 2;
                        a_warp_tensor(number<APackIter>{}) = load_tile_with_offset(
                            a_warp_window_ping,
                            tuple<number<AmIter * WG::kM>,
                                  number<sizeof(ADataType) * AkIter * WG::kK / APackedSize>>{});
                    }
                });
            LastHotLoopScheduler();
        }
        else
        {
            static_assert(false, "Wrong TailNum");
        }
        return c_warp_tensors;
    }
};

} // namespace ck_tile
